Database develop. life cycle - Data warehouses and data lakes

1. Data Warehouse

Definition:

  • A data warehouse is a centralized repository for structured and processed data from multiple sources.

  • Data is organized for reporting, analysis, and business intelligence (BI).

Characteristics:

  • Stores structured data (tables, rows, columns)

  • Schema-on-write → data is cleaned and formatted before storage

  • Optimized for query performance and analytics

  • Supports historical data

Advantages:

  • Fast and optimized queries

  • Ensures data quality and consistency

  • Useful for decision-making and reporting

Disadvantages:

  • Expensive to scale

  • Not suitable for raw or unstructured data

  • Data preparation (ETL) can be time-consuming

Example:

  • Amazon Redshift, Google BigQuery, Snowflake

Use Case:

  • A retail company analyzing sales trends, customer behavior, and inventory


2. Data Lake

Definition:

  • A data lake is a storage system that holds raw, unprocessed data in its native format (structured, semi-structured, or unstructured).

Characteristics:

  • Stores all types of data: JSON, XML, images, videos, logs

  • Schema-on-read → data is structured when accessed

  • Highly scalable, often on cloud storage

  • Supports big data and machine learning

Advantages:

  • Can store massive volumes of raw data

  • Flexible → handles structured and unstructured data

  • Ideal for advanced analytics, AI, and ML

Disadvantages:

  • Query performance is slower than data warehouses

  • Data governance and quality can be challenging

  • Without proper management → can become a “data swamp”

Example:

  • Amazon S3 + AWS Lake Formation, Azure Data Lake, Google Cloud Storage

Use Case:

  • A social media company storing user activity logs, images, and videos for AI analysis


3. Key Differences

Feature Data Warehouse Data Lake
Data Type Structured only Structured, semi-structured, unstructured
Schema Schema-on-write Schema-on-read
Storage Cost High (optimized for performance) Low (raw storage, scalable)
Processing ETL (Extract, Transform, Load) ELT (Extract, Load, Transform)
Use Case Business intelligence, reporting Big data analytics, AI/ML
Performance Fast for queries Slower for complex queries
Flexibility Less flexible Highly flexible

Summary:

  • Data Warehouse: Clean, structured, ready for reporting and business intelligence.

  • Data Lake: Raw, flexible, ideal for advanced analytics and machine learning.