Data Lakehouse: the best of both worlds
Combine the flexibility of a data lake with the performance of a data warehouse
Unified Platform
One platform for BI, ML and streaming. No more data duplication needed.
ACID + Flexibiliteit
Transactie-garanties op open formaten. Time travel en schema evolution ingebouwd.
Kostenefficient
Up to 90% lower storage costs than traditional warehouses. Pay-as-you-go compute.
Governance Ingebouwd
Data lineage, access control and compliance. Prevent your lake from becoming a swamp.
What is a Data Lakehouse?
A data lakehouse is a modern data architecture that combines the best properties of data lakes en data warehouses It offers the scalability and flexibility of a data lake (inexpensive object storage, all data types) with the reliability and performance of a data warehouse (ACID transactions, schema enforcement, fast queries).
The evolution of data architectures
For years organizations had to choose: a data warehouse for BI and reporting, or a data lake for machine learning and big data. This led to duplicate storage, complex ETL pipelines and inconsistent data between systems.
The lakehouse architecture puts an end to this dichotomy. Through technologies such as Delta Lake, Apache Iceberg and Apache Hudi, organizations can apply ACID transactions and schema enforcement directly on data lake storage. The result: a unified platform for all analytics workloads.
What does this mean in practice? Your data scientists and your business analysts finally work with the same data source. No more discussions about why numbers differ between the ML model and the management dashboard. Data is stored once, managed once and used multiple times.
At EasyData we see that many organizations are now at this crossroads. The existing warehouse solution no longer meets the growing demand for advanced analytics, but a full migration seems risky. The lakehouse approach offers an evolution path: you retain the reliability you are used to, while opening the door to new possibilities such as real-time analytics and AI-toepassingen. More about enterprise datamanagement. As a European company we guarantee that your data is processed within digitaal soevereine infrastructure, supported by our cloudoplossingen en on-premise opties.
Lakehouse vs Warehouse vs Lake
| Kenmerk | Data Warehouse | Data Lake | Data Lakehouse |
|---|---|---|---|
| ACID Transacties ACID guarantees that data modifications are processed reliably. Essential when processing financial data or customer data. | Volledig | Not native | Volledig (Delta/Iceberg) |
| Schema Enforcement Schema enforcement determines how strictly the data structure is enforced. Schema-on-write checks during storage, schema-on-read only during reading. | Schema-on-write | Schema-on-read | Beide ondersteund |
| Storage Kosten Warehouse storage is more expensive due to proprietary formats. A lakehouse uses open formats on inexpensive object storage, which can make up to 90% difference. | Hoog (proprietary) | Laag (object storage) | Laag (open formats) |
| BI/Rapportages Business Intelligence reporting requires fast, consistent queries. Lakehouses offer the same BI performance as warehouses thanks to caching and indexing. | Excellent | Beperkt | Excellent |
| Machine Learning ML workloads require access to raw data in diverse formats. A lakehouse provides direct access for data scientists without copying data. | Beperkt | Excellent | Excellent |
| Streaming Data Real-time data ingestion is crucial for IoT, monitoring and live dashboards. A lakehouse combines streaming with ACID guarantees. | Via ETL | Native | Native + ACID |
| Data Governance Governance encompasses access rights, data lineage and auditability. Essential for GDPR compliance and responsible data management. | Sterk | Uitdagend | Sterk (Unity Catalog) |
| Vendor Lock-in Open table formats such as Delta and Iceberg work with multiple engines. Your data remains yours, regardless of which tools you choose tomorrow. | Hoog | Laag | Laag (open formats) |
Lakehouse Architectuur Lagen
Storage Layer
- Object storage (S3, Azure Blob, GCS)
- Open bestandsformaten (Parquet)
- Columnar storage optimalisatie
- Onbeperkte schaalbaarheid
- Pay-per-use pricing model
- Multi-cloud ondersteuning
Transaction Layer
- Delta Lake / Iceberg / Hudi
- ACID transactie garanties
- Time travel (data versioning)
- Schema evolution support
- Concurrent schrijfoperaties
- Rollback mogelijkheden
Consumption Layer
- SQL analytics (Spark SQL)
- BI tool integraties
- Machine learning workloads
- Streaming analytics
- Data science notebooks
- API access for applications
Lakehouse Technologieen
Delta Lake
Open-source storage layer from Databricks. ACID transactions, time travel, and schema enforcement on Parquet files.
Apache Iceberg
Table format for analytical datasets. Hidden partitioning, snapshot isolation and vendor-neutral.
Apache Hudi
Streaming data lakehouse platform. Record-level updates, incremental processing en change data capture.
Databricks
Unified analytics platform. Combines Delta Lake with managed Spark, ML and SQL analytics.
Benefits of a Data Lakehouse
Unified Analytics
One platform for BI reporting, machine learning and streaming analytics. No data duplication or ETL complexity.
TCO Reductie
Up to 90% lower storage costs through open formats. Eliminate expensive warehouse licenses and duplicate data storage.
Time Travel
View data as it was at any point in the past. Essential for audits, debugging and compliance.
Data Governance
Central management of access rights, data lineage and compliance. Unity Catalog for enterprise governance.
Performance
Z-ordering, data skipping and caching provide warehouse-like query performance on lake storage.
No Vendor Lock-in
Open table formats work with multiple engines. Retain control over your data. Read more about datasoevereiniteit en digitale onafhankelijkheid.
Lakehouse Use Cases
Real-time BI en Analytics
Combine batch and streaming data for up-to-date dashboards. ACID transactions guarantee consistent reporting while new data flows in.
MLOps en Feature Stores
Train ML models directly on production data. Feature stores with versioning and lineage for reproducible experiments.
Change Data Capture
Stream database changes to the lakehouse for near real-time analytics. Maintain complete audit trail with time travel.
Regulatory Compliance
GDPR, SOX and other compliance requirements. Data lineage, access logging and point-in-time recovery for audits.
IoT en Sensor Data
Process millions of events per second with streaming ingestion. Combine with historical data for predictive maintenance.
Data Mesh Architectuur
Support decentralized data ownership with shared governance. Domain teams manage their own data products.
Interested in a modern data architecture?
Discover how a data lakehouse can help your organization. Request no-obligation architecture advice.
What you can expect
Architectuur Assessment Analysis of your current data landscape and lakehouse readiness
Technologie Advies Delta Lake, Iceberg or Hudi – which fits your use cases
Migration Roadmap Step-by-step plan for transition to lakehouse architecture
Europese Expertise 25+ jaar ervaring in datamanagement en Europese dataverwerking
Veelgestelde vragen
Een data warehouse uses proprietary storage with schema-on-write and is optimized for BI queries. A lakehouse combines the low costs of object storage (like a data lake) with ACID transactions and schema enforcement. The result is a unified platform for both BI and machine learning workloads.
Delta Lake is an open-source storage layer that adds ACID transactions to Apache Spark and data lakes. It stores data in Parquet format with a transaction log that tracks all changes. This enables time travel, rollbacks and concurrent writes on standard object storage.
The choice depends on your ecosystem and use cases. Delta Lake works optimally with Databricks and Spark. Iceberg is vendor-neutral and supports multiple query engines. Hudi excels in streaming and record-level updates. All three offer ACID compliance.
Yes, migration is possible and often cost-effective. Start with new workloads on the lakehouse, gradually migrate historical data, and keep the warehouse temporarily operational for legacy reporting. EasyData guides organizations through this transition with a phased migratieplan. Neem contact op for a no-obligation consultation.
Modern lakehouses achieve warehouse-like performance through techniques such as Z-ordering (data clustering), data skipping, caching and columnar storage (Parquet). For many BI workloads the performance is comparable to dedicated warehouses.
Yes, through pay-as-you-go pricing, kleinere organisaties can also benefit. You only pay for the storage and compute you actually use. Managed services reduce operational overhead.
Platforms such as Databricks Unity Catalog offer enterprise governance: central access control, data lineage tracking, audit logging and compliance features. You define policies at table or column level that are automatically enforced. This aligns with the requirements of the AVG.
Time travel lets you view data as it was at any point in the past. Essential for: reproducing ML experiments, debugging data issues, compliance audits, and recovering accidentally deleted data. Delta Lake retains 30 days of history by default.
