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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

Data quality leaders need to determine: Where the change should occur (source systems, data lakes, or at the point of analysis). Who should make the change (data engineers, system owners, or data quality professionals). Why is the change necessary (alignment with business objectives or regulatory compliance)?

Scorecard 180
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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

This has led to the emergence of real-time OLAP solutions, which are particularly relevant in the following use cases: User-facing analytics – Incorporating analytics into products or applications that consumers use to gain insights, sometimes referred to as data products. Anomaly detection – Identifying outliers or unusual behavior patterns.

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