Remove Data Governance Remove Data Management Remove High Quality Data
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Modern Data Governance: Trends for 2025

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Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful data governance. Recognize that artificial intelligence is a data governance accelerator and a process that must be governed to monitor ethical considerations and risk.

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Practical First Steps In Data Governance For Long Term Success

Data Engineering Podcast

Summary Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. At what point does a lack of an explicit governance policy become a liability?

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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. Data lakes are notoriously complex. Your first 30 days are free!

Data Lake 262
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Troubleshooting Kafka In Production

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team.

Kafka 245
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Modern Data Architecture: Data Mesh and Data Fabric 101

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Key Takeaways: Data mesh is a decentralized approach to data management, designed to shift creation and ownership of data products to domain-specific teams. Data fabric is a unified approach to data management, creating a consistent way to manage, access, and share data across distributed environments.

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AI Success – Powered by Data Governance and Quality

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Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.

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Improve Data Quality Through Engineering Rigor And Business Engagement With Synq

Data Engineering Podcast

He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Can you describe what Synq is and the story behind it?