Aspect | OneLake | Delta Lake | Data Lake |
Definition | A centralized storage system within Microsoft Fabric that acts as a unified data lake for all workloads. | An open-source storage layer built on top of data lakes that provides ACID transactions and schema enforcement. | A centralized repository designed to store large volumes of structured, semi-structured, and unstructured data. |
Primary Use Case | Unified storage for structured, semi-structured, and unstructured data, enabling seamless access across Fabric workloads. | Optimized for handling large-scale data with capabilities like versioning, updates, and transaction control. | Long-term storage and management of raw data for analytics, reporting, and AI/ML use cases. |
Architecture | A service-layer abstraction that consolidates data from various Fabric workloads into a single logical layer. | An extension of the data lake concept that uses Parquet files and a transaction log for reliability and consistency. | Typically built on cloud storage systems like Azure Data Lake Storage (ADLS), Amazon S3, or Google Cloud Storage. |
ACID Transactions | Not directly responsible for ACID compliance but supports services (e.g., Lakehouse) that may implement Delta Lake for transactions. | Fully supports ACID transactions, enabling reliable updates, inserts, and deletes on large datasets. | Does not natively support ACID transactions unless enhanced with Delta Lake or similar frameworks. |
Governance & Security | Built-in integration with Fabric security, compliance, and governance frameworks for centralized control. | Relies on the underlying storage system's security; additional layers can be applied via tools or platforms. | Offers basic security features like IAM roles, encryption, and network policies; governance is often added via external tools. |
Data Format | Stores data in the Delta Lake format for interoperability across Fabric services. | Uses Parquet files with an additional transaction log layer to support Delta Lake functionality. | Supports multiple formats, including CSV, JSON, Avro, ORC, and Parquet, but without transaction logs. |
Scalability | Highly scalable and designed for enterprise-level integration across analytics workloads. | Scalable for big data analytics and machine learning workloads, with specific optimizations for large datasets. | Scalable, but performance depends on how well it is structured and managed (e.g., folder structures, metadata). |
Interoperability | Seamlessly integrates with all Fabric components, including Lakehouse, Dataflows, Warehouse, etc. | Compatible with various data processing engines like Apache Spark, Databricks, and Microsoft Fabric. | Can integrate with various tools and frameworks (e.g., Spark, Hadoop, Presto, Athena), but requires additional setup. |
Versioning | Supports Delta Lake versioning through Fabric services (e.g., Lakehouse), enabling time travel and history tracking. | Provides built-in versioning, allowing users to query historical snapshots of data. | Does not natively support versioning unless extended with Delta Lake or other technologies. |
Storage Abstraction | Logical data storage system that abstracts physical storage (e.g., Azure Blob, ADLS). | Built on physical storage like Azure Data Lake, Amazon S3, or HDFS with a transactional layer. | A raw storage repository for data; lacks abstraction and relies on physical storage solutions like Azure Blob or S3. |
Integration with Microsoft Fabric | Core storage layer for Fabric workloads (Lakehouse, Warehouse, Eventhouse, etc.), ensuring consistent data access. | Used within Fabric services (e.g., Lakehouse) for managing data with transactional reliability. | Can be used with Fabric, but without additional frameworks (like Delta Lake), lacks advanced functionality. |
