A data lakehouse combines the vast, economical storage of data lakes with the structured management of data warehouses. This hybrid approach lets companies store all their data in one place while maintaining governance, performance, and reliability.
Understanding the data lakehouse architecture
A data lakehouse merges the flexibility of a data lake with the data management features of a data warehouse. It stores raw sensor logs, images, JSON files, and relational tables together while providing ACID transactions, schema enforcement, and indexing.
The architecture consists of several interconnected layers. Cloud object storage holds all raw data files in formats like Parquet or ORC. Open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi add a transactional metadata layer, transforming passive file storage into manageable tables with defined schemas and transaction logs.
The compute layer includes processing engines like Spark for transformations, Trino for interactive SQL queries, or Flink for streaming data. A metadata catalog tracks table definitions. A governance layer handles security and auditing.
Because lakehouses use open file formats, organizations avoid vendor lock-in. Teams can use preferred tools—Spark for engineering, Trino for SQL, TensorFlow for machine learning—all accessing the same data.
What distinguishes a lakehouse from a data lake is improved transactional support and additional functionality. Open table formats bring reliable updates, deletes, and time-travel querying to lake storage, enabling warehouse-style analytics without moving data.
The evolution from warehouses and lakes
Organizations initially relied on data warehouses for structured data and business intelligence, but these systems struggled with modern data volume and variety. Data lakes arose for storing massive raw data cheaply, yet lacked governance and performance for reliable analytics.
The term “data lakehouse” was first documented around 2017 when new technologies enabled warehouse-like capabilities directly on data lakes. Projects like Delta Lake, Apache Iceberg, and Apache Hudi brought structure and performance to these repositories.
Industry adoption
Cloud vendors quickly embraced the concept. AWS announced a “Lake House” architecture in 2019 to integrate Redshift with S3 data lakes. Netflix created Apache Iceberg to treat S3 data as transactional tables. Uber built Apache Hudi to handle incremental data processing at scale.
Netflix’s trajectory illustrates the shift. They maintained a massive Hadoop data lake but needed warehouse-like features for analytics. Rather than moving data to a separate warehouse, they developed Iceberg to add transactional capabilities directly to S3 storage—running ACID-compliant queries on exabytes of data while simplifying operations like GDPR deletion requests.
Core technical components and features
The lakehouse’s power comes from key technical features: ACID transactions ensure multiple users can reliably read and write concurrently. Schema enforcement maintains data integrity while allowing evolution. Indexing and pruning accelerate queries by skipping irrelevant data blocks. Time-travel enables querying data as it existed at any past point.
How the table format layer works
These capabilities stem from the open table format layer. When a query engine needs data, it consults the table format’s metadata for schema and file locations. For updates or deletes, the table format maintains a transaction log tracking changes—providing database-like reliability at lake storage costs.
Unified batch and streaming
Lakehouses unify batch and streaming data. Real-time events, including IoT feeds, clickstreams, and transaction logs, land in the same tables as historical data, enabling near-real-time analytics without separate infrastructure.
The separation of storage and compute lets compute engines scale independently. Organizations can run hundreds of concurrent queries during business hours, then scale down overnight.
Practical benefits and real-world impact
56% of organizations report over 50% savings in analytics costs after adopting a lakehouse, stemming from the elimination of duplicate data and inexpensive cloud storage. The unified platform accelerates insights by removing lengthy ETL—data becomes available hours or days faster.
Enabling AI and machine learning
Traditional warehouses can’t handle the 80-90% of enterprise data that’s unstructured, including text, images, and sensor logs. That’s why 81% of organizations use lakehouses for AI model development. Algorithms train on raw data while analysts query refined tables, all within the same system.
Real-world examples
Zalando demonstrates this unified approach. They transitioned from siloed systems to a lakehouse on Amazon S3, combining clickstream data with purchase history in a single SQL query. This powered customer 360 analyses and improved their recommendation engine. 7bridges achieved 98% faster reporting by querying data in place rather than waiting for lengthy pipelines.
Implementation considerations and best practices
Organizations typically begin by ingesting data into cloud object storage using open formats, organized into zones—raw, refined, and business-ready. The medallion architecture uses Bronze tables for raw data, Silver for cleansed data, and Gold for aggregated information.
Choosing a table format
Table format selection matters. Apache Iceberg offers broad engine compatibility and multi-cloud support. Delta Lake integrates tightly with Databricks and Spark. Meanwhile, Apache Hudi excels at incremental processing and streaming upserts. Establishing a metadata catalog from day one is essential for discovery and governance.
Operational considerations
Operational considerations remain important. In particular, teams must address small-file issues that impact performance, implement appropriate partitioning strategies, and establish maintenance procedures. Clear data ownership models help—domain teams are responsible for quality, platform teams provide infrastructure.
Looking forward
The data lakehouse is becoming the analytics standard. 70% of enterprises expect to run most analytics on lakehouses within three years. The market is projected to reach $10.4 billion in 2025, up from $8.5 billion in 2024.
Open table formats continue evolving, with Apache Iceberg gaining momentum as organizations prioritize vendor neutrality. Boundaries between warehouses and lakehouses are blurring as vendors add lakehouse capabilities to their platforms.
FAQs
What is a data lakehouse?
A hybrid architecture combining data lake storage with warehouse-grade management—ACID transactions, schema enforcement, and indexing—in a single platform.
How does a data lakehouse work?
Open table formats (Iceberg, Delta Lake, Hudi) layer transactional metadata over cloud object storage. Compute engines query through this layer while a catalog tracks schemas, and governance enforces security.
What’s the difference between a data lake and a data lakehouse?
A data lake stores raw files cheaply but lacks structure and reliability. A lakehouse adds ACID transactions, enforced schemas, and governance—enabling SQL analytics directly on lake storage.
What are the main open table formats for lakehouses?
Three formats dominate: Apache Iceberg (broad engine compatibility, multi-cloud), Delta Lake (Databricks/Spark integration), and Apache Hudi (streaming and incremental updates). All provide ACID transactions and time-travel queries.
When should an organization choose a lakehouse over a data warehouse?
When you need to analyze structured and unstructured data together, support both BI and machine learning on one platform, avoid vendor lock-in, or reduce costs by eliminating duplicate data copies.
