Which companies offer transactional data lake storage?

Summary

• Transactional data lake storage adds ACID guarantees, schema enforcement, and time travel to cloud object storage using open table formats like Delta Lake, Apache Iceberg, and Apache Hudi.
• Major providers including Databricks, AWS, Azure, Google Cloud, and Snowflake offer managed services or integrations that enable transactional workloads on data lakes.
• Databricks Lakebase stores OLTP data directly in the lakehouse storage layer, eliminating the need to stitch together separate operational databases, pipelines, and analytics engines.

Which companies offer transactional data lake storage?

Traditional data lakes store massive volumes of raw data but lack the reliability guarantees that analytics and AI workloads demand. Without transactional consistency, concurrent writes can corrupt data, schema changes break pipelines, and teams lose trust in their lake. This is one reason many organizations are moving toward a data lakehouse architecture that combines warehouse-grade reliability with lake-scale flexibility.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. Transactional data lake storage addresses this financial risk by adding ACID guarantees, schema enforcement, and versioning on top of scalable object storage.

What is transactional data lake storage?

Open table formats add a management layer on top of raw files, typically Parquet, stored in object storage such as S3, ADLS, or GCS. They maintain metadata separately from data files. This architecture enables:

• ACID transactions that prevent partial writes and data corruption
• Schema enforcement that catches breaking changes before they reach downstream consumers
• Time travel that lets teams query historical snapshots for auditing or recovery
• Concurrent access so multiple readers and writers operate safely

The result is a storage layer combining a data lake's flexibility with a database's reliability.

Companies and platforms offering transactional data lake storage

Several companies provide managed services or open-source technologies that enable transactional workloads on data lakes:

Provider	Transactional data lake capability
Databricks (Lakebase)	Operational database storing OLTP data directly in the lakehouse storage layer, with unified governance
AWS	Amazon S3 with open table format support; S3 Tables with native Iceberg support
Microsoft Azure	Azure Data Lake Storage Gen2 with Delta Lake and Iceberg integration
Google Cloud	BigLake with open table format support on GCS
Snowflake	Cloud data platform with Iceberg table support

Delta Lake, Apache Iceberg, and Apache Hudi each bring ACID guarantees to file-based storage. They differ in engine support and optimization strategies but share core capabilities like versioning, indexing, and concurrent access.

Key features to evaluate

When selecting a transactional data lake solution, focus on these criteria:

1. Transaction isolation: Snapshot isolation ensures readers never see partially written data.
2. Schema evolution: Add, drop, or rename fields without breaking downstream consumers.
3. Streaming and CDC integration: Native support for change data capture and incremental processing.
4. Automated table maintenance: Compaction, clustering, and file optimization reduce query latency.
5. Governance and access control: Centralized security policies across tables, schemas, and catalogs. Solutions like Unity Catalog provide this governance layer across the lakehouse.
6. Interoperability: Support for multiple query engines and open formats avoids lock-in.

How Lakebase unifies transactional and analytical workloads

Most transactional data lake architectures still require teams to stitch together separate operational databases, pipelines, and analytics engines. Lakebase stores OLTP data directly in the data lakehouse storage layer, where it is immediately accessible to analytics, governance, and AI.
This eliminates unnecessary integration work and simplifies operations. Together with Databricks Apps, Lakebase removes the friction of moving data between systems. Teams get one governed platform for building, deploying, and running applications.

Common use cases across industries

• Financial services: Combine structured transaction records with unstructured sources for risk modeling, using time travel for regulatory snapshots.
• Manufacturing and IoT: Ingest sensor data at scale with schema evolution handling changing device formats, enabling predictive maintenance.
• Healthcare: Maintain compliant patient records alongside research datasets with unified access control.
• AI agents and event-driven apps: Lakebase provides a transactional data layer for agentic workloads with persistent memory consistent with the broader lakehouse.
• Operational analytics: Activate lakehouse data through a transactional layer rather than replicating it into separate serving databases.

FAQs

What is transactional data lake storage and how does it work?

Transactional data lake storage adds ACID guarantees to files in cloud object storage. Open table formats maintain metadata separately, enabling reliable transactions and lower risk of corruption.

How do open table formats enable transactions on data lakes?

Delta Lake, Apache Iceberg, and Apache Hudi track commits in metadata logs that ensure atomic writes, snapshot isolation, and consistent reads, even during concurrent access.

Which cloud providers offer managed transactional data lake services?

AWS, Azure, and Google Cloud each offer transactional capabilities through object storage and open table format integrations. Databricks offers Lakebase, which stores OLTP data directly in the lakehouse storage layer.

What are the most widely adopted open table formats?

The three main formats are Delta Lake, Apache Iceberg, and Apache Hudi. All provide ACID transactions, time travel, and schema evolution.

How does transactional data lake storage handle schema evolution and time travel?

Open table formats version metadata with each commit. Schema evolution lets columns be added or renamed without rewriting data. Time travel queries previous versions by referencing historical metadata snapshots.

How do you implement transactional data lake storage on top of object storage?

Choose an open table format, configure a metastore or catalog, then write data through a compatible engine. The format manages transaction logs and file-level metadata automatically.

What is the difference between a traditional data lake and a transactional data lakehouse?

A traditional data lake can suffer from corruption, quality issues, and limited security. A lakehouse combines warehouse-grade structure with a lake's low-cost storage. Lakebase extends this by bringing OLTP data, application state, and operational logic onto the same governed storage layer.
Lakebase: where your app's data, models, and logic meet. Explore Lakebase to see how it unifies transactional and analytical workloads on a single governed platform.