🏪 Analogy

The warehouse system that organizes finished products for distribution

Problems Solved

  • Scalable data storage
  • Fast query performance
  • Data governance and security
  • Cost optimization

Understanding Data Storage

Types of Data Storage

Data Warehouses

Optimized for analytics with SQL interface and built-in optimizations

Examples: BigQuery, Snowflake, Redshift, Azure Synapse
Best for: Business intelligence, analytics, structured queries

Data Lakes

Scalable object storage for any data type with schema-on-read

Examples: S3, Google Cloud Storage, Azure Data Lake
Best for: Big data, machine learning, cost-effective storage

Lakehouses

Combining data warehouse performance with data lake flexibility

Examples: Databricks, Snowflake (with Iceberg), BigQuery (with BigLake)
Best for: Analytics + ML, ACID transactions, flexibility

Table Formats

Optimized file formats for analytical queries and data management

Examples: Parquet, Delta Lake, Iceberg, Hudi
Best for: Performance, ACID transactions, schema evolution

Recommended Tools

Tools for data storage by category:

Apache Druid

Use case: Fast analytics on streaming data

Distributed Real-time Analytics

Amazon S3

Use case: Object storage, data lakes

Data Lake Cloud Storage

Amazon Redshift

Use case: Complex analytical queries

Petabyte-scale Data Warehouse

Google Cloud Storage

Use case: Object storage, analytics

Data Lake Cloud Storage

Google BigQuery

Use case: Large-scale analytics

Serverless Data Warehouse

Azure Data Lake Storage

Use case: Enterprise data lakes

Data Lake Cloud Storage

Azure Synapse

Use case: Integrated analytics, data warehousing

Analytics Data Warehouse

Snowflake

Use case: Enterprise analytics

Cloud Warehouse Data Warehouse

Delta Lake

Use case: ACID transactions, reliability

Lakehouse Table Format

Apache Iceberg

Use case: Schema evolution, analytics

Lakehouse Table Format

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