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Data Speaks for Itself: Placing Data Governance in Context

TDAN

Yes, let’s talk about data governance, that thing we love to hate. I just attended the 17th Annual Chief Data Officer and Information Quality Symposium in July, and there, I heard many creative suggestions for renaming data governance.

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5 Ways Data Engineers Can Support Data Governance

Alation

These data requirements could be satisfied with a strong data governance strategy. Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. How can data engineers address these challenges directly?

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How to rule your data world: The role of data governance

BI-Survey

With the growing interconnectedness of people, companies and devices, we are now accumulating increasing amounts of data from a growing variety of channels. New data (or combinations of data) enable innovative use cases and assist in optimizing internal processes. This is where data governance comes in. .

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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

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Why erwin Data Intelligence 12 led me to Quest

erwin

First, erwin Data Intelligence 12.0 delivers new integrated data quality capabilities for customers. Customers then use the erwin Data Quality platform to automate the data discovery, profiling and quality assessment of their data sources. Figure 1: erwin Data Intelligence 12.0

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Best Practices for Data Catalog Implementation

Octopai

For business users Data Catalogs offer a number of benefits such as better decision-making; data catalogs provide business users with quick and easy access to high-quality data. This availability of accurate and timely data enables business users to make informed decisions, improving overall business strategies.

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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

This ensures that each change is tracked and reversible, enhancing data governance and auditability. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks.

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