Microsoft Fabric

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Microsoft Fabric Experiences

Data Factory

Synapse Data Engineering

Synapse Data Science

Synapse Data Warehouse

Synapse Real Time Analytics

Power BI

Direct Lake mode in Power BI allows read Delta tables directly from the OneLake with the similar performance of Import mode but without having to copy them. When Delta tables are updated in the OneLake, the changes are automatically reflected in corresponding Power BI reports.

Data Activator

OneLake

Lakehouse

Tables in a Microsoft Fabric lakehouse are based on the Linux foundation Delta Lake table format, commonly used in Apache Spark. Delta Lake is an open-source storage layer for Spark that enables relational database capabilities for batch and streaming data. 

The benefits of using Delta tables include:

  • Relational tables that support querying and data modification. With Apache Spark, you can store data in Delta tables that support CRUD (create, read, update, and delete) operations. In other words, you can select, insert, update, and delete rows of data in the same way you would in a relational database system.
  • Support for ACID transactions. Relational databases are designed to support transactional data modifications that provide atomicity (transactions complete as a single unit of work), consistency (transactions leave the database in a consistent state), isolation (in-process transactions can’t interfere with one another), and durability (when a transaction completes, the changes it made are persisted). Delta Lake brings this same transactional support to Spark by implementing a transaction log and enforcing serializable isolation for concurrent operations.
  • Data versioning and time travel. Because all transactions are logged in the transaction log, you can track multiple versions of each table row and even use the time travel feature to retrieve a previous version of a row in a query.
  • Support for batch and streaming data. While most relational databases include tables that store static data, Spark includes native support for streaming data through the Spark Structured Streaming API. Delta Lake tables can be used as both sinks (destinations) and sources for streaming data.
  • Standard formats and interoperability. The underlying data for Delta tables is stored in Parquet format, which is commonly used in data lake ingestion pipelines. Additionally, you can use the SQL Endpoint for the Microsoft Fabric lakehouse to query Delta tables in SQL.

Azure Synapse Link

Usage with Dataverse: The Azure Synapse Link service enables continuous replication of Dataverse table data to either an Azure Data Lake Storage Gen 2 account or an Azure Synapse Workspace. You can then use it to run analytics such as Power BI reporting, Microsoft Azure Machine Learning, Data Warehousing, and other integration scenarios.

Reference Materials

Microsoft Learn: