Build a Modern Data Lakehouse
An interactive guide to the architecture that unifies data lakes and data warehouses, enabling seamless BI, analytics, and AI on all your data.
Unified Platform
Combines the flexibility of data lakes with the performance and reliability of data warehouses, eliminating data silos.
Open & Scalable
Built on open standards and low-cost object storage, allowing for massive scale and preventing vendor lock-in.
Ready for AI/ML
Provides a single source of truth for structured and unstructured data, ideal for training advanced machine learning models.
The Evolution of Data Platforms
The data lakehouse emerged to solve the limitations of its predecessors. This section allows you to interactively compare the three architectures to understand their key differences in cost, performance, and flexibility.
Core Architecture: The Medallion Model
A data lakehouse logically organizes data into layers (or zones) to progressively improve quality and structure. The Medallion Architecture is a popular design pattern for this. Click on each layer to understand its role in creating a single source of truth.
Bronze (Raw)
Silver (Validated)
Gold (Curated)
The Technology Stack
A modern data lakehouse is not a single product but a combination of synergistic technologies. This section outlines the key components across storage, ingestion, processing, and table formats that work together to deliver the lakehouse promise.
Storage
Low-cost, scalable cloud object storage forms the foundation.
- AWS S3 Simple Storage Service
- Azure ADLS Data Lake Storage
- Google GCS Cloud Storage
Table Formats
Open formats add reliability and performance to raw data files.
- Delta Lake ACID, Time Travel
- Apache Iceberg Schema Evolution
- Apache Hudi Streaming Upserts
Processing Engines
Powerful engines for both batch and streaming analytics.
- Apache Spark Large-scale processing
- Trino/Presto Fast SQL queries
- Flink Stream processing
Data Ingestion
Tools for moving data from various sources into the lakehouse.
- Kafka/Kinesis Real-time streams
- Airbyte/Hevo Batch ETL/ELT
- Apache NiFi Data flow automation
Governance & Performance
To prevent a "data swamp" and ensure the lakehouse is usable, strong governance and performance optimization are essential. This section highlights the key pillars that provide reliability, security, and speed.
Unified Data Catalog
A central inventory of all data assets, providing discoverability, context, and lineage to build trust.
Fine-Grained Access Control
Secure data at the row, column, and table level using Role-Based Access Control (RBAC) to ensure compliance.
Schema Evolution
Manage changes to data structure over time without breaking pipelines or compromising data quality.
Strategic Partitioning
Organize data by common filter dimensions (like date or region) to drastically reduce query scan times.
File Compaction & Sizing
Merge small, inefficient files into larger, optimized files to improve read performance for analytical queries.
Intelligent Caching
Store frequently accessed data in memory to accelerate query response times for BI dashboards and reports.
Cloud Provider Implementations
Major cloud providers offer specialized services and best practices for building a data lakehouse. This section provides a snapshot of how each platform supports this modern architecture. Select a provider to view their key practices.