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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:

  1. Collect sensor data: Sensors continuously monitor equipment parameters such as temperature, vibration, and power consumption.
  2. Process data locally: GenAI models analyze the sensor data in real-time to identify patterns and anomalies that indicate potential equipment failures.
  3. Trigger alerts: When anomalies are detected, the system can trigger alerts to notify maintenance personnel.
  4. 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:

The post GenAI at the Edge: The Power of TinyML and Embedded Databases appeared first on Actian.


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Author: Kunal Shah

Sync Your Data From Edge-to-Cloud With Actian Zen EasySync

Welcome back to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 3 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen.

Establishing consistency and consolidating data across different devices and servers are essential for most edge-to-cloud solutions. Syncing data is necessary for almost every mobile, edge, or IoT application, and developers are familiar with the basic concepts and challenges. That’s why many experienced developers value efficient solutions. The Actian Zen EasySync tool is a new utility specifically designed for this purpose.

This blog will guide you through the steps for setting up and running EasySync.

What is EasySync?

Zen EasySync is a versatile data synchronization tool that automates the synchronization of newly created or updated records from one Zen database server to another. This tool transfers data across multiple servers, whether you’re working on the edge or within a centralized network. Key features of EasySync include:

  • Flexible Syncing Schedule: Sync data can be scheduled to poll for changes on a defined interval or can be used as a batch transfer tool, depending on your needs.
  • Logging: Monitor general activity, detect errors, and troubleshoot unexpected results with logging capabilities.

Prerequisites

Before using EasySync, ensure the following in your Zen installation:

  • System Data: The files must have system data v2 enabled, with file format version 13 or version 16.
  • ZEN 16.0  installed.
  • Unique Key: Both source and destination files must have a user-defined unique key.

EasySync Usage Scenarios

EasySync supports various data synchronization scenarios, making it a flexible tool for different use cases. Here are some common usage scenarios depicted in the diagram below:

  1. Push to Remote: Synchronize data from a local database to a remote database.
  2. Pull from Remote: Synchronize data from a remote database to a local database.
  3. Pull and Push to Remotes: Synchronize data between multiple remote databases.
  4. Aggregate Data from Edge: Collect data from multiple edge databases and synchronize it to a central database.
  5. Disseminate Data to Edge: Distribute data from a central database to multiple edge databases.

actian edge easysync chart

Getting Started With EasySync

To demonstrate how to use EasySync, we will create a Python application that simulates sensor data and synchronizes it using EasySync. This application will create a sensor table on your edge device and remote server, insert random sensor data, and sync the data with a remote database. The remote database can contain various sets of data from several edge devices.

Step 1: Create the Configuration File

First, we need to create a JSON configuration file (config.json). This file will define the synchronization settings and the files to be synchronized, where files are stored in a source (demodata) and destination (demodata) folders.

Here is an example of what the configuration file might look like:

{
  "version": 1,
  "settings": {
    "polling_interval_sec": 10,
    "log_file": " C:/ProgramData/Actian/Zen/logs/datasync.log",
    "record_err_log": " C:/ProgramData/Actian/Zen/logs/recorderrors.log",
    "resume_on_error": true
  },
  "files": [
    {
      "id": 1,
      "source_file": "btrv://localhost/demodata?dbfile= sensors.mkd",
      "source_username": "",
      "source_password": "",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile= sensors.mkd",
      "destination_username": "",
      "destination_password": "",
      "unique_key": 0
    },
    {
      "id": 2,
      "source_file": "btrv://localhost/demodata?dbfile=bookstore.mkd",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile=bookstore.mkd",
      "create_destination": true,
      "unique_key": 1
    }
  ]
}

Step 2: Write the Python Script

Next, we create a Python script that simulates sensor data, creates the necessary database table, and inserts records into the database. 

Save the following Python code in a file named run_easysync.py. Run the script to create the sensors table on your local edge device and server, and to insert data on your edge device.

import pyodbc
import random
import time
from time import sleep
random.seed()
def CreateSensorTable(server, database):
    try:
db_connection_string = f"Driver={{Pervasive ODBC Interface}};
ServerName={server};
DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
       # cursor.execute("DROP TABLE IF EXISTS sensors;")
        cursor.execute("""
            CREATE TABLE sensors SYSDATA_KEY_2(
                id IDENTITY,
                ts DATETIME NOT NULL,
                temperature INT NOT NULL,
                pressure FLOAT NOT NULL,
                humidity INT NOT NULL
            );
        """)
        print(f"Table 'sensors' created successfully on {server}")
     except pyodbc.DatabaseError as err:
         print(f"Failed to create table on {server} with error: {err}")
def GetTemperature():
     return random.randint(70, 98)
def GetPressure():
     return round(random.uniform(29.80, 30.20), 3)
def GetHumidity():
     return random.randint(40, 55)
def InsertSensorRecord(server, database):
     temp = GetTemperature()
     press = GetPressure()
     hum = GetHumidity()
     try:
      insert = 'INSERT INTO sensors (id, ts, temperature, pressure, humidity) VALUES (0, NOW(), ?, ?, ?)'
        db_connection_string = f"Driver={{Pervasive ODBC Interface}};ServerName={server};DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
        cursor.execute(insert, temp, press, hum)
        print(f"Inserted record [Temperature {temp}, Pressure {press}, Humidity {hum}] on {server}")
    except pyodbc.DatabaseError as err:
        print(f"Failed to insert record on {server} with error: {err}")
# Main
local_server = "localhost"
local_database = "Demodata"
remote_server = "remote-server_name"
remote_database = "demodata"

# Create sensor table on both local and remote servers
CreateSensorTable(local_server, local_database)
CreateSensorTable(remote_server, remote_database)

while True:
    InsertSensorRecord(local_server, local_database)
    sleep(0.5)

Syncing Data from IoT Device to Remote Server

Now, let’s incorporate the data synchronization process using the EasySync tool to ensure the sensor data from the IoT device is replicated to a remote server.

Step 3: Run EasySync

To synchronize the data using EasySync, follow these steps:

  1. Ensure the easysync utility is installed and accessible from your command line.
  2. Run the Python script to start generating and inserting sensor data.
  3. Execute the EasySync command to start the synchronization process.

Open your command line and navigate to the directory containing your configuration file and Python script. Then, run the following command:

easysync -o config.json

This command runs the EasySync utility with the specified configuration file and ensures that the synchronization process begins.

Conclusion

Actian Zen EasySync is a simple but effective tool for automating data synchronization across Zen database servers. By following the steps outlined in this blog, you can easily set up and run EasySync. EasySync provides the flexibility and reliability you need to manage your data on the edge. Remember to ensure your files are in the correct format, have system data v2 enabled, and possess a user-defined unique key for seamless synchronization. With EasySync, you can confidently manage data from IoT devices and synchronize it to remote servers efficiently.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

The post Sync Your Data From Edge-to-Cloud With Actian Zen EasySync appeared first on Actian.


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Author: Johnson Varughese

Getting Started With Actian Zen and BtrievePython

Welcome to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 1 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen. In this blog, we’ll explore how to leverage BtrievePython to run Btrieve2 Python applications, using the Zen 16.0 Enterprise/Server Database Engine.

But before we dive in, let’s do a quick introduction.

What is Btrieve?

Actian Zen Btrieve interface is a high-performance, low-level, record-oriented database management system (DBMS) developed by Pervasive Software, now part of Actian Corporation. It provides efficient and reliable data storage and retrieval by focusing on record-level operations rather than complex queries. Btrieve is known for its speed, flexibility, and robustness, making it a popular choice for applications that require high-speed data access and transaction processing.

What is BtrievePython?

BtrievePython is a modern Python interface for interacting with Actian Zen databases. It allows developers to leverage the powerful features of Btrieve within Python applications, providing an easy-to-use and efficient way to manage Btrieve records. By integrating Btrieve with Python, BtrievePython enables developers to build high-performance, data-driven applications using Python’s extensive ecosystem and Btrieve’s reliable data-handling capabilities.

This comprehensive guide will walk you through the setup on both Microsoft Server 2019 and Ubuntu V20, ensuring you have all the tools you need for success.

Getting Started With Actian Zen

Actian Zen offers a range of data access solutions compatible with various operating systems, including Android, iOS, Linux, Raspbian, and Windows (including IoT and Nano Server). For this demonstration, we’ll focus on Microsoft Server 2019, though the process is similar across different platforms.

Before we dive into the setup, ensure you’ve downloaded and installed the Zen 16.0 Enterprise/Server Database Engine for Windows or Linux on Ubuntu. Detailed installation instructions can be found on Actian’s Academy channel.

Setting Up Your Environment

Installing Python and BtrievePython on Windows:

      • Download and Install Python: Visit Python’s official website and download the latest version (we’re using Python v3.12).
      • Open Command Prompt as Administrator: Ensure you have admin rights to proceed with the installation.
      • Install BtrievePython: Execute pip install btrievePython. Note that this step requires an installed ZEN 16.0 client or Engine. If the BtrievePython installation fails, ensure you have Microsoft Visual C++ 14.0 or greater by downloading the Visual C++ Build Tools.
      • Verify Installation: Run pip list to check if BtrievePython is listed.
      • Run a Btrieve2 Python Sample: Download the sample program from the Actian documentation and run it using python btr2sample.py 9 from an admin command prompt.

Installing Python and BtrievePython on Linux (Ubuntu):

      • Install PIP: Use sudo apt install python3-pip to get PIP, the Python package installer.
      • Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin
      • Install BtrievePython: Execute sudo pip install btrievePython, ensuring a ZEN 16.0 client or Engine is present.
      • Verify Installation: Run pip show btrievePython to confirm the installation.
      • Run a Btrieve2 Python Sample: After downloading the sample from the Actian documentation, run the sample with python3 btr2sample.py 9

Visual Guide

The setup process includes several steps that are best followed with visual aids. Here are some key screenshots to help guide you through the setup:

For the Windows setup:

Downloading and setting up Python.

Python Download Site

python download site

Command Prompt Operations: Steps to install BtrievePython.

command prompt operations for btrieve

Code snippet:

code snippet btrieve

Verification and Execution: verifying the installation and running the Btrieve2 sample application.

verification and execution btrieve

For the Linux Setup:

Installation Commands

Install Python3-pip

install python3 linux btrieve

BtrievePython Setup: BtrievePython installation.

btrieve python setup

Open a terminal window as a non-“root” user and export PATH=$PATH:/usr/local/actianzen/bin

BtrievePython Installed

btrieve python installed

Sample Execution: running the Btrieve2 sample app.

sample execution btrieve

Conclusion

This guide has provided a thorough walkthrough on using BtrievePython with Actian Zen to run Btrieve2 Python applications. Whether you’re working on Windows or Linux, these steps will help you set up your environment efficiently and get your applications running smoothly. Actian Zen’s compatibility with multiple platforms ensures that you can manage your data seamlessly, regardless of your operating system.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

The post Getting Started With Actian Zen and BtrievePython appeared first on Actian.


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Author: Johnson Varughese

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:

The post The Rise of Embedded Databases in the Age of IoT appeared first on Actian.


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Author: Kunal Shah

Actian Zen 16.0 Delivers Performance Enhancements, Offers Flexible Deployment, Improves Developer Experiences, and Introduces New Data Sync Utility

We are thrilled to announce the general availability of Actian Zen 16.0, delivering up to 50% faster query processing, flexible cloud deployment options, improved developer productivity, and a new data synchronization utility called EasySync.

More than 13,000 organizations across the globe trust Actian Zen as their embedded edge database for making fast, confident decisions. With this release, Actian is committed to helping businesses simplify edge-to-cloud data management, boost developer productivity, and build secure, distributed IoT apps.

Actian Zen’s latest release solidifies its position as the go-to database for building low-latency embedded applications. These applications enable real-time data access, optimize operations, and deliver valuable insights faster than ever before. The Zen 16.0 release helps embedded edge developers bring more efficiency at the edge with the following capabilities:

Curious how the new capabilities can help? Let an Actian representative show you!

Let’s dive into the ways Zen 16.0 empowers users with the new capabilities.

Execute Queries Up to 50% Faster

You can run faster, smarter applications on edge devices with Zen 16.0. Zen accesses frequently used data that’s stored in the L2 cache, speeding up results for queries using this data. Common queries, such as those for frequently used reports or analysis, will experience significantly faster results.
Another technique boosting query performance is page read-ahead, which makes it much faster to scan large data files. When a query is executed, the Zen MicroKernel engine anticipates the data and preloads pages from the data file into memory. This optimization mechanism allows the database engine to not read from the disk as often, enabling faster results.
Having ultra-fast data retrieval is perfect for applications requiring immediate insights from edge devices. This capability ensures real-time analytics and decision-making, enhancing the overall efficiency and responsiveness of your operations. For example, Tsubakimoto Chain Company, a global machinery manufacturer, relies on Actian Zen as the embedded database, sorting up to 10,000 items per hour on their high-speed material handling systems.

Deploy Your Way With Zen Container SDK

With containerization, developers can quickly set up and use Actian Zen, running in Docker containers, with Kubernetes orchestration and Helm Chart configuration. This makes deployment and management across various environments, including on-premises, cloud, and hybrid, much easier.
The containerization of Zen supports ARM 32 and ARM 64 processors for wider deployment options. The ARM architecture is increasingly prevalent in various devices, from smartphones to Internet of Things (IoT) gadgets. Container support for ARM allows developers to target a broader range of platforms with their applications.

Elevate Developer Experiences Leveraging a Btrieve 2 Python Package

The Btrieve2 Python SDK has gained popularity within the Python community. With this release, developers can now leverage the performance and flexibility of Btrieve databases from Python using the Btrieve2 Python package:

  • Simplified Btrieve integration. The Btrieve2 Python package streamlines the process of working with Btrieve databases from Python applications. Developers can leverage familiar Python syntax for database operations, reducing the learning curve and development time.
  • Broader developer reach. Availability on PyPI makes the Btrieve2 package easily discoverable and installable using the familiar pip command. This expands the potential user base for Btrieve-compatible applications.
  • Simplified distribution and management. PyPI provides a centralized repository for package distribution and version management. You can easily share and update your Btrieve2 package, ensuring users have access to the latest version.

Zen 16.0 also boosts developer productivity with features such as LIKE with ESCAPE syntax and literal matching for concise, readable queries. Additionally, Zen now supports JSON nested-object queries to simplify data retrieval from JSON formats, allowing developers to focus on core logic and accelerate development cycles. Lastly, SQL query logging improves performance debugging effectiveness by revealing database interactions, aiding in identifying bottlenecks and optimizing query performance.

Enable Real-Time Data Streaming with Zen and Apache Kafka

Real-time data streaming – particularly in Kafka – is a popular method for moving data from the edge to the cloud, and vice versa. Zen support for Kafka allows you to benefit from streaming-based edge applications.

Combining Zen replication features with Apache Kafka can create a real-time data pipeline. Zen acts as the source database, replicating changes to a secondary database for analytical workloads. Kafka serves as a high-throughput messaging system, efficiently streaming data updates to analytics engines for immediate processing and insights.

Zen’s support for Kafka also allows you to build apps for real-time data processing. This is crucial for scenarios requiring immediate responses to data updates such as fraud detection or sensor data analysis.

Move and Sync Data Easier Using Zen EasySync

A pre-built data synchronization utility called EasySync saves time and effort compared to custom replication logic, allowing you to focus on core application functionality. EasySync lets you move and copy data easier than ever:

  • Data consistency and availability. Zen offers a robust replication mechanism for ensuring data is kept synchronized across multiple servers or geographically dispersed locations. This minimizes downtime and data loss risk in cases of hardware failures, network outages, or planned maintenance.
  • Reduced development complexity. By providing a pre-built data synchronization solution, Actian Zen saves you time and effort compared to implementing custom replication logic from scratch or paying for a separate data sync solution. This allows you to focus on core application functionality.

In Industrial IoT (IIOT) environments, the ability to replicate data to the database from handheld devices without requiring a gateway and without creating new code opens new use cases and opportunities. This enables real-time data collection and faster decision making for process control, remote monitoring, and field service.

Drive Better Outcomes at the Edge

Zen simplifies edge-to-cloud data management with secure, scalable storage and seamless cloud synchronization. We listened to customer feedback and looked at market trends to ensure Zen continues to deliver new and sustainable value for your IoT and edge devices.
For example, you asked us to create longer index keys with more descriptive names. We delivered with index keys longer than 255 characters, enabling you to create more granular indexes that target the data needed for specific queries. You benefit from improved query speed, especially for complex searches or filtering, while being able to create data models with more expressive and descriptive field names to improve code readability and maintainability.

You can use Zen Mobile, Zen Edge, and Zen Enterprise to support modernization efforts, optimize embedded apps, and simplify edge-to-cloud data management. The surge in data from IoT and edge devices, alongside rapidly growing data volumes, makes extracting actionable insights a key differentiator.

Empower your team to achieve embedded edge intelligence with Zen 16.0. Packed with productivity-boosting features and flexible deployment options, Zen 16.0 helps you build the future of IoT.

Get started today!

The post Actian Zen 16.0 Delivers Performance Enhancements, Offers Flexible Deployment, Improves Developer Experiences, and Introduces New Data Sync Utility appeared first on Actian.


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Author: Emma McGrattan

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