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Oct 11 2024

Harnessing Data: From Resource to Asset to Product

Companies that are data-driven demonstrate improved business performance. McKinsey says that data and analytics can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to MIT, digitally mature firms are 26% more profitable than their peers [2]. Forrester research found that organizations using data are three times more […]

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New Tools, New Tech, Same Roadblocks: Data Governance in the Age of AI

Organizations are racing to adopt AI for its promise of efficiency and insights, yet the path to successful AI integration remains fraught with obstacles. Despite advancements in tools like ChatGPT and Google’s Gemini, fundamental issues with data governance – such as high costs, poor data quality, and security concerns – continue to hinder progress. Stop me […]

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Identity as Infrastructure: Why Digital Identities Are Crucial and How to Secure Their Data

Whether it’s building roads or optimizing power supplies, investing in infrastructure is vital to the safety and efficiency of nations and organizations. And in today’s digital age, this investment must extend to establishing trusted identities for all. Identity is foundational for a robust public infrastructure, initiating substantial economic growth – like using driver’s licenses to […]

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Book of the Month: “AI & The Data Revolution”

Welcome to October 2024’s edition of “Book of the Month.” This month, we’re enjoying some time in the fall sun and the local library diving into Laura Madsen’s “AI & The Data Revolution.” The central theme of this book is the management and impact of artificial intelligence (AI) disruption in the workplace. Madsen shares her […]

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5 Cutting-Edge Innovations to Boost Your Cybersecurity Defenses

With everything on a business leader’s plate, cybersecurity can often feel like an afterthought. Between managing teams, pursuing new opportunities, and dealing with the bottom line, who has time to keep up with the latest hacker threats and security defenses? Especially if you don’t have your IT staff focused solely on locking down the castle […]

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Mind the Gap: Ask for Business Actions, Not Business Value

I’m not sure I know anyone in the data and analytics field whose platform doesn’t face budget scrutiny. Analytics is expensive. And when cost-cutting is the order of the day, analytics is a big target. Up shields. Time for another business value inventory. I’ve spoken with consultants who have completed analytics business value inventories at […]

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Chief Officers: Do You Know What Your Data is Costing You?

CXOs this year have witnessed a rollercoaster economy amid plenty of turbulent events – from ongoing inflation affecting consumer spending to large stock market swings, major overseas conflicts, and the uncertainties of an election year. Not surprisingly, the economic forecast remains murky at best. According to a CNBC CFO survey, CFOs seem to agree that […]

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Strengthening Data Governance Through Data Security Governance

Data security governance is becoming increasingly critical as organizations manage vast amounts of sensitive information across complex, hybrid IT environments. A robust governance framework ensures that data is protected, accessible, and compliant with regulations like GDPR and HIPAA. By centralizing access controls, automating workflows, and applying consistent security measures, organizations can more effectively and efficiently […]

Scalability in Data Engineering: Preparing Your Infrastructure for Digital Transformation

In the present era of data-centricity, institutions are amassing an immense amount of information at an unparalleled pace. This inundation of data holds the solution to unlocking invaluable perceptions, but only with proficient management and analysis. That is precisely where the art of data engineering comes into play. Data engineering services engineer systems that collect, store, and […]

The Book Look: Enterprise Intelligence

Every once in a while, a book comes along that contains such innovative ideas that I find myself whispering “wow” and “interesting” as I read through the pages. “Enterprise Intelligence,” by Eugene Asahara, is one such book. Eugene takes three basic ingredients that are not so new (business intelligence, knowledge graphs, and large language models), […]

Data Leader’s Playbook for Data Mapping

I’ve been thinking a lot about data mapping lately. I know, weird, right? With analytics, AI, cloud, etc., why would someone do that? What’s even stranger is that I’ve been thinking about its impact on data leaders. For clarity’s sake, I’m not talking about geographic maps with data points, I’m referring to the process of […]

Data Crime: Cartoon Signatures

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from the mistakes and prevent future data crimes. The stories can also be helpful if you must explain the importance of  data management to someone.  The Story The state of Rhode […]

Leveraging Citizen Data Scientists to Augment Data Science Teams

According to some estimates, the average salary of a data scientist in the United States is over $150,000 per year. If your business wishes to accommodate a data-first strategy to improve metrics and measurable success and avoid guesswork and strategies that are based on opinion rather than fact, it can either employ a team of expensive […]

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.

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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.

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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.

Unleashing the Power of People and Culture: The Ultimate Drivers of Data Governance Success

In the high-stakes world of data governance, where organizations strive to protect and leverage their most valuable asset, one truth stands out: technology alone won’t get you there. The secret sauce? People and culture. They are the lifeblood of any successful data governance strategy, the pulse that drives data literacy, and the force that propels […]

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Data Visualization in the Era of AI/ML

How will data visualization evolve in the era of AI/ML? While AI is rapidly evolving, it is ironic that business users are still using “dumb” dashboards. The challenge is to move beyond these unintelligent dashboards to a genuinely transformative visual analytics solution that harnesses the power of AI/ML. While some vendors offer a ChatGPT-like querying […]

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How Retail Data Products Are Changing Customer Journey Mapping

The retail world is constantly evolving, and in this fast-paced environment, understanding your customer is more important than ever. It’s no longer just about making a sale; it’s about creating a journey that turns casual shoppers into loyal customers. With the rise of advanced retail data tools, businesses can now dig deep into customer preferences and behaviors […]

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Author: Mridula Dileepraj Kidiyur

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ACTIAN Big Data Business Intelligence Cloud Data Warehouse data architecture Data Migration Data Platform data strategy Data Warehouse databases Enterprise Data Management

Sep 24 2024

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.

The post Data Warehousing Demystified: Your Guide From Basics to Breakthroughs appeared first on Actian.

AI-Powered Data Preparation: The Key to Unlocking Powerful AI Use Cases

Generative AI (GenAI), specifically as it pertains to the public availability of large language models (LLMs), is a relatively new business tool, so it’s understandable that some might be skeptical of a technology that can generate professional documents or organize data instantly across multiple repositories. On the one hand, AI-powered data preparation sounds too good to be […]

The post AI-Powered Data Preparation: The Key to Unlocking Powerful AI Use Cases appeared first on DATAVERSITY.

Key Insights From the ISG Buyers Guide for Data Intelligence 2024

Modern data management requires a variety of technologies and tools to support the people responsible for ensuring that data is trustworthy and secure. Conquering the data challenge has led to a massive number of vendors offering solutions that promise to solve data issues.

With the evolving vendor landscape, it can be difficult to know where to start. It can also be difficult to understand how to determine the best way to evaluate vendors to be sure you’re seeing a true representation of their capabilities—not just sales speak. When it comes to data intelligence, it can be difficult to even define what that means to your business.

With budgets continuously stretched even thinner and new demands placed on data, you need data technologies that meet your needs for performance, reliability, manageability, and validation. Likewise, you want to know that the product has a strong roadmap for your future and a reputation for service you can count on, giving you the confidence to meet current and future needs.

Independent Assessments Are Key to Informing Buying Decisions

Independent analyst reports and buying guides can help you make informed decisions when evaluating and ultimately purchasing software that aligns with your workloads and use cases. The reports offer unbiased, critical insights into the advantages and drawbacks of vendors’ products. The information cuts through marketing jargon to help you understand how technologies truly perform, helping you choose a solution with confidence.

These reports are typically based on thorough research and analysis, considering various factors such as product capabilities, customer satisfaction, and market performance. This objectivity helps you avoid the pitfalls of biased or incomplete information.

For example, the 2024 Buyers Guide for Data Intelligence by ISG Research, which provides authoritative market research and coverage on the business and IT aspects of the software industry, offers insights into several vendors’ products. The guide offers overall scoring of software providers across key categories, such as product experience, capabilities, usability, ROI, and more.

In addition to the overall guide, ISG Research offers multiple buyers guides that focus on specific areas of data intelligence, including data quality and data integration.

ISG Research Market View on Data Intelligence

Data intelligence is a comprehensive approach to managing and leveraging data across your organization. It combines several key components working seamlessly together to provide a holistic view of data assets and facilitate their effective use.

The goal of data intelligence is to empower all users to access and make use of organizational data while ensuring its quality. As ISG Research noted in its Data Quality Buyers Guide, the data quality product category has traditionally been dominated by standalone products focused on assessing quality.

“However, data quality functionality is also an essential component of data intelligence platforms that provide a holistic view of data production and consumption, as well as products that address other aspects of data intelligence, including data governance and master data management,” according to the guide.

Similarly, ISG Research’s Data Integration Buyers Guide notes the importance of bringing together data from all required sources. “Data integration is a fundamental enabler of a data intelligence strategy,” the guide points out.

Companies across all industries are looking for ways to remove barriers to easily access data and enable it to be treated as an important asset that can be consumed across the organization and shared with external partners. To do this effectively and securely, you must consider various capabilities, including data integration, data quality, data catalogs, data lineage, and metadata management solutions.

These capabilities serve as the foundation of data intelligence. They streamline data access and make it easier for teams to consume trusted data for analytics and business intelligence that inform decision making.

ISG Research Criteria for Choosing Data Intelligence Vendors

ISG Research notes that software buying decisions should be based on research. “We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data integration technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential,” according to the company.

In the 2024 Data Intelligence Buyers Guide, ISG Research evaluated software and presented findings in key categories that are important to modern businesses. The evaluation offers a framework that allows you to shorten the cycle time when considering and purchasing software.

For example, ISG Research encourages you to follow a process to ensure the best possible outcomes by:

Defining the business case and goals. Understand what you are trying to accomplish to justify the investment. This should include defining the specific needs of people, processes, and technology. Ventana Research, which is part of ISG Research, predicts that through 2026, three-quarters of enterprises will be engaged in data integrity initiatives to increase trust in their data.
Assessing technologies that align with business needs. Based on your business goals, you should determine the technological capabilities needed for success. This will ensure you maximize your technology investments and avoid paying for tools that you may not require. ISG Research notes that “too many capabilities may be a negative if they introduce unnecessary complexity.”
Including people and defining processes. While choosing the right software will help enforce data quality and facilitate getting data to more people across your organization, it’s important to consider the people who need to be involved in defining and maintaining data quality processes.
Evaluating and selecting technology properly. Determine the business and technology approach that best aligns with your requirements. This allows you to create criteria for meeting your needs, which can be used for evaluating technologies.

As ISG Research points out in its buyers guide, all the products it evaluated are feature-rich. However, not all the capabilities offered by a software provider are equally valuable to all types of users or support all business requirements needed to manage products on a continuous basis. That’s why it’s important to choose software based on your specific and unique needs.

Buy With Confidence

It can be difficult to keep up with the fast-changing landscape of data products. Independent analyst reports help by enabling you to make informed decisions with confidence.

Actian is providing complimentary access to the ISG Research Data Quality Buyers Guide that offers a detailed software provider and product assessment. Get your copy to find out why Actian is ranked in the “Exemplary” category.

If you’re looking for a single, unified data platform that offers data integration, data warehousing, data quality, and more at unmatched price-performance, Actian can help. Let’s talk.

The post Key Insights From the ISG Buyers Guide for Data Intelligence 2024 appeared first on Actian.

Data Lake Strategy: Its Benefits, Challenges, and Implementation

In today’s hyper-competitive business environment, data is one of the most valuable assets an organization can have. However, the sheer volume, variety, and velocity of data can overwhelm traditional data management solutions. Enter the data lake – a centralized repository designed to store all types of data, whether structured, semi-structured, or unstructured. Unlike traditional data warehouses, data […]

The post Data Lake Strategy: Its Benefits, Challenges, and Implementation appeared first on DATAVERSITY.

Putting Threat Modeling into Practice: A Guide for Business Leaders

Recognizing the value of threat modeling – a process that helps identify potential risks and threats to a business’s applications, systems, and other resources – is easy enough. By providing comprehensive insight into how cyberattacks might pan out before they occur, threat modeling helps organizations prepare proactively and reduce the risk of experiencing a successful […]

The post Putting Threat Modeling into Practice: A Guide for Business Leaders appeared first on DATAVERSITY.

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Sep 18 2024

Charting a Course Through the Data Mapping Maze in Three Parts

Companies are dealing with more data sources than ever – sales figures, customer profiles, inventory updates, you name it. Data professionals say, on average, data volumes are growing by 63% per month in their organizations. Data teams are struggling to ensure all that data hangs together across systems and is accurate and consistent. Bad data is bad […]

The post Charting a Course Through the Data Mapping Maze in Three Parts appeared first on DATAVERSITY.

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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

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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