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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

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

Edge Computing With Actian Zen: Paving the Way for a Sustainable Future

Consider your morning commute–taking your kids to school, your morning coffee run, hurrying to the office–how much time do you spend in the car? And how much money are you spending filling up your tank? Or maybe you’re like me and desperately trying not to think about your carbon footprint every time you drive 20 minutes (each way!) to the grocery store.

Now imagine how it would be if your office was just a block or two down the street, daycare right next door, and your grocery store in-between. Imagine the time savings, cost savings, and reduction in your personal carbon emissions if you could do everything you need, but without having to travel as far. If you could snap your fingers to make it happen, would you?

That’s the question being asked across the world of data processing. Businesses are increasingly seeking efficient and sustainable ways to manage and process their data across the world. End-users are less patient and the sheer volume of data being transferred from one endpoint to the next has massive implications for energy consumption and overall latency.

One solution to this is edge computing, which is the data processing equivalent of reducing your commute from an hour to two minutes. Not only does edge computing use fewer resources and energy, but it’s faster and more efficient, making it a greener choice for managing data.

Understanding Edge Computing

Before delving into the sustainability benefits, it’s crucial to understand what edge computing is. Edge computing is a distributed computing framework where data is processed closer to where it is generated, rather than relying on a centralized data center or the cloud. If you’ve ever used the Target App for shopping, you may notice it’ll give a little warning for items that are low in stock. “Only 2 left at your store!” Retailers like Target use edge enabled sensors to track products on shelves in real-time, automating inventory management for a more reliable picture of what’s available locally.

If not for IoT sensors and edge computing, you likely wouldn’t get a real-time view of inventory– data would be collected via barcode scans, transferred to a centralized data center that could be states away, batch processed, and then synchronized with inventory systems. This could take minutes, hours, or even days depending on the company, and this process is rife with problems like latency, network reliability, bandwidth constraints, and high infrastructure costs. Not to mention that a central server represents a single point of failure–meaning if one store is down, they all are. Not a great experience for shoppers, and a great case for moving to the edge.

The Sustainability Edge

Because it’s 2024, sustainability and environmental, social, and governance (ESG) initiatives are paramount. For example, 90% of S&P 500 companies release ESG reports, and ESG initiatives are considered by 89% of investors when making investment decisions. Sticking with those high numbers, 89% of executives plan to increase their overall technology budget, and 28% say that at least one-fifth of their workforce is involved in emerging tech as part of their primary job function. That’s a huge amount of people who are actively considering both sustainability and emerging technologies in their day-to-day work, in their projections, and in their strategic initiatives.

Edge computing marries these two initiatives beautifully. For instance, 60% of companies are using edge to some degree today, and half of those have deeply integrated edge into their digital core. In fact, Forbes predicts a mass migration from the cloud to the edge in 2024. The sustainability advantages perfectly complement the cost savings and consumer benefits of edge computing as opposed to the traditional cloud.

Here are three primary ways edge computing supports ESG:

  1. Reduced Energy Consumption: Traditional data centers and cloud computing require substantial energy to power and cool the vast arrays of servers. This energy consumption not only translates into high operational costs but also contributes significantly to carbon emissions. Edge computing, on the other hand, decentralizes data processing, distributing it across multiple edge devices that are often located closer to the data source. This decentralization reduces the load on central data centers, leading to lower overall energy consumption.
  2. Optimized Bandwidth Usage: Transmitting large volumes of data to and from centralized data centers or the cloud can be bandwidth-intensive. This not only increases operational costs but also places a strain on network infrastructure. By processing data at the edge, organizations can significantly reduce the amount of data that needs to be transmitted over the network. This not only optimizes bandwidth usage but also reduces the associated energy consumption and emissions.
  3. Decreased Latency and Improved Efficiency: One of the inherent advantages of edge computing is the reduction in latency. By processing data closer to the source, edge computing eliminates the delays associated with transmitting data to distant data centers. This not only enhances the speed and responsiveness of applications but also improves overall system efficiency.

Actian Zen: A Sustainable Edge Solution

Edge computing doesn’t exist in a vacuum, and it takes the right toolkit to take advantage of all the benefits. You need to be sure you have the right database and a database management system (DBMS) that’s edge compatible.

Enter Actian Zen, a high-performance, embedded, and zero-administration DBMS designed for edge computing, IoT applications, and mobile environments. Known for its small footprint, low resource consumption, and ability to operate efficiently on a wide range of devices, Actian Zen provides a versatile and powerful DBMS that meets the needs of modern business across various industries.

Three main benefits Zen delivers include:

  1. Optimizing IT and Cloud Expenditures: Actian Zen is designed to operate efficiently on a wide range of devices, from IoT sensors to industrial gateways. Its compact size means it can be deployed on low-power devices, reducing the need for energy-intensive hardware. Additionally, by processing data locally at the edge, Actian Zen significantly reduces the need for extensive data transmission to central servers or cloud environments. This local processing minimizes bandwidth usage and decreases the load on centralized data centers, leading to lower operational costs associated with data storage and cloud services. Furthermore, the reduced reliance on large, energy-intensive data centers aligns with sustainability goals by lowering overall energy consumption and carbon emissions.
  2. Ensuring Compliance with Internal Policies and External Regulations: By enabling data processing at the edge, Actian Zen reduces the need for data transmission to centralized servers, thus saving bandwidth and energy. This local processing aligns with sustainability initiatives aimed at reducing energy consumption and emissions. Actian Zen also features role-based access, which allows for granular control over who can access and manipulate data, aligning with internal security policies and regulatory standards.
  3. Enabling Scalability and Flexibility to Accommodate Future Growth: With Actian Zen, developers can scale from a core set of libraries capable of single-user client data management to a full-fledged, enterprise-grade server. It’s capable of supporting thousands of users on multicore, VM cloud environments, or in Docker containers with Kubernetes orchestration and Helm chart deployment configuration.

Zen: The Sustainable Database Solution

As the demand for sustainable computing solutions grows, edge computing with Actian Zen emerges as a game-changer. By reducing energy consumption, optimizing bandwidth usage, and decreasing latency, Actian Zen not only enhances operational efficiency but also contributes to a greener future. If you’re looking to balance performance with sustainability, you’ll find Actian Zen’s edge computing capabilities to be a compelling choice. Embrace the power of edge computing with Actian Zen and take a step toward a more sustainable, efficient, and environmentally friendly future.

The post Edge Computing With Actian Zen: Paving the Way for a Sustainable Future appeared first on Actian.


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Author: Kasey Nolan

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