Data Lakehouse Architecture: Unifying Analytics and Engineering
The Best of Both Worlds
For years, organizations maintained separate systems for data engineering (data lakes) and business analytics (data warehouses). Data lakes provided cheap, flexible storage for raw data. Data warehouses provided fast, structured queries for business intelligence. Maintaining both created complexity, duplication, and inconsistency.
The data lakehouse architecture promises to unify these workloads on a single platform. Built on open table formats like Delta Lake, Apache Iceberg, or Apache Hudi, lakehouses add warehouse-like capabilities — ACID transactions, schema enforcement, and fast queries — to the data lake.
How It Works
A lakehouse stores all data in open file formats (Parquet, ORC) on object storage (S3, ADLS, GCS). On top of this storage layer, a table format provides:
ACID transactions: Multiple writers can update the same table concurrently without corrupting data. Failed writes are automatically rolled back.
Schema enforcement and evolution: The table format enforces schema on write while supporting backward-compatible schema evolution. This prevents the "data swamp" problem that plagues traditional data lakes.
Time travel: Every change to a table is versioned. Users can query historical snapshots, roll back erroneous changes, and audit data modifications.
Partition management: Intelligent partitioning and file compaction optimize query performance without requiring users to understand the physical data layout.
The Table Format Landscape
Three open table formats dominate the lakehouse ecosystem:
Delta Lake: Created by Databricks and now an open-source Linux Foundation project. Deeply integrated with the Spark ecosystem. The most mature option with the largest production deployment base.
Apache Iceberg: Created at Netflix and now an Apache project. Designed for massive-scale tables with excellent partition evolution and vendor-neutral design. Adopted by Snowflake, AWS, and others.
Apache Hudi: Created at Uber for real-time data ingestion. Strong CDC and incremental processing capabilities. Well-suited for use cases that require frequent updates.
All three are converging in functionality. The choice increasingly depends on your existing ecosystem and vendor relationships rather than fundamental technical differences.
Lakehouse vs. Cloud Data Warehouse
The lakehouse does not replace the cloud data warehouse for all use cases. Here is an honest comparison:
Where lakehouses excel: Large-scale data engineering, machine learning workloads, unstructured and semi-structured data, cost-sensitive storage of historical data, and workloads that benefit from open formats and vendor flexibility.
Where warehouses excel: Interactive SQL analytics, business intelligence dashboards, ad-hoc queries by business users, and workloads that prioritize simplicity and managed operations over flexibility.
The pragmatic approach: Many organizations benefit from both. Use a lakehouse as the primary data platform for engineering and data science, and feed a data warehouse for business intelligence workloads that demand sub-second query performance and simplified governance.
Implementation Considerations
Compute Engine Selection
Lakehouses separate storage from compute, enabling you to choose the best engine for each workload:
- Apache Spark: The workhorse for large-scale data processing and machine learning
- Trino/Presto: Fast, interactive SQL queries across multiple data sources
- DuckDB: Lightweight, embedded analytics for development and small-scale queries
- Cloud-native engines: BigQuery, Athena, and Synapse can query lakehouse tables directly
Data Governance
Lakehouses require deliberate governance to avoid the chaos of traditional data lakes:
- Implement a data catalog that indexes all lakehouse tables with ownership and descriptions
- Define and enforce naming conventions and organizational standards
- Implement fine-grained access control at the table, column, and row level
- Track data lineage from source through transformation to consumption
Cost Optimization
Lakehouse storage costs are typically lower than warehouse storage, but compute costs require careful management:
- Right-size compute clusters for each workload type
- Use auto-scaling to match capacity to demand
- Implement file compaction and vacuum operations to maintain query performance
- Monitor and optimize query patterns to reduce unnecessary full-table scans
Migration Strategy
Migrating from a traditional data warehouse to a lakehouse is best done incrementally:
- Set up lakehouse infrastructure alongside your existing warehouse
- Begin landing new data sources in the lakehouse
- Build new data products on the lakehouse platform
- Gradually migrate existing warehouse workloads, starting with data engineering and machine learning
- Retain the warehouse for BI workloads that justify the cost premium
The goal is not to eliminate your warehouse overnight. It is to build a unified platform that reduces duplication and enables new capabilities.
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