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As AI Algorithms Become More Sophisticated in Edge Devices, Persistent Data Requirements Must Advance at the Same Pace

Actian

Companies use distributed AI algorithms to monitor and optimize real-time operations – receiving inputs from embedded sensors, GPS-enabled mobile applications, IoT devices, and video cameras and aggregating this data into a holistic, digital representation of the physical operations. How AI at the edge is being used.

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Data Scalability Raises Considerable Risk Management Concerns

Smart Data Collective

They include the identification of the potential risk, analysis of its potential effects, prioritizing, and developing a plan on how to manage the risk in case it occurs. Aligning these elements of risk management with the handling of big data requires that you establish real-time monitoring controls.

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What is Zero ETL? Components, Benefits How Does it Work

Astera

This process also eradicates the need for intermediate data storage in a staging area. So, let’s dig further and see how zero-ETL works and how i t can b e beneficial in certain data management use cases. Moreover, highly complex data require more development and maintenance resources to maintain zero-ETL solutions.

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Press Release: Astera Launches Code-Free API Lifecycle Management Solution, Astera Data Services at Big Data LDN 2022

Astera

Astera showcased its code-free data management platform and its latest addition, Astera Data Services. Our team interacted with various conference participants and discussed opportunities to accelerate data-driven initiatives. Astera Data Services enables users to easily secure and manage APIs in one place.

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Finance Data Warehouse for Reporting and Analytics

Astera

Efficient Reporting: Standardized data within a data warehouse simplifies the reporting process. This enables analysts to generate consistent reports swiftly, which are essential to evaluate performance, monitor financial health, and make informed strategic decisions. Build Finance Data Warehouse with Astera Contact Us

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What Is Change Data Capture (CDC): Methods, Benefits, and Challenges

Astera

What is Change Data Capture? Change Data Capture (CDC) is a technique used in data management to identify and track changes made to data in a database, and applying those changes to the target system. Delivery : After data storage, there may be a need to deliver this information to downstream systems.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

Data Pine

To work effectively, big data requires a large amount of high-quality information sources. Where is all of that data going to come from? Transparency: With the ability to monitor the movements of goods and delivery operatives in real-time, you can improve internal as well as external efficiency.