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Embedded Databases Everywhere: Top 3 IoT Use Cases

The rise of edge computing is fueling demand for embedded devices for Internet of Things (IoT). IoT describes physical objects with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. Diverse technologies such as real-time data analytics, machine learning, and automation tie in with IoT to provide insights across various edge to cloud use cases. 

It is not surprising that embedded databases are widely used for IoT given its explosive growth. International Data Corporation (IDC) estimates there will be 55.7 billion connected IoT devices (or “things”) by 2025, generating almost 80B zettabytes (ZB) of data. 

Our research reveals the top six use cases for embedded databases for IoT. Here, we will discuss the first 3: manufacturing, mobile and isolated environments, and medical devices. You can read our Embedded Databases Use Cases Solution Brief if you would like to learn more about the other three use cases.  

Manufacturing  

In fiercely competitive global markets, IoT-enabled manufacturers can get better visibility into their assets, processes, resources, and products. For example, connected machines used in smart manufacturing at factories help streamline operations, optimize productivity, and improve return on investment. Warehouse and inventory management can leverage real-time data analytics to source missing production inputs from an alternative supplier or to resolve a transportation bottleneck by using another shipper. Predictive maintenance using IoT can help identify and resolve potential problems with production-line equipment before they happen and spot bottlenecks and quality assurance issues faster.  

Mobile/Isolated Environments 

IoT is driving the shift towards connected logistics, infrastructure, transportation, and other mobile/isolated use cases. In logistics, businesses use edge computing for route optimization and tracking vehicles and shipping containers. Gas and oil companies take advantage of IoT to monitor remote infrastructure such as pipelines and offshore rigs. In the transportation industry, aviation and automotive companies use IoT to improve the passenger experience and to improve safety and maintenance.  

Medical Devices 

Healthcare is one of the industries that will benefit the most from IoT, given its direct connection with improving lives. IoT is recognized as one of the most promising technological advancements in healthcare analytics. Medical IoT devices are simultaneously improving patient outcomes and providers’ return on investment. The processing of medical images and laboratory equipment maintenance are particularly important use cases. Data from MRIs, CTs, ultrasounds, X-Rays, and other imaging machines help medical experts diagnose diseases at earlier stages and provide faster and more accurate results. Edge analytics enables predictive maintenance of laboratory equipment to reduce maintenance costs, but more importantly, to help prevent the failure of critical equipment that is often in short supply.  

What is possible today with IoT in healthcare was inconceivable a decade ago: tracking medications, their temperature, and safe transportation at any point in time. 

Learn More 

Read our solution brief for more information on additional embedded database for IoT use cases as well as Actian’s Edge to Cloud capabilities for these. 

The post Embedded Databases Everywhere: Top 3 IoT Use Cases appeared first on Actian.


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Author: Teresa Wingfield

Top Technical Requirements for Embedded Analytics

What is Embedded Analytics?

More employees making decisions based on data insights leads to better business outcomes.  Increasingly, data analytics needs to be surfaced to users via the right medium to inform better and faster decisions. This is why embedded analytics has emerged as an important way to help organizations unlock the potential of their data.  Gartner defines embedded analytics as a digital workplace capability where data analytics occurs within a user’s natural workflow, without the need to toggle to another application.

How do you embed data analytics so that users can better understand and use data? It all starts with building the right data foundation with a modern cloud data platform. While technical requirements to support embedded analytics depend on specific use case and user needs, there are general requirements that a cloud data platform should always meet. Below is a summary of each one.

Technical Requirements

API Integration: The cloud data platform must provide flexible API choices to allow effortless application access to data.

Extract, Transform and Load (ETL) integration: The solution should also include ETL capabilities to integrate data from diverse sources, including databases, internal and third-party applications, and cloud storage.

Data variety: Support for different data types, including structured, semi-structured, and unstructured data, is essential as data comes in many forms, including text, video, audio, and many others.

Data modeling: The solution should be able to model the data in a way that supports analytics use cases, such as aggregating, filtering, and visualizing data.

Data quality: Data profiling and data quality should be built into the platform so that users have data they can trust.

Performance: REAL real-time performance is a critical need to ensure that users can access and analyze data in the moment.

Scalability: The solution should be able to handle large volumes of data, support a growing number of users and use cases, and reuse data pipelines.

Security: The solution should provide robust security measures to protect data from unauthorized access, including role-based access control, encryption, and secure connections.

Governance: Embedded analytics demands new approaches to data privacy. The cloud data platform should help organizations comply with relevant data and privacy regulations in their geography and industry while also making sure that data is useful to analysts and decision-makers.

Support for embedded analytics vendors: In addition to sending data directly to applications, the cloud data platform should allow developers to leverage their embedded application of choice.

How the Avalanche Cloud Data Platform Helps

The Avalanche Cloud Data Platform, with built-in integration, including APIs, and data quality, is an ideal foundation for embedded analytics. These features combined with dynamic scaling, patented REAL real-time performance, compliance and data masking help meet the needs of even the most challenging embedded analytics use cases. In addition, you can fuel your applications with data directly from the Avalanche platform or use your preferred application for embedded analytics.

Don’t take our word for it, start your free trial today and see why the Avalanche platform is a great fit for your embedded analytics needs!

The post Top Technical Requirements for Embedded Analytics appeared first on Actian.


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Author: Teresa Wingfield

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