Lecture on Data Lake House and History of Data Management

Introduction

• Topic: Origin and purpose of the Data Lake House
• Focus: Challenges of managing Big Data

History of Data Management and Analytics

1980s: Data Warehouses

• Need: Businesses wanted data-driven insights for decisions and innovation.
• Solution: Development of data warehouses to manage and analyze high volumes of data.
• Features:
  • Structured and clean data with predefined schemas.
  • Supported business intelligence (BI) and analytics.
• Limitations:
  • Not suitable for semi-structured or unstructured data.
  • Expensive for non-schema data.

Early 2000s: Big Data and Data Lakes

• Catalyst: Increased volume, velocity, and variety of data with digital growth.
• Development: Data Lakes to handle structured, semi-structured, and unstructured data simultaneously.
• Features:
  • Stored multiple data types side by side.
  • Quick and cheap storage in low-cost cloud object stores.
• Challenges:
  • Lack of support for transactional data and enforcing data quality.
  • Slower performance for analysis.
  • Governance issues with security and privacy.

Complex Technology Stacks

• Involving data lakes, warehouses, and specialized systems (e.g., for streaming, time-series).
• Problems:
  • Complexity and delays due to disjointed work and data teams in silos.
  • Data had to be copied between systems, impacting governance and increasing costs.
  • Difficult to implement AI and achieve actionable outcomes.

Introduction of Data Lake House

Need for New Architecture

• Businesses required a single, flexible, high-performance system for:
  • Data exploration
  • Predictive modeling and analytics
  • Diverse data applications (e.g., SQL analytics, real-time analysis, data science, machine learning)
• Goal: Address challenges and provide a unified platform.

Features of Data Lake House

• Combination: Benefits of data lakes with analytical power of data warehouses.
• Key Features:
  • Transaction support (ACID transactions)
  • Schema enforcement and governance
  • Robust data governance for privacy and regulatory compliance
  • BI support to reduce insight latency
  • Decoupled storage from compute (independent scaling)
  • Open storage formats (e.g., Apache Parquet)
  • Support for diverse data types (structured, semi-structured, unstructured)
  • Support for diverse workloads (data science, machine learning, SQL analytics)
  • End-to-end streaming for real-time reports

Benefits

• Single reliable source of truth for AI and BI.
• Unified location for data analysts, engineers, and scientists.
• Modern solution providing flexibility and comprehensive data handling.

Conclusion

• The Data Lake House represents the evolution of data management systems, integrating the strengths of both data lakes and warehouses while addressing the challenges posed by Big Data.