Differences Between Database, Data Warehouse, and Data Lake

Introduction

• Video discussing the differences between a database, data warehouse, and data lake.
• Initially, only databases are commonly known, but data warehouses and data lakes are also important.

Database

• Relational Database: Most common type.
• OLTP (Online Transactional Process):
  • Captures and stores data in real-time.
  • Records transactions such as item sales.
  • Data is stored in tables with columns and rows.
• Characteristics:
  • Highly detailed and flexible schema allowing modifications.

Data Warehouse

• Also a Database: Primarily for analytical processing (OLAP - Online Analytical Processing).
• Data Entry:
  • Uses ETL (Extract, Transform, Load) process to aggregate and send data from multiple databases.
  • Stores summarized data rather than detailed data.
• Characteristics:
  • Contains historical data but may not always have current data depending on ETL frequency.
  • More rigid schema requiring thorough planning.

Key Differences: Database vs. Data Warehouse

• Usage:
  • Database: For recording transactions.
  • Data Warehouse: For analytics and reporting.
• Data Freshness and Detail:
  • Database: Fresh and detailed data.
  • Data Warehouse: Summarized data, freshness depends on ETL.
• Performance:
  • Database: Slower for large data queries, may affect transaction processing.
  • Data Warehouse: Designed for fast querying without impacting transaction processing.

Data Lake

• Purpose: Designed to capture any type of data (videos, images, documents, graphs, etc.).
• Use Cases: Particularly useful for machine learning and AI for utilizing structured and unstructured data.
• Characteristics:
  • Stores raw data which may require cleaning for analytical purposes.
  • Can hold unstructured or semi-structured data not suitable for databases.

Summary

• No One-Size-Fits-All:
  • Database: Ideal for recording transactions.
  • Data Warehouse: Suitable for handling large amounts of data and analytics.
  • Data Lake: Best for storing raw, unstructured, or semi-structured data.
• Combined Usage: Different components (database, data warehouse, data lake) can be used within the same company for different needs.