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Edge vs. Cloud: The Data Dilemma of AI-Powered IoT


As artificial intelligence (AI) integrates with the Internet of Things (IoT), a trillion-dollar question emerges: Is it better to process device data at the edge or in the cloud? This decision carries significant implications for privacy, performance, and the future of smart devices. So, let’s explore the growth of autonomous smart devices, examine the key […]

The post Edge vs. Cloud: The Data Dilemma of AI-Powered IoT appeared first on DATAVERSITY.


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Author: Carsten Rhod Gregersen

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.

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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

AI at the Edge: Creating a Successful Strategy


The recent hype surrounding AI makes every organization feel like they must rethink their strategy to ensure they are aligned with the market expectations and not let the competition gain an advantage. AI has been in the news for a while, but when ChatGPT was introduced, people outside of business started to explore the technology […]

The post AI at the Edge: Creating a Successful Strategy appeared first on DATAVERSITY.


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Author: Sathish Kumar Sampath

The Top Benefits of Analyzing Data at the Edge


Demand for real-time data and analytics has never been higher – and for good reason. Businesses want to be able to tap into their data and generate insights that can lead to a competitive edge in their respective industry. To meet those objectives, organizations are increasingly turning to the cloud, on-premise data centers, and the […]

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Author: Rudy De Anda

Why IT Leaders Are Embracing Edge Computing: Security, Serviceability, and Scalability


To counter current economic pressures such as inflation, scarce talent, and supply constraints, 80% of companies are increasing investments in digital technology, according to a report by Gartner. Driving these initiatives are IT leaders, who – depending on the industry they work in – are increasingly doing so in operational environments like factories, production sites, and even […]

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Author: Rudy De Anda

Adopting an Edge-to-Cloud Approach


Back in 2009, there was an enterprise technology with a lot of promise. But adoption, marred by questions about security and reliability, lagged. That technology – cloud computing – is now a $600 billion market. Edge computing technology has followed a similar trajectory to the cloud. It, too, was met with early cynicism and slow […]

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Author: Jason Andersen