Data Lakehouse
A data architecture that combines the low-cost storage and flexibility of a data lake with the structured querying and performance of a data warehouse, supporting both raw and curated data in one system.
Also known as: lakehouse architecture, unified data platform
Why It Matters
Historically, companies faced a choice: store raw, unstructured data cheaply in a data lake (but struggle to query it), or structure everything into a data warehouse (but pay more and lose flexibility). The lakehouse eliminates this tradeoff by adding warehouse-like query capabilities on top of lake-like storage.
For analytics teams, this means you can run fast SQL queries against structured tables for your dashboards while also storing raw event logs, JSON payloads, and semi-structured data for future analysis. You do not have to decide upfront exactly how you will use every piece of data.
The lakehouse architecture also reduces data duplication. Instead of maintaining a lake for data scientists and a separate warehouse for analysts, both teams work from the same underlying storage. This simplifies governance, reduces costs, and ensures everyone is working from the same data.
Industry Applications
A large marketplace migrates from separate data lake and warehouse systems to a unified lakehouse. This eliminates data freshness discrepancies between their analytics dashboards (fed by the warehouse) and their recommendation models (fed by the lake), improving recommendation relevance by 15%.
A product analytics company uses a lakehouse to store both structured product usage events and semi-structured API logs. When a customer reports a data discrepancy, the team can trace the issue from the curated analytics table back to the raw API payload without switching systems.
How to Track in KISSmetrics
Export KISSmetrics event data to a lakehouse platform (Databricks, Snowflake, or BigQuery) for advanced analysis. The lakehouse can serve as your unified storage layer, combining KISSmetrics behavioral data with application data, third-party data, and machine learning features in one queryable environment.
Common Mistakes
- -Adopting lakehouse architecture for its buzzword value when a simpler data warehouse would serve your needs
- -Not implementing proper table formats (Delta Lake, Iceberg, Hudi) which are what make the "warehouse" part of lakehouse work
- -Neglecting data catalog and discovery tools, making the lakehouse difficult to navigate
- -Over-engineering for scale you do not yet have - many companies would be better served by a well-managed warehouse until they reach hundreds of terabytes
Pro Tips
- +Use medallion architecture (bronze/silver/gold layers) to organize data from raw ingestion through curated analytics tables
- +Start with a managed lakehouse service rather than building from open-source components to reduce operational burden
- +Implement fine-grained access controls from day one - the flexibility of a lakehouse makes governance more important, not less
- +Use your lakehouse to store raw KISSmetrics events alongside enriched versions so you can always reprocess from source data
Related Terms
Data Warehouse
A centralized repository that stores large volumes of structured and semi-structured data from multiple sources, optimized for analytical queries and reporting rather than transactional processing.
ETL Pipeline
A data integration process that Extracts data from source systems, Transforms it into a consistent format, and Loads it into a destination system like a data warehouse for analysis.
Batch Processing
A data processing approach that collects events over a defined time period and processes them together as a group, typically on hourly or daily schedules, optimized for throughput and complex computations.
Real-Time Streaming
A data processing approach that ingests, processes, and delivers data continuously as events occur, rather than collecting data in batches for periodic processing.
Data Governance
The framework of policies, processes, and standards that ensure data across an organization is accurate, consistent, secure, and used in compliance with regulations and business rules.
See Data Lakehouse in action
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