Data Warehouses & Lakehouses

Overview

Data warehouses are optimized for analytical queries across large volumes of structured and semi-structured data. The newer "lakehouse" paradigm combines the flexibility of data lakes with the performance and governance of data warehouses.

Market Size

The cloud data warehouse market exceeds $15 billion and is one of the fastest-growing segments in enterprise data infrastructure.

Top Players

Snowflake

• Company: Snowflake Inc. (USA)
• Market Position: Leader in cloud data warehousing
• Key Strengths: Separation of compute and storage, near-zero administration, data sharing/marketplace, multi-cloud (AWS/Azure/GCP)
• Key Features: Time Travel, zero-copy cloning, Snowpark (Python/Scala), Cortex AI
• Typical Customers: Data-driven enterprises across all industries

Databricks

• Company: Databricks Inc. (USA)
• Market Position: Leader in data lakehouse architecture
• Key Strengths: Unified analytics + AI platform, Delta Lake (open-source), Apache Spark foundation, MLflow integration
• Key Features: Unity Catalog (governance), Delta Sharing, Databricks SQL, AI/ML notebooks
• Typical Customers: Data engineering and data science teams, AI-heavy organizations

Google BigQuery

• Company: Google Cloud (USA)
• Market Position: Pioneer in serverless data warehousing
• Key Strengths: Fully serverless, blazing fast SQL at petabyte scale, ML built-in (BigQuery ML), real-time streaming
• Key Features: Slots-based pricing, BI Engine, BigLake (multi-format), Omni (multi-cloud)
• Typical Customers: Google Cloud users, analytics-heavy organizations

Amazon Redshift

• Company: Amazon Web Services (USA)
• Market Position: Established cloud warehouse leader
• Key Strengths: Deep AWS ecosystem integration, Redshift Serverless, RA3 instances (separate compute/storage), Spectrum (query S3)
• Typical Customers: AWS-centric organizations, enterprise analytics teams

Microsoft Fabric

• Company: Microsoft (USA)
• Market Position: Emerging all-in-one analytics platform (replacing Synapse)
• Key Strengths: Unified SaaS analytics (data engineering + science + warehousing + BI), OneLake, Power BI integration, Copilot
• Typical Customers: Microsoft-centric enterprises, Power BI users

Key Trends

• Lakehouse convergence: Warehouses adding lake features, lakes adding warehouse features
• Open table formats: Apache Iceberg, Delta Lake, and Apache Hudi becoming industry standards
• AI-native warehouses: Built-in vector search, ML training, and LLM serving in data platforms
• Data sharing: Cross-organization data sharing and data marketplaces becoming mainstream