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Sep 30 2024

The Essential Guide to Modernizing HCL Informix Applications (Part 1)

Welcome to the first installment of my four-part blog series on HCL Informix® application modernization.

Organizations like yours face increasing pressure to modernize their legacy applications to remain competitive and meet customer needs. HCL Informix, a robust and reliable database platform, has been a cornerstone of many businesses for decades. Now, as technology advances and business needs change, HCL Informix can play a new role—helping you to reevaluate and modernize your applications.

In the HCL Informix Modernization Checklist, I outline four steps to planning your modernization journey:

Start building your business strategy
Evaluate your existing Informix database environment
Kick off your modernization project
Learn, optimize, and innovate

Throughout this modernization series, we will dedicate a blog to each of these steps, delving into the strategic considerations, technical approaches, and best practices so you can get your project started on the right track.

Start building your business strategy

Establish your application modernization objectives

The initial step in any application migration and modernization project is to clearly define the business problems you are trying to solve and optimize your project planning to best serve those needs. For example, you may be facing challenges with:

Security and compliance
Stability and reliability
Performance bottlenecks and scalability
Web and modern APIs
Technological obsolescence
Cost inefficiencies

By defining these parameters, you can set a clear objective for your migration and modernization efforts. This will guide your decision-making process and help in selecting the right strategies and technologies for a successful transformation.

Envision the end result

Understanding the problem you want to address is crucial, but it’s equally important to develop a solution. Start by envisioning an ideal scenario. For instance, consider goals like:

Real-time responses
Scale to meet user demand
Update applications with zero downtime
Zero security incidents
100% connectivity with other applications
Deliver the project on time and on budget
Complete business continuity

Track progress with key performance indicators

Set key performance indicators (KPIs) to track progress toward your goals and objectives. This keeps leadership informed and motivates the team. Some sample KPIs might look like:

kpis for hcl informix

Identify the capabilities you want to incorporate into your applications

With your vision in place, identify capabilities you wish to incorporate into your applications to help you meet your KPIs. Consider incorporating capabilities like:

Cloud computing
Third-party solutions and microservices
Orchestration and automation
DevOps practices
APIs for better integration

Evaluate each capability and sketch an architecture diagram to determine if existing tools meet your needs. If not, identify new services required for your modernization project.

Get Your Modernization Checklist

For more best-practice approaches to modernizing your Informix applications, download the HCL Informix Modernization Checklist and stay tuned for the next blog in the series.

Get the Checklist >

Informix® is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

The post The Essential Guide to Modernizing HCL Informix Applications (Part 1) appeared first on Actian.

Table Cloning: Create Instant Snapshots Without Data Duplication

What is Table Cloning?

Table Cloning is a database operation that makes a copy of an X100 table without the performance penalty of copying the underlying data. If you arrived here looking for the SQL syntax to clone a table in Actian Vector, it works like this:

CREATE TABLE newtable CLONE existingtable
[, newtable2 CLONE existingtable2, ...]
            [ WITH <option, option, ...> ];

The WITH options are briefly listed here. We’ll explain them in more detail later on.

WITH <option>
NODATA	Clone only the table structure, not its contents.
GRANTS	Also copy privileges from existing tables to new tables.
REFERENCES= NONE \| RESTRICTED \| EXTENDED	Disable creation of references between new tables (NONE), create references between new tables to match those between existing tables (RESTRICTED, the default), or additionally enable creation of references from new tables to existing tables not being cloned (EXTENDED).

The new table – the “clone” – has the same contents the existing table did at the point of cloning. The main thing to remember is that the clone you’ve created is just a table. No more, no less. It looks exactly like a copy. The new table may subsequently be inserted into, updated, deleted from, and even dropped, without affecting the original table, and vice versa.

In developing this feature, it became common to field questions like “Can you create a view on a clone?” or “Can you update a clone?” and “Can you grant privileges on a clone?” The answer, in all cases, is yes. It’s a table. If it helps, after you clone a table, you can simply forget that the table was created with the CLONE syntax. That’s what Vector does.

What Isn’t Table Cloning?

It’s just as important to recognize what Table Cloning is not. You can only clone an X100 table, all its contents or none of it, within the same database. You can’t clone only part of a table, or clone a table between two databases.

What’s it For?

With Table Cloning, you can make inexpensive copies of an existing X100 table. This can be useful to create and persist daily snapshots of a table that changes gradually over time, for example. These snapshots can be queried like any other table.

Users can also make experimental copies of sets of tables and try out changes on them, before applying those changes to the original tables. This makes it faster for users to experiment with tables safely.

How Table Cloning Works

In X100’s storage model, when a block of table data is written to storage, that block is never modified, except to be deleted when no longer required. If the table’s contents are modified, a new block is written with the new data, and the table’s list of storage blocks is updated to include the new block and exclude the old one.

X100 catalog and storage for a one-column table MYTABLE, with two storage blocks.

There’s nothing to stop X100 creating a table that references another table’s storage blocks, as long as we know which storage blocks are still referenced by at least one table. So that’s what we do to clone a table. This allows X100 to create what looks like a copy of the table, without having to copy the underlying data.

In the image below, mytableclone references the same storage blocks as mytable does.

X100 catalog and storage after MYTABLECLONE is created as a clone of MYTABLE.

Note that every table column, including the column in the new table, “owns” a storage file, which is the destination file for any new storage blocks for that column. So if new rows are added to mytableclone in the diagram above, the new block will be added to its own storage file:

X100 catalog and storage after another storage block is added to MYTABLECLONE.

X100 tables can also have in-memory updates, which are applied on top of the storage blocks when the table is scanned. These in-memory updates are not cloned, but copied. This means a table which has recently had a large number of updates might not clone instantly.

My First Clone: A Simple Example

Create a table (note that on Actian Ingres, WITH STRUCTURE=X100 is needed to ensure you get an X100 table):

CREATE TABLE mytable (c1 INT, c2 VARCHAR(10)) WITH STRUCTURE=X100;

Insert some rows into it:

INSERT INTO mytable VALUES (1, 'one'), (2, 'two'), (3, 'three'), (4, 'four'), (5, 'five');

Create a clone of this table called myclone:

CREATE TABLE myclone CLONE mytable;

The tables now have the same contents:

SELECT * FROM mytable;

c1	c2
1	one
2	two
3	three
4	four
5	five

SELECT * FROM myclone;

c1	c2
1	one
2	two
3	three
4	four
5	five

Note that there is no further relationship between the table and its clone. The two tables can be modified independently, as if you’d created the new table with CREATE TABLE … AS SELECT …

UPDATE mytable SET c2 = 'trois' WHERE c1 = 3;
INSERT INTO mytable VALUES (6, 'six');
DELETE FROM myclone WHERE c1 = 1;

SELECT * FROM mytable;

c1	c2
1	one
2	two
3	trois
4	four
5	five
6	six

SELECT * FROM myclone;

c1	c2
2	two
3	three
4	four
5	five

You can even drop the original table, and the clone is unaffected:

DROP TABLE mytable;

SELECT * FROM myclone;

c1	c2
2	two
3	three
4	four
5	five

Security and Permissions

You can clone any table you have the privilege to SELECT from, even if you don’t own it.

When you create a table, whether by cloning or otherwise, you own it. That means you have all privileges on it, including the privilege to drop it.

By default, the privileges other people have on your newly-created clone are the same as if you created a table the normal way. If you want all the privileges other users were GRANTed on the existing table to be granted to the clone, use WITH GRANTS.

Metadata-Only Clone

The option WITH NODATA will create an empty copy of the existing table(s), but not the contents. If you do this, you’re not doing anything you couldn’t do with existing SQL, of course, but it may be easier to use the CLONE syntax to make a metadata copy of a group of tables with complicated referential relationships between them.

The WITH NODATA option is also useful on Actian Ingres 12.0. The clone functionality only works with X100 tables, but Actian Ingres 12.0 allows you to create metadata-only clones of non-X100 Ingres tables, such as heap tables.

Cloning Multiple Tables at Once

If you have a set of tables connected by foreign key relationships, you can clone them to create a set of tables connected by the same relationships, as long as you clone them all in the same statement.

For example, suppose we have the SUPPLIER, PART and PART_SUPP, defined like this:

CREATE TABLE supplier (
supplier_id INT PRIMARY KEY,
supplier_name VARCHAR(40),
supplier_address VARCHAR(200)
);

CREATE TABLE part (
part_id INT PRIMARY KEY,
part_name VARCHAR(40)
);

CREATE TABLE part_supp (
supplier_id INT REFERENCES supplier(supplier_id),
part_id INT REFERENCES part(part_id),
cost DECIMAL(6, 2)
);

If we want to clone these three tables at once, we can supply multiple pairs of tables to the clone statement:

CREATE TABLE
supplier_clone CLONE supplier,
part_clone CLONE part,
part_supp_clone CLONE part_supp;

We now have clones of the three tables. PART_SUPP_CLONE references the new tables SUPPLIER_CLONE and PART_CLONE – it does not reference the old tables PART and SUPPLIER.

Without Table Cloning, we’d have to create the new tables ourselves with the same definitions as the existing tables, then copy the data into the new tables, which would be further slowed by the necessary referential integrity checks. With Table Cloning, the database management system doesn’t have to perform an expensive referential integrity check on the new tables because their contents are the same as the existing tables, which have the same constraints.

WITH REFERENCES=NONE

Don’t want your clones to have references to each other? Then use WITH REFERENCES=NONE:

CREATE TABLE
supplier_clone CLONE supplier,
part_clone CLONE part,
part_supp_clone CLONE part_supp
WITH REFERENCES=NONE;

WITH REFERENCES=EXTENDED

Normally, the CLONE statement will only create references between the newly-created clones.

For example, if you only cloned PART and PART_SUPP:

CREATE TABLE
part_clone CLONE part,
part_supp_clone CLONE part_supp;

PART_SUPP_CLONE would have a foreign key reference to PART_CLONE, but not to SUPPLIER.

But what if you want all the clones you create in a statement to retain their foreign keys, even if that means referencing the original tables? You can do that if you want, using WITH REFERENCES=EXTENDED:

CREATE TABLE
part_clone CLONE part,
part_supp_clone CLONE part_supp
WITH REFERENCES=EXTENDED;

After the above SQL, PART_SUPP_CLONE would reference PART_CLONE and SUPPLIER.

Table Cloning Use Case and Real-World Benefits

The ability to clone tables opens up new use cases. For example, a large eCommerce company can use table cloning to replicate its production order database. This allows easier reporting and analytics without impacting the performance of the live system. Benefits include:

Reduced reporting latency. Previously, reports were generated overnight using batch ETL processes. Table cloning can create reports in near real-time, enabling faster decision-making. It can also be used to create a low-cost daily or weekly snapshot of a table which receives gradual changes.
Improved analyst productivity. Analysts no longer have to make a full copy of a table in order to try out modifications. They can clone the table and work on the clone instead, without having to wait for a large table copy or modifying the original.
Cost savings. A clone takes up no additional storage initially, because it only refers to the original table’s storage blocks. New storage blocks are written only as needed when the table is modified. Table cloning would therefore reduce storage costs compared to maintaining a separate data warehouse for reporting.

This hypothetical example illustrates the potential benefits of table cloning in a real-world scenario. By implementing table cloning effectively, you can achieve significant improvements in development speed, performance, cost savings, and operational efficiency.

Create Snapshot Copies of X100 Tables

Table Cloning allows the inexpensive creation of snapshot copies of existing X100 tables. These new tables are tables in their own right, which may be modified independently of the originals.

Actian Vector 7.0, available this fall, will offer Table Cloning. You’ll be able to easily create snapshots of table data at any moment, while having the ability to revert to previous states without duplicating storage. With this Table Cloning capability, you’ll be able to quickly test scenarios, restore data to a prior state, and reduce storage costs. Find out more.

The post Table Cloning: Create Instant Snapshots Without Data Duplication appeared first on Actian.

Build an IoT Smart Farm Using Raspberry Pi and Actian Zen

Technology is changing every industry, and agriculture is no exception. The Internet of Things (IoT) and edge computing provide powerful tools to make traditional farming practices more efficient, sustainable, and data-driven. One affordable and versatile platform that can form the basis for such a smart agriculture system is the Raspberry Pi.

In this blog post, we will build a smart agriculture system using IoT devices to monitor soil moisture, temperature, and humidity levels across a farm. The goal is to optimize irrigation and ensure optimal growing conditions for crops. We’ll use a Raspberry Pi running Raspbian OS, Actian Zen Edge for database management, Zen Enterprise to handle the detected anomalies on the remote server database, and Python with the Zen ODBC interface for data handling. Additionally, we’ll leverage AWS SNS (Simple Notification Service) to send alerts for detected anomalies in real-time for immediate action.

Prerequisites

Before we start, ensure you have the following:

A Raspberry Pi running Raspbian OS.
Python installed on your Raspberry Pi.
Actian Zen Edge database installed.
PyODBC library installed.
AWS SNS set up with an appropriate topic and access credentials.

Step 1: Setting Up the Raspberry Pi

First, update your Raspberry Pi and install the necessary libraries:

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-pip
pip3 install pyodbc boto3

Step 2: Install Actian Zen Edge

Follow the instructions on the Actian Zen Edge download page to download and install Actian Zen Edge on your Raspberry Pi.

Step 3: Create Tables in the Database

We need to create tables to store sensor data and anomalies. Connect to your Actian Zen Edge database and create the following table:

CREATE TABLE sensor_data (
    id identity PRIMARY KEY,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT
);

Install Zen Enterprise, connect to the central database, and create the following table:

 CREATE TABLE anomalies (
    id identity PRIMARY KEY ,
    timestamp DATETIME,
    soil_moisture FLOAT,
    temperature FLOAT,
    humidity FLOAT,
    description longvarchar
);

Step 4: Define the Python Script

Now, let’s write the Python script to handle sensor data insertion, anomaly detection, and alerting via AWS SNS.

Anomaly Detection Logic

Define a function to check for anomalies based on predefined thresholds:

def check_for_anomalies(data):
    threshold = {'soil_moisture': 30.0, 'temperature': 35.0, 'humidity': 70.0}
    anomalies = []
    if data['soil_moisture'] < threshold['soil_moisture']:
        anomalies.append('Low soil moisture detected')
    if data['temperature'] > threshold['temperature']:
        anomalies.append('High temperature detected')
    if data['humidity'] > threshold['humidity']:
        anomalies.append('High humidity detected')
    return anomalies

Insert Sensor Data

Define a function to insert sensor data into the database:

import pyodbc

def insert_sensor_data(data):
    conn = pyodbc.connect('Driver={Pervasive ODBC 
Interface};servername=localhost;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO sensor_data (timestamp, soil_moisture, temperature, humidity) VALUES (?, ?, ?, ?)",
                   (data['timestamp'], data['soil_moisture'], data['temperature'], data['humidity']))
    conn.commit()
    cursor.close()
    conn.close()

Send Anomalies to the Remote Database

Define a function to send detected anomalies to the database:

def send_anomalies_to_server(anomaly_data):
    conn = pyodbc.connect('Driver={Pervasive ODBC Interface};servername=<remote server>;Port=1583;serverdsn=demodata;')
    cursor = conn.cursor()
    cursor.execute("INSERT INTO anomalies (timestamp, soil_moisture, temperature, humidity, description) VALUES (?, ?, ?, ?, ?)",
                   (anomaly_data['timestamp'], anomaly_data['soil_moisture'], anomaly_data['temperature'], anomaly_data['humidity'], anomaly_data['description']))
    conn.commit()
    cursor.close()
    conn.close()

Send Alerts Using AWS SNS

Define a function to send alerts using AWS SNS:

def send_alert(message):
    sns_client = boto3.client('sns', aws_access_key_id='Your key ID',
    aws_secret_access_key ='Your Access key’, region_name='your-region')
    topic_arn = 'arn:aws:sns:your-region:your-account-id:your-topic-name'
    response = sns_client.publish(
        TopicArn=topic_arn,
        Message=message,
        Subject='Anomaly Alert'
    )
    return response

Replace your-region, your-account-id, and your-topic-name with your actual AWS SNS topic details.

Step 5: Generate Sensor Data

Define a function to simulate real-world sensor data:

import random
import datetime

def generate_sensor_data():
    return {
        'timestamp': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
        'soil_moisture': random.uniform(20.0, 40.0),
        'temperature': random.uniform(15.0, 45.0),
        'humidity': random.uniform(30.0, 80.0)
    }

Step 6: Main Function to Simulate Data Collection and Processing

Finally, put everything together in a main function:

def main():
    for _ in range(100):
        sensor_data = generate_sensor_data()
        insert_sensor_data(sensor_data)
        anomalies = check_for_anomalies(sensor_data)
        if anomalies:
            anomaly_data = {
                'timestamp': sensor_data['timestamp'],
                'soil_moisture': sensor_data['soil_moisture'],
                'temperature': sensor_data['temperature'],
                'humidity': sensor_data['humidity'],
                'description': ', '.join(anomalies)
            }
            send_anomalies_to_server(anomaly_data)
            send_alert(anomaly_data['description'])
if __name__ == "__main__":
    main()

Conclusion

And there you have it! By following these steps, you’ve successfully set up a basic smart agriculture system on a Raspberry Pi using Actian Zen Edge and Python. This system, which monitors soil moisture, temperature, and humidity levels, detects anomalies, stores data in databases, and sends notifications via AWS SNS, is a scalable solution for optimizing irrigation and ensuring optimal growing conditions for crops. Now, it’s your turn to apply this knowledge and contribute to the future of smart agriculture.

Remember to replace placeholders with your actual AWS SNS topic details and database connection details. Happy farming!

The post Build an IoT Smart Farm Using Raspberry Pi and Actian Zen appeared first on Actian.

Data Warehousing Demystified: Your Guide From Basics to Breakthroughs

Table of contents

Understanding the Basics

What is a Data Warehouse?

The Business Imperative of Data Warehousing

The Technical Role of Data Warehousing

Understanding the Differences: Databases, Data Warehouses, and Analytics Databases

The Human Side of Data: Key User Personas and Their Pain Points

Data Warehouse Use Cases For Modern Organizations

6 Common Business Use Cases

9 Technical Use Cases

Understanding the Basics

Welcome to data warehousing 101. For those of you who remember when “cloud” only meant rain and “big data” was just a database that ate too much, buckle up—we’ve come a long way. Here’s an overview:

What is a Data Warehouse?

Data warehouses are large storage systems where data from various sources is collected, integrated, and stored for later analysis. Data warehouses are typically used in business intelligence (BI) and reporting scenarios where you need to analyze large amounts of historical and real-time data. They can be deployed on-premises, on a cloud (private or public), or in a hybrid manner.

Think of a data warehouse as the Swiss Army knife of the data world – it’s got everything you need, but unlike that dusty tool in your drawer, you’ll actually use it every day!

Prominent examples include Actian Data Platform, Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure Synapse Analytics, and IBM Db2 Warehouse, among others.

Proper data consolidation, integration, and seamless connectivity with BI tools are crucial for a data strategy and visibility into the business. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.

“Proper data consolidation, integration, and seamless connectivity with BI tools are crucial aspects of a data strategy. A data warehouse without this holistic view provides an incomplete narrative, limiting the potential insights that can be drawn from the data.”

The Business Imperative of Data Warehousing

Data warehouses are instrumental in enabling organizations to make informed decisions quickly and efficiently. The primary value of a data warehouse lies in its ability to facilitate a comprehensive view of an organization’s data landscape, supporting strategic business functions such as real-time decision-making, customer behavior analysis, and long-term planning.

But why is a data warehouse so crucial for modern businesses? Let’s dive in.

A data warehouse is a strategic layer that is essential for any organization looking to maintain competitiveness in a data-driven world. The ability to act quickly on analyzed data translates to improved operational efficiencies, better customer relationships, and enhanced profitability.

The Technical Role of Data Warehousing

The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself. The BI team configures the data warehouse to align with its analytical needs. Essentially, a data warehouse acts as a structured repository, comprising tables of rows and columns of carefully curated and frequently updated data assets. These assets feed BI applications that drive analytics.

“The primary function of a data warehouse is to facilitate analytics, not to perform analytics itself.”

Achieving the business imperatives of data warehousing relies heavily on these four key technical capabilities:

1. Real-Time Data Processing: This is critical for applications that require immediate action, such as fraud detection systems, real-time customer interaction management, and dynamic pricing strategies. Real-time data processing in a data warehouse is like a barista making your coffee to order–it happens right when you need it, tailored to your specific requirements.

2. Scalability and Performance: Modern data warehouses must handle large datasets and support complex queries efficiently. This capability is particularly vital in industries such as retail, finance, and telecommunications, where the ability to scale according to demand is necessary for maintaining operational efficiency and customer satisfaction.

3. Data Quality and Accessibility: The quality of insights directly correlates with the quality of data ingested and stored in the data warehouse. Ensuring data is accurate, clean, and easily accessible is paramount for effective analysis and reporting. Therefore, it’s crucial to consider the entire data chain when crafting a data strategy, rather than viewing the warehouse in isolation.

4. Advanced Capabilities: Modern data warehouses are evolving to meet new challenges and opportunities:

- - Data virtualization: Allowing queries across multiple data sources without physical data movement.
  - Integration with data lakes: Enabling analysis of both structured and unstructured data.
  - In-warehouse machine learning: Supporting the entire ML lifecycle, from model training to deployment, directly within the warehouse environment.

“In the world of data warehousing, scalability isn’t just about handling more data—it’s about adapting to the ever-changing landscape of business needs.”

Understanding the Differences: Databases, Data Warehouses, and Analytics Databases

Databases, data warehouses, and analytics databases serve distinct purposes in the realm of data management, with each optimized for specific use cases and functionalities.

A database is a software system designed to efficiently store, manage, and retrieve structured data. It is optimized for Online Transaction Processing (OLTP), excelling at handling numerous small, discrete transactions that support day-to-day operations. Examples include MySQL, PostgreSQL, and MongoDB. While databases are adept at storing and retrieving data, they are not specifically designed for complex analytical querying and reporting.

Data warehouses, on the other hand, are specialized databases designed to store and manage large volumes of structured, historical data from multiple sources. They are optimized for analytical processing, supporting complex queries, aggregations, and reporting. Data warehouses are designed for Online Analytical Processing (OLAP), using techniques like dimensional modeling and star schemas to facilitate complex queries across large datasets. Data warehouses transform and integrate data from various operational systems into a unified, consistent format for analysis. Examples include Actian Data Platform, Amazon Redshift, Snowflake, and Google BigQuery.

Analytics databases, also known as analytical databases, are a subset of databases optimized specifically for analytical processing. They offer advanced features and capabilities for querying and analyzing large datasets, making them well-suited for business intelligence, data mining, and decision support. Analytics databases bridge the gap between traditional databases and data warehouses, offering features like columnar storage to accelerate analytical queries while maintaining some transactional capabilities. Examples include Actian Vector, Exasol, and Vertica. While analytics databases share similarities with traditional databases, they are specialized for analytical workloads and may incorporate features commonly associated with data warehouses, such as columnar storage and parallel processing.

“In the data management spectrum, databases, data warehouses, and analytics databases each play distinct roles. While all data warehouses are databases, not all databases are data warehouses. Data warehouses are specifically tailored for analytical use cases. Analytics databases bridge the gap, but aren’t necessarily full-fledged data warehouses, which often encompass additional components and functionalities beyond pure analytical processing.”

The Human Side of Data: Key User Personas and Their Pain Points

Welcome to Data Warehouse Personalities 101. No Myers-Briggs here—just SQL, Python, and a dash of data-induced delirium. Let’s see who’s who in this digital zoo.

Note: While these roles are presented distinctly, in practice they often overlap or merge, especially in organizations of varying sizes and across different industries. The following personas are illustrative, designed to highlight the diverse perspectives and challenges related to data warehousing across common roles.

DBAs are responsible for the technical maintenance, security, performance, and reliability of data warehouses. “As a DBA, I need to ensure our data warehouse operates efficiently and securely, with minimal downtime, so that it consistently supports high-volume data transactions and accessibility for authorized users.”
Data analysts specialize in processing and analyzing data to extract insights, supporting decision-making and strategic planning. “As a data analyst, I need robust data extraction and query capabilities from our data warehouse, so I can analyze large datasets accurately and swiftly to provide timely insights to our decision-makers.”
BI analysts focus on creating visualizations, reports, and dashboards from data to directly support business intelligence activities. “As a BI analyst, I need a data warehouse that integrates seamlessly with BI tools to facilitate real-time reporting and actionable business insights.”
Data engineers manage the technical infrastructure and architecture that supports the flow of data into and out of the data warehouse. “As a data engineer, I need to build and maintain a scalable and efficient pipeline that ensures clean, well-structured data is consistently available for analysis and reporting.”
Data scientists use advanced analytics techniques, such as machine learning and predictive modeling, to create algorithms that predict future trends and behaviors. “As a data scientist, I need the data warehouse to handle complex data workloads and provide the computational power necessary to develop, train, and deploy sophisticated models.”
Compliance officers ensure that data management practices comply with regulatory requirements and company policies. “As a compliance officer, I need the data warehouse to enforce data governance practices that secure sensitive information and maintain audit trails for compliance reporting.”
IT managers oversee the IT infrastructure and ensure that technological resources meet the strategic needs of the organization. “As an IT manager, I need a data warehouse that can scale resources efficiently to meet fluctuating demands without overspending on infrastructure.”
Risk managers focus on identifying, managing, and mitigating risks related to data security and operational continuity. “As a risk manager, I need robust disaster recovery capabilities in the data warehouse to protect critical data and ensure it is recoverable in the event of a disaster.”

Data Warehouse Use Cases For Modern Organizations

In this section, we’ll feature common use cases for both the business and IT sides of the organization.

6 Common Business Use Cases

This section highlights how data warehouses directly support critical business objectives and strategies.

1. Supply Chain and Inventory Management: Enhances supply chain visibility and inventory control by analyzing procurement, storage, and distribution data. Think of it as giving your supply chain a pair of X-ray glasses—suddenly, you can see through all the noise and spot exactly where that missing shipment of left-handed widgets went.

Examples:

- - - Retail: Optimizing stock levels and reorder points based on sales forecasts and seasonal trends to minimize stockouts and overstock situations.
    - Manufacturing: Tracking component supplies and production schedules to ensure timely order fulfillment and reduce manufacturing delays.
    - Pharmaceuticals: Ensuring drug safety and availability by monitoring supply chains for potential disruptions and managing inventory efficiently.

2. Customer 360 Analytics: Enables a comprehensive view of customer interactions across multiple touchpoints, providing insights into customer behavior, preferences, and loyalty.

Examples:

- - - Retail: Analyzing purchase history, online and in-store interactions, and customer service records to tailor marketing strategies and enhance customer experience (CX).
    - Banking: Integrating data from branches, online banking, and mobile apps to create personalized banking services and improve customer retention.
    - Telecommunications: Leveraging usage data, service interaction history, and customer feedback to optimize service offerings and improve customer satisfaction.

3. Operational Efficiency: Improves the efficiency of operations by analyzing workflows, resource allocations, and production outputs to identify bottlenecks and optimize processes. It’s the business equivalent of finding the perfect traffic route to work—except instead of avoiding road construction, you’re sidestepping inefficiencies and roadblocks to productivity.

Examples:

- - - Manufacturing: Monitoring production lines and supply chain data to reduce downtime and improve production rates.
    - Healthcare: Streamlining patient flow from registration to discharge to enhance patient care and optimize resource utilization.
    - Logistics: Analyzing route efficiency and warehouse operations to reduce delivery times and lower operational costs.

4. Financial Performance Analysis: Offers insights into financial health through revenue, expense, and profitability analysis, helping companies make informed financial decisions.

Examples:

- - - Finance: Tracking and analyzing investment performance across different portfolios to adjust strategies according to market conditions.
    - Real Estate: Evaluating property investment returns and operating costs to guide future investments and development strategies.
    - Retail: Assessing the profitability of different store locations and product lines to optimize inventory and pricing strategies.

5. Risk Management and Compliance: Helps organizations manage risk and ensure compliance with regulations by analyzing transaction data and audit trails. It’s like having a super-powered compliance officer who can spot a regulatory red flag faster than you can say “GDPR.”

Examples:

- - - Banking: Detecting patterns indicative of fraudulent activity and ensuring compliance with anti-money laundering laws.
    - Healthcare: Monitoring for compliance with healthcare standards and regulations, such as HIPAA, by analyzing patient data handling and privacy measures.
    - Energy: Assessing and managing risks related to energy production and distribution, including compliance with environmental and safety regulations.

6. Market and Sales Analysis: Analyzes market trends and sales data to inform strategic decisions about product development, marketing, and sales strategies.

Examples:

- - - eCommerce: Tracking online customer behavior and sales trends to adjust marketing campaigns and product offerings in real time.
    - Automotive: Analyzing regional sales data and customer preferences to inform marketing efforts and align production with demand.
    - Entertainment: Evaluating the performance of media content across different platforms to guide future production and marketing investments.

These use cases demonstrate how data warehouses have become the backbone of data-driven decision making for organizations. They’ve evolved from mere data repositories into critical business tools.

In an era where data is often called “the new oil,” data warehouses serve as the refineries, turning that raw resource into high-octane business fuel. The real power of data warehouses lies in their ability to transform vast amounts of data into actionable insights, driving strategic decisions across all levels of an organization.

9 Technical Use Cases

Ever wonder how boardroom strategies transform into digital reality? This section pulls back the curtain on the technical wizardry of data warehousing. We’ll explore nine use cases that showcase how data warehouse technologies turn business visions into actionable insights and competitive advantages. From powering machine learning models to ensuring regulatory compliance, let’s dive into the engine room of modern data-driven decision making.

1. Data Science and Machine Learning: Data warehouses can store and process large datasets used for machine learning models and statistical analysis, providing the computational power needed for data scientists to train and deploy models.

Key features:

1. 1. 1. Built-in support for machine learning algorithms and libraries (like TensorFlow).
    2. High-performance data processing capabilities for handling large datasets (like Apache Spark).
    3. Tools for deploying and monitoring machine learning models (like MLflow).

2. Data as a Service (DaaS): Companies can use cloud data warehouses to offer cleaned and curated data to external clients or internal departments, supporting various use cases across industries.

Key features:

1. 1. 1. Robust data integration and transformation capabilities that ensure data accuracy and usability (using tools like Actian DataConnect, Actian Data Platform for data integration, and Talend).
    2. Multi-tenancy and secure data isolation to manage data access (features like those in Amazon Redshift).
    3. APIs for seamless data access and integration with other applications (such as RESTful APIs).
    4. Built-in data sharing tools (features like those in Snowflake).

3. Regulatory Compliance and Reporting: Many organizations use cloud data warehouses to meet compliance requirements by storing and managing access to sensitive data in a secure, auditable manner. It’s like having a digital paper trail that would make even the most meticulous auditor smile. No more drowning in file cabinets!

Key features:

1. 1. 1. Encryption of data at rest and in transit (technologies like AES encryption).
    2. Comprehensive audit trails and role-based access control (features like those available in Oracle Autonomous Data Warehouse).
    3. Adherence to global compliance standards like GDPR and HIPAA (using compliance frameworks such as those provided by Microsoft Azure).

4. Administration and Observability: Facilitates the management of data warehouse platforms and enhances visibility into system operations and performance. Consider it your data warehouse’s health monitor—keeping tabs on its vital signs so you can diagnose issues before they become critical.

Key features:

1. 1. 1. A platform observability dashboard to monitor and manage resources, performance, and costs (as seen in Actian Data Platform, or Google Cloud’s operations suite).
    2. Comprehensive user access controls to ensure data security and appropriate access (features seen in Microsoft SQL Server).
    3. Real-time monitoring dashboards for live tracking of system performance (like Grafana).
    4. Log aggregation and analysis tools to streamline troubleshooting and maintenance (implemented with tools like ELK Stack).

5. Seasonal Demand Scaling: The ability to scale resources up or down based on demand makes cloud data warehouses ideal for industries with seasonal fluctuations, allowing them to handle peak data loads without permanent investments in hardware. It’s like having a magical warehouse that expands during the holiday rush and shrinks during the slow season. No more paying for empty shelf space!

Key features:

1. 1. 1. Semi-automatic or fully automatic resource allocation for handling variable workloads (like Actian Data Platform’s scaling and Schedules feature, or Google BigQuery’s automatic scaling).
    2. Cloud-based scalability options that provide elasticity and cost efficiency (as seen in AWS Redshift).
    3. Distributed architecture that allows horizontal scaling (such as Apache Hadoop).

6. Enhanced Performance and Lower Costs: Modern data warehouses are engineered to provide superior performance in data processing and analytics, while simultaneously reducing the costs associated with data management and operations. Imagine a race car that not only goes faster but also uses less fuel. That’s what we’re talking about here—speed and efficiency in perfect harmony.

Key features:

1. 1. 1. Advanced query optimizers that adjust query execution strategies based on data size and complexity (like Oracle’s Query Optimizer).
    2. In-memory processing to accelerate data access and analysis (such as SAP HANA).
    3. Caching mechanisms to reduce load times for frequently accessed data (implemented in systems like Redis).
    4. Data compression mechanisms to reduce the storage footprint of data, which not only saves on storage costs but also improves query performance by minimizing the amount of data that needs to be read from disk (like the advanced compression techniques in Amazon Redshift).

7. Disaster Recovery: Cloud data warehouses often feature built-in redundancy and backup capabilities, ensuring data is secure and recoverable in the event of a disaster. Think of it as your data’s insurance policy—when disaster strikes, you’re not left empty-handed.

Key features:

1. 1. 1. Redundancy and data replication across geographically dispersed data centers (like those offered by IBM Db2 Warehouse).
    2. Automated backup processes and quick data restoration capabilities (like the features in Snowflake).
    3. High availability configurations to minimize downtime (such as VMware’s HA solutions).

Note: The following use cases are typically driven by separate solutions, but are core to an organization’s warehousing strategy.

8. (Depends on) Data Consolidation and Integration: By consolidating data from diverse sources like CRM and ERP systems into a unified repository, data warehouses facilitate a comprehensive view of business operations, enhancing analysis and strategic planning.

Key features:

1. 1. 1. 1. ETL and ELT capabilities to process and integrate diverse data (using platforms like Actian Data Platform or Informatica).
      2. Support for multiple data formats and sources, enhancing data accessibility (capabilities seen in Actian Data Platform or SAP Data Warehouse Cloud).
      3. Data quality tools that clean and validate data (like tools provided by Dataiku).

9. (Facilitates) Business Intelligence: Data warehouses support complex data queries and are integral in generating insightful reports and dashboards, which are crucial for making informed business decisions. Consider this the grand finale where all your data prep work pays off—transforming raw numbers into visual stories that even the most data-phobic executive can understand.

Key features:

1. 1. 1. 1. Integration with leading BI tools for real-time analytics and reporting (like Tableau).
      2. Data visualization tools and dashboard capabilities to present actionable insights (such as those in Snowflake and Power BI).
      3. Advanced query optimization for fast and efficient data retrieval (using technologies like SQL Server Analysis Services).

The technical capabilities we’ve discussed showcase how modern data warehouses are breaking down silos and bridging gaps across organizations. They’re not just tech tools; they’re catalysts for business transformation. In a world where data is the new currency, a well-implemented data warehouse can be your organization’s most valuable investment.

However, as data warehouses grow in power and complexity, many organizations find themselves grappling with a new challenge: managing an increasingly intricate data ecosystem. Multiple vendors, disparate systems, and complex data pipelines can turn what should be a transformative asset into a resource-draining headache.

“In today’s data-driven world, companies need a unified solution that simplifies their data operations. Actian Data Platform offers an all-in-one approach, combining data integration, data quality, and data warehousing, eliminating the need for multiple vendors and complex data pipelines.”

This is where Actian Data Platform shines, offering an all-in-one solution that combines data integration, data quality, and data warehousing capabilities. By unifying these core data processes into a single, cohesive platform, Actian eliminates the need for multiple vendors and simplifies data operations. Organizations can now focus on what truly matters—leveraging data for strategic insights and decision-making, rather than getting bogged down in managing complex data infrastructure.

As we look to the future, the organizations that will thrive are those that can most effectively turn data into actionable insights. With solutions like Actian Data Platform, businesses can truly capitalize on their data warehouse investment, driving meaningful transformation without the traditional complexities of data management.

Experience the data platform for yourself with a custom demo.

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GenAI at the Edge: The Power of TinyML and Embedded Databases

The convergence of artificial intelligence (AI) and edge computing is ushering in a new era of intelligent applications. At the heart of this transformation lies GenAI (Generative AI), which is rapidly evolving to meet the demands of real-time decision-making and data privacy. TinyML, a subset of machine learning that focuses on running models on microcontrollers, and embedded databases, which store data locally on devices, are key enablers of GenAI at the edge.

This blog delves into the potential of combining TinyML and embedded databases to create intelligent edge applications. We will explore the challenges and opportunities, as well as the potential impact on various industries.

Understanding GenAI, TinyML, and Embedded Databases

GenAI is a branch of AI that involves creating new content, such as text, images, or code. Unlike traditional AI models that analyze data, GenAI models generate new data based on the patterns they have learned.

TinyML is the process of optimizing machine learning models to run on resource-constrained devices like microcontrollers. These models are typically small, efficient, and capable of performing tasks like image classification, speech recognition, and sensor data analysis.

Embedded databases are databases designed to run on resource-constrained devices, such as microcontrollers and embedded systems. They are optimized for low power consumption, fast access times, and small memory footprints.

The Power of GenAI at the Edge

The integration of GenAI with TinyML and embedded databases presents a compelling value proposition:

Real-time processing: By running large language models (LLMs) at the edge, data can be processed locally, reducing latency and enabling real-time decision-making.
Enhanced privacy: Sensitive data can be processed and analyzed on-device, minimizing the risk of data breaches and ensuring compliance with privacy regulations.
Reduced bandwidth consumption: Offloading data processing to the edge can significantly reduce network traffic, leading to cost savings and improved network performance.

Technical Considerations

To successfully implement GenAI at the edge, several technical challenges must be addressed:

Model optimization: LLMs are often computationally intensive and require significant resources. Techniques such as quantization, pruning, and knowledge distillation can be used to optimize models for deployment on resource-constrained devices.
Embedded database selection: The choice of embedded database is crucial for efficient data storage and retrieval. Factors to consider include database footprint, performance, and capabilities such as multi-model support.
Power management: Optimize power consumption to prolong battery life and ensure reliable operation in battery-powered devices.
Security: Implement robust security measures to protect sensitive data and prevent unauthorized access to the machine learning models and embedded database

A Case Study: Edge-Based Predictive Maintenance

Consider a manufacturing facility equipped with sensors that monitor the health of critical equipment. By deploying GenAI models and embedded databases at the edge, the facility can:

Collect sensor data: Sensors continuously monitor equipment parameters such as temperature, vibration, and power consumption.
Process data locally: GenAI models analyze the sensor data in real-time to identify patterns and anomalies that indicate potential equipment failures.
Trigger alerts: When anomalies are detected, the system can trigger alerts to notify maintenance personnel.
Optimize maintenance schedules: By predicting equipment failures, maintenance can be scheduled proactively, reducing downtime and improving overall efficiency.

The Future of GenAI at the Edge

As technology continues to evolve, we can expect to see even more innovative applications of GenAI at the edge. Advances in hardware, software, and algorithms will enable smaller, more powerful devices to run increasingly complex GenAI models. This will unlock new possibilities for edge-based AI, from personalized experiences to autonomous systems.

In conclusion, the integration of GenAI, TinyML, and embedded databases represents a significant step forward in the field of edge computing. By leveraging the power of AI at the edge, we can create intelligent, autonomous, and privacy-preserving applications.

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. Optimized for embedded systems and edge computing, Zen boasts small-footprint with fast read and write access, making it ideal for resource-constrained environments.

Additional Resources:

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A Day in the Life of an Application Owner

The role of an application owner is often misunderstood within businesses. This confusion arises because, depending on the company’s size, an application owner could be the CIO or CTO at a smaller startup, or a product management lead at a larger technology company. Despite the variation in titles, the core responsibilities remain the same: managing an entire application from top to bottom, ensuring it meets the business’s needs (whether it’s an internal or customer-facing application), and doing so cost-effectively.

Being an application owner is a dynamic and multifaceted role that requires a blend of technical expertise, strategic thinking, and excellent communication skills. Here’s a glimpse into a typical day in the life of an application owner.

Morning: Planning and Prioritizing

6:30 AM – 7:30 AM: Start the Day Right

The day begins early with a cup of coffee and a quick review of emails and messages. This is the time to catch up on any overnight developments, urgent issues, or updates from global teams.

7:30 AM – 8:30 AM: Daily Stand-Up Meeting

The first official task is the daily stand-up meeting with the development team. This meeting is crucial for understanding the current status of ongoing projects, identifying any roadblocks, and setting priorities for the day. It’s also an opportunity to align the team’s efforts with the overall business goals and discuss any new application needs.

Mid-Morning: Deep Dive into Projects

8:30 AM – 10:00 AM: Project Reviews and Code Reviews

After the stand-up, it’s time to dive into project reviews. This involves going through the latest code commits, reviewing progress on key features, and ensuring that everything is on track, and if it’s not, create a strategy to address the issues. Code reviews are essential to maintain the quality and integrity of the application.

10:00 AM – 11:00 AM: Stakeholder Meetings

Next up are meetings with stakeholders. These could be product managers, business analysts, or even end-users. The goal is to gather feedback, discuss new requirements, and ensure that the application is meeting the needs of the business.

Late Morning: Problem Solving and Innovation

11:00 AM – 12:00 PM: Troubleshooting and Bug Fixes

No day is complete without some troubleshooting. This hour is dedicated to addressing any critical issues or bugs that have been reported. It’s a time for quick thinking and problem-solving to ensure minimal disruption to users.

12:00 PM – 1:00 PM: Lunch Break and Networking

Lunch is not just a break but also an opportunity to network with colleagues, discuss ideas, and sometimes even brainstorm solutions to ongoing challenges.

Afternoon: Strategic Planning and Development

1:00 PM – 2:30 PM: Strategic Planning

The afternoon kicks off with strategic planning sessions. These involve working on the application’s roadmap, planning future releases, incorporating customer input, and aligning with the company’s long-term vision. It’s a time to think big and set the direction for the future.

2:30 PM – 4:00 PM: Development Time

This is the time to get hands-on with development. Whether it’s coding new features, optimizing existing ones, or experimenting with new technologies, this block is dedicated to building and improving the application.

Late Afternoon: Collaboration and Wrap-Up

4:00 PM – 5:00 PM: Cross-Functional Team Standup

Collaboration is key to the success of any application. This hour is spent working with cross-functional teams such as sales, UX/UI designers, and marketing to analyze and improve the product onboarding experience. The goal is to ensure that everyone is aligned and working toward the same objectives.

5:00 PM – 6:00 PM: End-of-Day Review and Planning for Tomorrow

The day wraps up with a review of what was accomplished and planning for the next day. This involves updating task boards, setting priorities, and making sure that everything is in place for a smooth start the next morning.

Evening: Continuous Learning and Relaxation

6:00 PM Onwards: Continuous Learning and Personal Time

After a productive day, it’s important to unwind and relax. However, the learning never stops. Many application owners spend their evenings reading up on the latest industry trends, taking online courses, or experimenting with new tools and technologies.

Being an application owner is a challenging yet rewarding role. It requires a balance of technical skills, strategic thinking, and effective communication. Every day brings new challenges, opportunities, and rewards, making it an exciting career for those who love to innovate and drive change.

If you need help managing your applications, Actian Application Services can help.

>> Learn More

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The Rise of Embedded Databases in the Age of IoT

The Internet of Things (IoT) is rapidly transforming our world. From smart homes and wearables to industrial automation and connected vehicles, billions of devices are now collecting and generating data. According to a recent analysis, the number of Internet of Things (IoT) devices worldwide is forecasted to almost double from 15.1 billion in 2020 to more than 29 billion IoT devices in 2030. This data deluge presents both challenges and opportunities, and at the heart of it all lies the need for efficient data storage and management – a role increasingly filled by embedded databases.

Traditional Databases vs. Embedded Databases

Traditional databases, designed for large-scale enterprise applications, often struggle in the resource-constrained environment of the IoT. They require significant processing power, memory, and storage, which are luxuries most IoT devices simply don’t have. Additionally, traditional databases are complex to manage and secure, making them unsuitable for the often-unattended nature of IoT deployments.

Embedded databases, on the other hand, are specifically designed for devices with limited resources. They are lightweight, have a small footprint, and require minimal processing power. They are also optimized for real-time data processing, crucial for many IoT applications where decisions need to be made at the edge, without relaying data to a cloud database.

Why Embedded Databases are Perfect for IoT and Edge Computing

Several key factors make embedded databases the ideal choice for IoT and edge computing:

Small Footprint: Embedded databases require minimal storage and memory, making them ideal for devices with limited resources. This allows for smaller form factors and lower costs for IoT devices.
Low Power Consumption: Embedded databases are designed to be energy-efficient, minimizing the power drain on battery-powered devices, a critical concern for many IoT applications.
Fast Performance: Real-time data processing is essential for many IoT applications. Embedded databases are optimized for speed, ensuring timely data storage, retrieval, and analysis at the edge.
Reliability and Durability: IoT devices often operate in harsh environments. Embedded databases are designed to be reliable and durable, ensuring data integrity even in case of power failures or device malfunctions.
Security: Security is paramount in the IoT landscape. Embedded databases incorporate robust security features to protect sensitive data from unauthorized access.
Ease of Use: Unlike traditional databases, embedded databases are designed to be easy to set up and manage. This simplifies development and deployment for resource-constrained IoT projects.

Building complex IoT apps shouldn’t be a headache. Let us show you how our embedded edge database can simplify your next IoT project.

Benefits of Using Embedded Databases in IoT Applications

The advantages of using embedded databases in IoT applications are numerous:

Improved Decision-Making: By storing and analyzing data locally, embedded databases enable real-time decision making at the edge. This reduces reliance on cloud communication and allows for faster, more efficient responses.
Enhanced Functionality: Embedded databases can store device configuration settings, user preferences, and historical data, enabling richer functionality and a more personalized user experience.
Reduced Latency: Processing data locally eliminates the need for constant communication with the cloud, significantly reducing latency and improving responsiveness.
Offline Functionality: Embedded databases allow devices to function even when disconnected from the internet, ensuring uninterrupted operation and data collection.
Cost Savings: By reducing reliance on cloud storage and processing, embedded databases can help lower overall operational costs for IoT deployments.

Use Cases for Embedded Databases in IoT

Embedded databases are finding applications across a wide range of IoT sectors, including:

Smart Homes: Embedded databases can store device settings, energy usage data, and user preferences, enabling intelligent home automation and energy management.
Wearables: Fitness trackers and smartwatches use embedded databases to store health data, activity logs, and user settings.
Industrial Automation: Embedded databases play a crucial role in industrial IoT applications, storing sensor data, equipment settings, and maintenance logs for predictive maintenance and improved operational efficiency.
Connected Vehicles: Embedded databases are essential for connected car applications, storing vehicle diagnostics, driver preferences, and real-time traffic data to enable features like self-driving cars and intelligent navigation systems.
Asset Tracking: Embedded databases can be used to track the location and condition of assets in real-time, optimizing logistics and supply chain management.

The Future of Embedded Databases in the IoT

As the IoT landscape continues to evolve, embedded databases are expected to play an even more critical role. Here are some key trends to watch:

Increased Demand for Scalability: As the number of connected devices explodes, embedded databases will need to be scalable to handle larger data volumes and more complex workloads.
Enhanced Security Features: With growing security concerns in the IoT, embedded databases will need to incorporate even more robust security measures to protect sensitive data.
Cloud Integration: While embedded databases enable edge computing, there will likely be a need for seamless integration with cloud platforms for data analytics, visualization, and long-term storage.

The rise of the IoT has ushered in a new era for embedded databases. Their small footprint, efficiency, and scalability make them the perfect fit for managing data at the edge of the network. As the IoT landscape matures, embedded databases will continue to evolve, offering advanced features, enhanced security, and a seamless integration with cloud platforms.

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. And, with the latest release of Zen 16.0, we are committed to helping businesses simplify edge-to-cloud data management, boost developer productivity and build secure, distributed IoT applications.

Additional Resources:

[Datasheet] Actian Zen – Embedded Database for the Intelligent Edge.
Read the Actian Zen 16.0 Launch Blog for more detailed information about the latest release.

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Actian Ingres 12.0 Enhances Cloud Flexibility, Improves Security, and Offers up to 20% Faster Analytics

Today, we are excited to announce Actian Ingres 12.0*, which is designed to make cloud deployment simpler, enhance security, and deliver up to 20% faster analytics. The first release I worked on was Ingres 6.4/02 back in 1992 and the first bug I fixed was for a major US car manufacturer that used Ingres to drive its production line. It gives me great pride to see that three decades later, Ingres continues to manage some of the world’s most mission-critical data deployments and that there’s so much affection for the Ingres product.

With this release, we’re returning to the much-loved Ingres brand for all platforms. We continue to partner with our customers to understand their evolving business needs, and make sure that we deliver products that enable their modernization journey. With this new release, we focused on the following capabilities:

Backup to cloud and disaster recovery. Ingres 12.0 greatly simplifies these configurations for both on-premises and cloud deployments through the use of Virtual Machines (VMs) or Docker containers in Kubernetes.
Fortified protection automatically enables AES-256 encryption and hardened security to defend against brute force and Denial of Service (DoS) attacks.
Improved performance and workload management with up to 20% faster analytical queries using the X100 engine. Workload Manager 2.0 provides greater flexibility in allocation of resources to meet specific user demand.
Elevated developer experiences in OpenROAD 12. We make it quick and easy to create and transform database-centric applications for web and mobile environments.

These new capabilities, coupled with our previous enhancements to cloud deployment, are designed to help our customers deliver on their modernization goals. They reflect Actian’s vision to develop solutions that our customers can trust, are flexible to meet their specific needs, and are easy-to-use so they can thrive when uncertainty is the only certainty they can plan for.

Customers like Lufthansa Systems rely on Actian Ingres to power their Lido flight and route planning software. “It’s very reassuring to know that our solution, which keeps airplanes and passengers safe, is backed up by a database that has for so many years been playing in the ‘premier league’,” said Rudi Koffer, Senior Database Software Architect at the Lufthansa Systems Airlines Operations Solutions division in Frankfurt Raunheim, Germany.

Experience the new capabilities first-hand. Connect with an Actian representative to get started. Below we dive into what each capability delivers.

A Database Built for Your Modernization Journey

Backup to Cloud and Disaster Recovery

Most businesses today have 24×7 data operations, so a system outage can have serious consequences. With Ingres 12.0 we’ve added new backup functionality to cloud and disaster recovery capabilities to dramatically reduce the risk of application downtime and data loss with a new component called IngresSync. IngresSync makes copies of a database to a target location for offsite storage and quick restoration.

Disaster recovery is now Docker or Kubernetes container-ready for Ingres 12.0 customers, allowing users to set up a read-only standby server in their Kubernetes deployment. Recovery Point Objectives are in the order of minutes and are user configurable.

Backup to cloud and disaster recovery are imperative for situations like:

Natural disasters: When a natural disaster such as a hurricane or earthquake strikes a local datacenter, cloud backups ensure that a copy of the data is readily available, and an environment can be spun up quickly in the cloud of your choosing to resume business operations.
Cyberattacks: In the event of a cyberattack such as ransomware, having cloud backups and a disaster recovery plan are essential to establish a non-compromised version of the database in a protected cloud environment.

Fortified Protection

Actian Ingres 12.0 enables AES-256 bit encryption on data in motion by default. AES-256 bit is considered one of the most secure encryption standards available today and is widely used to protect sensitive data. The 256-bit key size makes it extremely resistant to attacks and is often used by governments and highly regulated industries like banking and healthcare.

In addition, Actian Ingres 12.0 offers user-protected privileges and containerized User Defined Functions (UDFs). These UDFs, which can be authored in SQL, JavaScript, or Python, safeguard against unauthorized activities within the company’s firewall that may target the database directly. Containerization of UDFs further enhances security by isolating user operations from core database management system (DBMS) processes.

Improved Performance and Workload Automation

Actian Ingres 12.0 customers can increase resource efficiency on transactional and analytic workloads in the same database. Workload Manager 2.0 enhances the data management experience with priority-driven queues, enabling the system to allocate resources based on predefined priorities and user roles. Now database administrators can define role-types such as DBAs, application developers, and end users, and assign a priority for each role-type.

The X100 engine, included with Ingres on Linux and Windows, brings efficiency improvements such as table cloning for x100 tables that allow customers to conduct projects or experiments in isolation from core DBMS operations.

Our Performance Engineering Team has determined that for analytics workloads, these enhancements make Actian Ingres 12.0 the fastest Ingres version yet with a 20% improvement over prior versions. Transactional workloads see improved release over release performance.

Elevated Developer Experiences

Actian OpenROAD 12.0, the latest update to the Ingres graphical 4GL, also sees some new enhancements designed to assist customers on their modernization journey. Surprisingly or not, we still have customers with forms-based applications and while many argue that these are the fastest and most reliable apps for data-entry, our customers want to deliver more modern versions of these apps mostly on tablet style devices. To facilitate this modernization and to protect the decades of investments in business logic, we have delivered enhanced versions of abf2or and WebGen in OpenROAD 12.0.

Additionally, OpenROAD users will benefit from the new gRPC-based architecture, which streamlines administration, bolsters concurrency support, and offers a more efficient framework, thanks to HTTP/2 and protocol buffers. The gRPC design is optimized for microservices and can be neatly packaged within a distinct container for deployment. The introduction of a newly distributed Docker file lays the groundwork for cloud deployment, providing production-ready business logic ready for integration with any modern client.

Leading Database Modernization and Innovation

These latest innovations join our recent milestones to solidify Actian’s position as a data and analytics leader. These achievements build on recent recognitions, including:

With this momentum, we are ready to accelerate solutions that our customers can trust, are flexible to their needs, and are easy-to-use.

Get hands-on with the new capabilities today. Connect with an Actian representative to get started.

*Actian Ingres includes the product formerly known as Actian X.

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Types of Databases, Pros & Cons, and Real-World Examples

Databases are the unsung heroes behind nearly every digital interaction, powering applications, enabling insights, and driving business decisions. They provide a structured and efficient way to store vast amounts of data. Unlike traditional file storage systems, databases allow for the organization of data into tables, rows, and columns, making it easy to retrieve and manage information. This structured approach coupled with data governance best practices ensures data integrity, reduces redundancy, and enhances the ability to perform complex queries. Whether it’s handling customer information, financial transactions, inventory levels, or user preferences, databases underpin the functionality and performance of applications across industries.

Types of Information Stored in Databases

Telecommunications: Verizon
Verizon uses databases to manage its vast network infrastructure, monitor service performance, and analyze customer data. This enables the company to optimize network operations, quickly resolve service issues, and offer personalized customer support. By leveraging database technology, Verizon can maintain a high level of service quality and customer satisfaction.

E-commerce: Amazon
Amazon relies heavily on databases to manage its vast inventory, process millions of transactions, and personalize customer experiences. The company’s sophisticated database systems enable it to recommend products, optimize delivery routes, and manage inventory levels in real-time, ensuring a seamless shopping experience for customers.

Finance: JPMorgan Chase
JPMorgan Chase uses databases to analyze financial markets, assess risk, and manage customer accounts. By leveraging advanced database technologies, the bank can perform complex financial analyses, detect fraudulent activities, and ensure regulatory compliance, maintaining its position as a leader in the financial industry.

Healthcare: Mayo Clinic
Mayo Clinic utilizes databases to store and analyze patient records, research data, and treatment outcomes. This data-driven approach allows the clinic to provide personalized care, conduct cutting-edge research, and improve patient outcomes. By integrating data from various sources, Mayo Clinic can deliver high-quality healthcare services and advance medical knowledge.

Types of Databases

The choice between relational and non-relational databases depends on the specific requirements of your application. Relational databases are ideal for scenarios requiring strong data integrity, complex queries, and structured data. In contrast, non-relational databases excel in scalability, flexibility, and handling diverse data types, making them suitable for big data, real-time analytics, and content management applications.

1. Relational Databases

Strengths

Structured Data: Ideal for storing structured data with predefined schemas
ACID Compliance: Ensures transactions are atomic, consistent, isolated, and durable (ACID)
SQL Support: Widely used and supported SQL for querying and managing data

Limitations

Scalability: Can struggle with horizontal scaling
Flexibility: Less suited for unstructured or semi-structured data

Common Use Cases

Transactional Systems: Banking, e-commerce, and order management
Enterprise Applications: Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems

Real-World Examples of Relational Databases

MySQL: Widely used in web applications like WordPress
PostgreSQL: Used by organizations like Instagram for complex queries and data integrity
Oracle Database: Powers large-scale enterprise applications in finance and government sectors
Actian Ingres: Widely used by enterprises and public sector like the Republic of Ireland

2. NoSQL Databases

Strengths

Scalability: Designed for horizontal scaling
Flexibility: Ideal for handling large volumes of unstructured and semi-structured data
Performance: Optimized for high-speed read/write operations

Limitations

Consistency: Some NoSQL databases sacrifice consistency for availability and partition tolerance (CAP theorem)
Complexity: Can require more complex data modeling and application logic
Common Use Cases

Big Data Applications: Real-time analytics, IoT data storage
Content Management: Storing and serving large volumes of user-generated content

Real-World Examples of NoSQL Databases

MongoDB: Used by companies like eBay for its flexibility and scalability
Cassandra: Employed by Netflix for handling massive amounts of streaming data
Redis: Utilized by X (formerly Twitter) for real-time analytics and caching
Actian Zen: Embedded database built for IoT and the intelligent edge. Used by 13,000+ companies
HCL Informix: Small footprint and self-managing. Widely used in financial services, logistics, and retail
Actian NoSQL: Object-oriented database used by the European Space Agency (ESA)

3. In-Memory Databases

Strengths
Speed: Extremely fast read/write operations due to in-memory storage
Low Latency: Ideal for applications requiring rapid data access

Limitations

Cost: High memory costs compared to disk storage
Durability: Data can be lost if not backed up properly

Common Use Cases

Real-Time Analytics: Financial trading platforms, fraud detection systems
Caching: Accelerating web applications by storing frequently accessed data

Real-World Examples of In-Memory Databases

Redis: Used by GitHub to manage session storage and caching
SAP HANA: Powers real-time business applications and analytics
Actian Vector: One of the world’s fastest columnar databases for OLAP workload

Combinations of two or more database models are often developed to address specific use cases or requirements that cannot be fully met by a single type alone. Actian Vector blends OLAP principles, relational database functionality, and in-memory processing, enabling accelerated query performance for real-time analysis of large datasets. The resulting capability showcases the technical versatility of modern database platforms.

4. Graph Databases

Strengths

Relationships: Optimized for storing and querying relationships between entities
Flexibility: Handles complex data structures and connections

Limitations

Complexity: Requires understanding of graph theory and specialized query languages
Scalability: Can be challenging to scale horizontally

Common Use Cases

Social Networks: Managing user connections and interactions
Recommendation Engines: Suggesting products or content based on user behavior

Real-World Examples of Graph Databases

Neo4j: Used by LinkedIn to manage and analyze connections and recommendations
Amazon Neptune: Supports Amazon’s personalized recommendation systems

Factors to Consider in Database Selection

Selecting the right database involves evaluating multiple factors to ensure it meets the specific needs of your applications and organization. As organizations continue to navigate the digital landscape, investing in the right database technology will be crucial for sustaining growth and achieving long-term success. Here are some considerations:

1. Data Structure and Type

Structured vs. Unstructured: Choose relational databases for structured data and NoSQL for unstructured or semi-structured data.
Complex Relationships: Opt for graph databases if your application heavily relies on relationships between data points.

2. Scalability Requirements

Vertical vs. Horizontal Scaling: Consider NoSQL databases for applications needing horizontal scalability.
Future Growth: For growing data needs, cloud-based databases offer scalable solutions.

3. Performance Needs

Latency: In-memory databases are ideal for applications requiring high-speed transactions, real-time data access, and low-latency access.
Throughput: High-throughput applications may benefit from NoSQL databases.

4. Consistency and Transaction Needs

ACID Compliance: If your application requires strict transaction guarantees, a relational database might be the best choice.
Eventual Consistency: NoSQL databases often provide eventual consistency, suitable for applications where immediate consistency is not critical.

5. Cost Considerations
Budget: Factor in both initial setup costs and ongoing licensing, maintenance, and support.
Resource Requirements: Consider the hardware and storage costs associated with different database types.

6. Ecosystem and Support

Community and Vendor Support: Evaluate the availability of support, documentation, and community resources.
Integration: Ensure that the database can integrate seamlessly with your existing systems and applications.

Databases are foundational to modern digital infrastructure. By leveraging the right database for the right use case, organizations can meet their specific needs and leverage data as a strategic asset. In the end, the goal is not just to store data but to harness its full potential to gain a competitive edge.

The post Types of Databases, Pros & Cons, and Real-World Examples appeared first on Actian.

Why Total Cost of Ownership Is a Critical Metric in High-Availability Databases

In the world of data management, the focus often zeroes in on the performance, scalability, and reliability of database systems. Total cost of ownership (TCO) is a crucial aspect that should hold equal – if not more – importance. TCO isn’t just a financial metric; it’s a comprehensive assessment that can significantly impact a business’s […]

The post Why Total Cost of Ownership Is a Critical Metric in High-Availability Databases appeared first on DATAVERSITY.

How to Easily Add Modern User Interfaces to Your Database Applications

Modernizing legacy database applications brings all the advantages of the cloud alongside benefits such as faster development, user experience optimization, staff efficiency, stronger security and compliance, and improved interoperability. In my first blog on legacy application modernization with OpenROAD, a rapid database application development tool, I drilled into the many ways it makes it easier to modernize applications with low risk by retaining your existing business logic. However, there’s still another big part of the legacy modernization journey, the user experience.

Users expect modern, intuitive interfaces with rich features and responsive design. Legacy applications often lack these qualities, which can often require significant redesign and redevelopment during application modernization to meet modern user experience expectations. Not so with OpenROAD! It simplifies the process of creating modern, visually appealing user interfaces by providing developers with a range of tools and features discussed below.

The abf2or Migration Utility

The abf2or migration utility modernizes Application-By-Forms (ABF) applications to OpenROAD frames, including form layout, controls, properties, and event handlers. It migrates business logic implemented in ABF scripts to equivalent logic in OpenROAD. This may involve translating script code and ensuring compatibility with OpenROAD’s scripting language. The utility also handles the migration of data sources to ensure that data connections and queries function properly and can convert report definitions.

WebGen

WebGen is an OpenROAD utility that lets you quickly generate web and mobile applications in HTML5 and JavaScript from OpenROAD frames allowing OpenROAD applications to deployed on-line and on mobile devices.  

OpenROAD and Workbench IDE 

The OpenROAD Workbench Integrated Development Environment (IDE) is a comprehensive toolset for software development, particularly for creating and maintaining applications built using the OpenROAD framework. It provides tools specifically designed to migrate partitioned ABF applications to OpenROAD frames. Developers can then use the IDE’s visual design tools to further refine and customize the programs.

Platform and Device Compatibility

Multiple platform support, including Windows and Linux, lets developers create user interfaces that can run seamlessly across different operating systems without significant modification. Developers can deliver applications to a desktop or place them on a web server for web browser access; OpenROAD installs them automatically if not already installed. The runtime for Windows Mobile enables deploying OpenROAD applications to mobile phones and Pocket PC devices.

Visual Development Environment

OpenROAD provides a visual development environment where developers can design user interface components using drag-and-drop tools, visual editors, and wizards. This makes it easier for developers to create complex user interface layouts without writing extensive code manually. 

Component Library

OpenROAD offers a rich library of pre-built user interface components, such as buttons, menus, dialog boxes, and data grids. Developers can easily customize and integrate these components into applications, saving time and user interface design effort.

Integration with Modern Technologies

Integration with modern technologies and frameworks such as HTML5, CSS3, and JavaScript allows developers to incorporate modern user interface design principles, such as responsive design and animations, into their applications.

Scalability and Performance

OpenROAD delivers scalable and high-performance user interfaces capable of handling large volumes of data and complex interactions. It optimizes resource utilization and minimizes latency, ensuring a smooth and responsive user experience.

Modernize Your OpenROAD applications

Your legacy database applications may be stable, but most will not meet the expectations of users who want modern user interfaces. You don’t have to settle for the status quo. OpenROAD makes it easy to deliver what your users are asking for with migration tools to convert older interfaces, visual design tools, support for web and mobile application development, an extensive library of pre-built user interface components, and much more.

The post How to Easily Add Modern User Interfaces to Your Database Applications appeared first on Actian.

Legacy Transactional Databases: Oh, What a Tangled Web

Database modernization is increasingly needed for digital transformation, but it’s hard work. There are many reasons why; this blog will drill down on one of the main ones: legacy entanglements. Often, organizations have integrated legacy databases with business processes, the applications they run (and their dependencies), and systems such as enterprise resource planning, customer relationship management, supply chain management, human resource management, point-of-sales systems, and e-commerce. Plus, there’s middleware and integration, identify and access management, backup and recovery, replication, and other technology integrations to consider.

Your Five-Step Plan for Untangling Legacy Dependencies

So, how do you safely untangle legacy databases for database modernization in the cloud? Here’s a list of steps that you can take for greater success and a less disruptive transition.

1. Understand and Document Dependencies and Underlying Technologies

There are many activities involved in identifying legacy dependencies. A good start is to review any available database documentation for integrations, including mentions of third-party libraries, frameworks, and services that the database relies on. Code review, with the help of dependency management tools, can identify dependencies within the legacy codebase. Developers, architects, database administrators, and other team members may be able to provide additional insights into legacy dependencies.

2. Prioritize Dependencies

Prioritization is important since you can’t do everything at once. Prioritizing legacy dependencies involves assessing the importance, impact, and risk associated with each dependency in the context of a migration or modernization effort. Higher-priority dependencies should incorporate those that are critical for the database to function and that have the highest business value. When assessing business impact, include how dependencies affect revenue generation and critical business operations.

Also, consider risks, interdependencies, and migration complexity when prioritizing dependencies. For example, outdated technologies can threaten database security and stability. Database dependencies can have significant ripple effects throughout an organization’s systems and processes that require careful consideration. For example, altering a database schema during a migration can lead to application errors, malfunctions, or performance issues. Finally, some dependencies are easier to migrate or replace than others and this might impact its importance or urgency during migration.

3. Take a Phased Approach

A phased migration approach to database modernization that includes preparation, planning, execution, operation, and optimization helps organizations manage complexity, minimize risks, and ensure continuity of operations throughout the migration process. Upfront preparation and planning are necessary to ensure success. It may be beneficial to start small with low-risk or non-critical components to validate procedures and identify issues. The operating phase involves managing workloads, including performance monitoring, resource management, security, and compliance. It’s critical to optimize activities and address concerns in these areas.

4. Reduce Risks

To reduce the risks associated with dependencies, consider approaches that run legacy and modern systems in parallel and use staging environments for testing. Replication offers redundancy that can help ensure business continuity. In case unexpected issues arise, always have a rollback plan to minimize disruption.

5. Breakdown Monolithic Dependencies

Lastly, don’t recreate the same monolithic dependencies found in your legacy database so that you can get the full benefits of digital transformation. A microservices architecture can break down the legacy database into smaller, independent components that can be developed, deployed, and scaled independently. This means that changes to one part of the database don’t affect other parts, reducing the risk of system-wide failures and making the database much easier to maintain and enhance.

How Actian Can Help with Database Modernization

The Ingres NeXt Readiness Assessment offers a pre-defined set of professional services tailored to your requirements. The service is designed to assist you with understanding the requirements to modernize Ingres and Application By Forms (ABF) or OpenROAD applications and to impart recommendations important to your modernization strategy formulation, planning, and implementation.

Based on the knowledge gleaned from the Ingres NeXt Readiness Assessment, Actian can assist you with your pilot and production deployment. Actian can also facilitate a training workshop should you require preliminary training.

For more information, please contact services@actian.com.

The post Legacy Transactional Databases: Oh, What a Tangled Web appeared first on Actian.

The Evolution of AI Graph Databases: Building Strong Relations Between Data (Part One)

We live in an era in which business operations and success are based in large part on how proficiently databases are handled. This is an area in which graph databases have emerged as a transformative force, reshaping our approach to handling and analyzing datasets. Unlike the conventional structure of traditional methods of accessing databases, which […]

The post The Evolution of AI Graph Databases: Building Strong Relations Between Data (Part One) appeared first on DATAVERSITY.

Auditing Database Access and Change

The increasing burden of complying with government and industry regulations imposes significant, time-consuming requirements on IT projects and applications. And nowhere is the pressure to comply with regulations greater than on data stored in corporate databases. Organizations must be hyper-vigilant as they implement controls to protect and monitor their data. One of the more useful […]

🔴 Red Button Data

🔴 Red Button Data

Category Archives: databases

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What is Table Cloning?

What Isn’t Table Cloning?

What’s it For?

How Table Cloning Works

My First Clone: A Simple Example

Security and Permissions

Metadata-Only Clone

Cloning Multiple Tables at Once

WITH REFERENCES=NONE

WITH REFERENCES=EXTENDED

Table Cloning Use Case and Real-World Benefits

Create Snapshot Copies of X100 Tables

Step 1: Setting Up the Raspberry Pi

Step 2: Install Actian Zen Edge

Step 3: Create Tables in the Database

Step 4: Define the Python Script

Step 5: Generate Sensor Data

Step 6: Main Function to Simulate Data Collection and Processing

Understanding the Basics

What is a Data Warehouse?

The Business Imperative of Data Warehousing

The Technical Role of Data Warehousing

Understanding the Differences: Databases, Data Warehouses, and Analytics Databases

The Human Side of Data: Key User Personas and Their Pain Points

Data Warehouse Use Cases For Modern Organizations

6 Common Business Use Cases

9 Technical Use Cases

Understanding GenAI, TinyML, and Embedded Databases

The Power of GenAI at the Edge

Technical Considerations

A Case Study: Edge-Based Predictive Maintenance

The Future of GenAI at the Edge

Morning: Planning and Prioritizing

6:30 AM – 7:30 AM: Start the Day Right

7:30 AM – 8:30 AM: Daily Stand-Up Meeting

Mid-Morning: Deep Dive into Projects

8:30 AM – 10:00 AM: Project Reviews and Code Reviews

10:00 AM – 11:00 AM: Stakeholder Meetings

Late Morning: Problem Solving and Innovation

11:00 AM – 12:00 PM: Troubleshooting and Bug Fixes

12:00 PM – 1:00 PM: Lunch Break and Networking

Afternoon: Strategic Planning and Development

1:00 PM – 2:30 PM: Strategic Planning

2:30 PM – 4:00 PM: Development Time

Late Afternoon: Collaboration and Wrap-Up

4:00 PM – 5:00 PM: Cross-Functional Team Standup

5:00 PM – 6:00 PM: End-of-Day Review and Planning for Tomorrow

Evening: Continuous Learning and Relaxation

6:00 PM Onwards: Continuous Learning and Personal Time

Traditional Databases vs. Embedded Databases

Why Embedded Databases are Perfect for IoT and Edge Computing

Benefits of Using Embedded Databases in IoT Applications

Use Cases for Embedded Databases in IoT

The Future of Embedded Databases in the IoT

A Database Built for Your Modernization Journey

Backup to Cloud and Disaster Recovery

Fortified Protection

Improved Performance and Workload Automation

Elevated Developer Experiences

Leading Database Modernization and Innovation

Types of Information Stored in Databases

1. Relational Databases

2. NoSQL Databases

3. In-Memory Databases

4. Graph Databases

Factors to Consider in Database Selection

The abf2or Migration Utility

WebGen

OpenROAD and Workbench IDE

Platform and Device Compatibility

Visual Development Environment

OpenROAD and Workbench IDE 