Smart Data Collective
MAY 20, 2019
We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason. It won’t be the last either, not by a longshot.
Astera
APRIL 22, 2024
For instance, integrating real-time data from wearable devices with EHRs enables healthcare professionals to make timely interventions and tailor care plans according to individual needs. Once the data is integrated, governance can further facilitate healthcare providers.
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Astera
OCTOBER 19, 2023
Built-in connectivity for these sources allows for easier data extraction and integration, as users will be able to retrieve complex data with only a few clicks. Data Security Data security and privacy checks protect sensitive data from unauthorized access, theft, or manipulation. This was up 2.6%
Astera
NOVEMBER 30, 2023
It’s designed to efficiently handle and process vast volumes of diverse data, providing a unified and organized view of information. With its ability to adapt to changing data types and offer real-time data processing capabilities, it empowers businesses to make timely, data-driven decisions.
Astera
JUNE 6, 2023
ETL architectures have become a crucial solution for managing and processing large volumes of data efficiently, addressing the challenges faced by organizations in the era of big data. Performance Optimization: Optimizing the ETL process to achieve high performance and reduced processing time.
Data Pine
OCTOBER 21, 2020
Big data has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analytics is making big changes is healthcare. In this article, we’re going to address the need for big data in healthcare and hospital big data: why and how can it help?
Astera
JANUARY 17, 2024
Common methods include Extract, Transform, and Load (ETL), Extract, Load, and Transform (ELT), data replication, and Change Data Capture (CDC). Each of these methods serves a unique purpose and is chosen based on factors such as the volume of data, the complexity of the data structures, and the need for real-time data availability.
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