‘Data Governance in Multi-Tenant Data Lakes – A Tech Perspective’

Embark on a journey into the intricate world of data governance and multi-tenancy with our insightful webinar, presented by Satish Kes, Head of Engineering at Zeotap. With 18 years of experience crafting big data pipelines across various domains, Satish delves into the challenges and solutions of blending data governance with multi-tenancy environments.

What You’ll Learn:

  1. Understanding Data Governance:
    • Grasp the foundational principles of data governance and its importance in managing valuable business data.
    • Learn about the key aspects such as data quality measures, metadata capturing, and access controls.
  2. Multi-Tenancy Challenges:
    • Discover the expanding context of multi-tenancy and how it adds layers of complexity to data governance.
  3. Tech Building Blocks for Governance:
    • Explore the essential technological components for a robust governance system, including asset inventory, lineage, security mechanisms, and quality metrics.
  4. Policy Management:
    • Dive into creating dynamic, self-serve policies for data access and management that align with governance goals.
  5. Lifecycle and Ownership:
    • Understand the evolving lifecycle of data and the significance of ownership and permissions throughout the data’s journey.
  6. Quality Measurement:
    • Learn the importance of continuous quality measurement and the tools that can aid in maintaining high data standards.
  7. Solving the Lineage Problem:
    • Tackle the lineage problem in multi-tenancy architectures and the methodologies to attribute data sources accurately.
  8. Leveraging Graph Databases:
    • See the advantages of using graph databases for managing complex, multi-relational data governance challenges.
  9. Evaluating Tools for Governance:
    • Get insights into the criteria for evaluating governance tools and the leading solutions in the market.
  10. Security and Infrastructure:
    • Gain knowledge on implementing zero trust and positive security models in your data infrastructure.
  11. Towards Self-Serve Automation:
    • Embrace the path to self-serve and minimal operational support with declarative data workflows and automated provisioning systems.

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