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Data Observability: Better Insights Through Reliable Data Practices

DZone

This is an article from DZone's 2023 Data Pipelines Trend Report. For more: Read the Report Organizations today rely on data to make decisions, innovate, and stay competitive. That data must be reliable and trustworthy to be useful.

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Phased Approach to Data Warehouse Modernization

DZone

Based on the scale of your existing data warehouse processes or jobs, it can be an enormous task to modernize. A modernized database will help you focus on building innovative solutions rather than investing your time and effort in managing these legacy systems.

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Financial Data Engineering in SAS

DZone

Financial data engineering in SAS involves the management, processing, and analysis of financial data using the various tools and techniques provided by the SAS software suite. Here are some key aspects of financial data engineering in SAS: 1.

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Viking Enterprise Solutions: Empowering Modern Data Infrastructure

DZone

In today's rapidly evolving technological landscape, developers, engineers, and architects face unprecedented challenges in managing, processing, and deriving value from vast amounts of data.

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Dynatrace completed Data Privacy Framework self-certification

Dynatrace

To enable participating organizations to meet the EU requirements for transferring personal data to the U.S., the Data Privacy Framework (DPF) is designed to serve as an adequate data transfer mechanism under the GDPR. Data Privacy Framework Program (The EU-U.S. Benefits of Data Privacy Framework for Dynatrace customers.

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Master the Art of Querying Data on Amazon S3

DZone

In an era where data is the new oil, effectively utilizing data is crucial for the growth of every organization. It is not enough to store these data durably, but also to effectively query and analyze them. Without a querying capability, the data stored in S3 would not be of any benefit.

Big Data 278
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Our First Netflix Data Engineering Summit

The Netflix TechBlog

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!