Data Lakes Fundamentals and Best Practices

Embark on an enlightening journey through the evolution of data lakes with Shreya Pal, Associate Director of Data and Technology at Cognizant. With 17 years of industry experience, Shreya shares invaluable insights into the challenges and triumphs of building data lakes, especially in the realms of big data and cloud computing. Discover the transformation of data lakes over the last decade, understand the current trends, and learn how to leverage best practices for your data lake architecture to avoid potential pitfalls.

What You’ll Learn:

  1. Genesis and Evolution of Data Lakes:
    • Explore the origin of data lakes and how they’ve transformed from centralized storage repositories to sophisticated, agile platforms enabling incremental growth and diverse analytics capabilities.
  2. Navigating the Data Landscape:
    • Understand the impact of RDBMS, big data, and cloud adoption on the evolution of data lakes, emphasizing the shift towards more distributed, cost-effective, and scalable data storage solutions.
  3. Architectural Innovations and Cloud Integration:
    • Learn how cloud technologies have revolutionized data lake architectures, offering flexibility in scaling and reducing operational costs, and the role of platforms like Qubole in simplifying big data and cloud integrations.
  4. Designing Data Lakes with Purpose:
    • Gain insights into the critical considerations for designing effective data lakes, including focusing on relevant use cases, adopting an incremental building approach, and ensuring just-in-time data enablement for specific analytics needs.
  5. Embracing Modern Data Management Practices:
    • Dive into the importance of real-time data management, self-service analytics, sandbox environments, and multi-platform support to meet the dynamic needs of today’s data-driven organizations.
  6. Key Principles for Data Lake Success:
    • Discover the foundational principles vital for any data lake strategy, covering data quality, security, privacy, compliance, lineage, metadata management, and the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles.
  7. Conceptual Data Lake Architecture Overview:
    • Visualize a high-level conceptual architecture of a data lake, detailing the source, raw data store, transformation, insights, and consumer layers, underscored by essential services like monitoring, security, and DevOps.

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