What is a Data Lakehouse?
A data lakehouse combines the flexibility and cost-effectiveness of data lakes with the performance and ACID transactions of data warehouses, creating a unified analytics platform.
Data Lake Benefits
- Store all data types (structured, semi-structured, unstructured)
- Low-cost object storage (S3, ADLS, GCS)
- Schema-on-read flexibility
- Support for ML and advanced analytics
Data Warehouse Benefits
- ACID transactions for data consistency
- High-performance SQL queries
- Schema enforcement and data quality
- BI tool integration
Lakehouse Technologies
Delta Lake (Databricks)
Open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.
- ACID transactions with optimistic concurrency control
- Time travel (data versioning)
- Schema enforcement and evolution
- Unified batch and streaming
Apache Iceberg
Open table format for huge analytic datasets, designed for high performance and reliability.
- Hidden partitioning (no partition predicates needed)
- Partition evolution without rewriting data
- Time travel and rollback
- Multi-engine support (Spark, Trino, Flink)
Apache Hudi
Transactional data lake platform that brings database and data warehouse capabilities to the data lake.
- Upserts and deletes on data lakes
- Incremental processing
- Change data capture (CDC)
- Snapshot isolation
4-Phase Lakehouse Migration Process
Assessment & Architecture Design
Week 1- Analyze current data lake/warehouse architecture
- Choose lakehouse technology (Delta Lake, Iceberg, Hudi)
- Design medallion architecture (Bronze, Silver, Gold)
- Plan data governance and security
Infrastructure Setup
Week 2- Set up cloud storage (S3, ADLS, GCS)
- Configure compute engines (Spark, Trino, Presto)
- Implement lakehouse format (Delta/Iceberg/Hudi)
- Set up metadata catalog (Hive Metastore, AWS Glue, Unity Catalog)
Data Migration & Transformation
Weeks 3-4- Migrate raw data to Bronze layer (as-is ingestion)
- Transform to Silver layer (cleaned, validated)
- Create Gold layer (business-ready aggregates)
- Implement incremental processing pipelines
Validation & Cutover
Week 5- Validate data quality and completeness
- Performance testing and optimization
- Migrate BI tools and analytics workloads
- Production cutover with monitoring
Data Lake vs Warehouse vs Lakehouse
| Feature | Data Lake | Data Warehouse | Data Lakehouse |
|---|---|---|---|
| Data Types | All types (structured, semi-structured, unstructured) | Structured only | All types |
| ACID Transactions | ❌ No | ✅ Yes | ✅ Yes |
| Schema | Schema-on-read | Schema-on-write | Both (flexible) |
| Query Performance | Slow (full scans) | Fast (optimized) | Fast (optimized) |
| Storage Cost | Low (object storage) | High (proprietary) | Low (object storage) |
| ML/AI Support | ✅ Excellent | ❌ Limited | ✅ Excellent |
| BI Tool Support | ❌ Limited | ✅ Excellent | ✅ Excellent |
| Data Quality | ❌ No enforcement | ✅ Strong enforcement | ✅ Strong enforcement |
| Time Travel | ❌ No | ⚠️ Limited | ✅ Yes |
| Streaming Support | ✅ Yes | ❌ No | ✅ Yes |
| Feature | DataMigration.AI | Manual Approach |
|---|---|---|
| Lakehouse Technology Selection | AI analyzes workload and recommends optimal format (Delta/Iceberg/Hudi) | Manual evaluation and testing required |
| Migration Timeline | 3-5 weeks with automated setup | 3-6 months with manual configuration |
| Medallion Architecture Setup | Automated Bronze/Silver/Gold layer creation | Manual pipeline development for each layer |
| Data Format Conversion | Automated conversion to Parquet/Delta/Iceberg | Custom scripts for format conversion |
| Schema Evolution | Automatic schema tracking and evolution | Manual schema management and versioning |
| Performance Optimization | AI-powered partitioning and Z-ordering | Manual tuning based on query patterns |
| Cost Savings | 75% reduction vs traditional warehouse | Varies based on implementation quality |
| ACID Compliance | 100% guaranteed with automated testing | Requires extensive manual validation |
| Time Travel Setup | Automatic versioning and rollback capability | Manual snapshot management required |
| Success Rate | 99.9% with automated validation | 70-80% due to configuration complexity |
DataMigration.AI automates lakehouse migration with intelligent technology selection, automated architecture setup, and continuous optimization, delivering 75% cost savings in 3-5 weeks.
People Also Ask
What's the difference between Delta Lake, Iceberg, and Hudi?
Delta Lake (Databricks) offers the best integration with Spark and Databricks platform, with excellent performance and ease of use. Apache Iceberg provides the most flexible architecture with hidden partitioning and multi-engine support (Spark, Trino, Flink). Apache Hudi excels at upserts and incremental processing, making it ideal for CDC workloads. Choose Delta Lake for Databricks environments, Iceberg for multi-engine flexibility, and Hudi for heavy upsert/CDC requirements.
Can I migrate from data warehouse to lakehouse without downtime?
Yes. Use a dual-write approach where data is written to both warehouse and lakehouse during migration. Gradually migrate read workloads (BI tools, analytics) to the lakehouse while validating performance and accuracy. Once all workloads are migrated and validated, decommission the warehouse. This approach ensures zero downtime and allows rollback if issues arise.
How much does lakehouse migration cost compared to warehouse?
Lakehouse typically costs 75% less than traditional data warehouses. Storage costs drop from $23/TB/month (Snowflake) to $2-5/TB/month (S3/ADLS/GCS). Compute costs are also lower with open-source engines (Spark, Trino) vs proprietary warehouse compute. A 100TB warehouse costing $200K/year can be replaced with a lakehouse costing $50K/year, saving $150K annually.
What is medallion architecture in lakehouse?
Medallion architecture organizes data into three layers: Bronze (raw data as-is from sources), Silver (cleaned, validated, deduplicated data), and Gold (business-ready aggregates and features). This architecture provides clear data lineage, incremental processing, and separation of concerns. Bronze enables data recovery, Silver ensures quality, and Gold optimizes for consumption.
Can BI tools connect directly to lakehouse?
Yes. Modern BI tools (Tableau, Power BI, Looker, Qlik) can connect to lakehouses via SQL engines like Databricks SQL, Trino, Presto, or Athena. These engines provide JDBC/ODBC connectivity and translate SQL queries to lakehouse format (Delta/Iceberg/Hudi). Performance is comparable to traditional warehouses with proper optimization (partitioning, Z-ordering, statistics).