Delta Lake Transformations in Azure Data Factory

Delta Lake, an open-source storage layer, enables ACID transactions in data lakes and supports diverse tasks like streaming and machine learning. Integrated with Azure Data Factory, it provides a scalable, fault-tolerant environment. ADF offers Delta Lake support through the Data Flow activity and with all Databricks operations. Here, we’ll look at using a Data Flow activity to build a Delta Lake.

Delta Lake and big data processing

Delta Lake leverages the capabilities of Apache Spark, providing a reliable and scalable solution for big data processing. Integrated with popular big data tools like Spark, Python, R, and SQL, it offers efficient management of structured and unstructured data within a unified platform. With features like schema enforcement, data versioning, and a unified API for batch and streaming data processing, Delta Lake streamlines the execution of complex big data analytics tasks, ensuring optimal performance and ease of integration with existing data processing pipelines.

In Delta Lake, big data processing can be done in several ways:

• Ingestion: Delta Lake can be used to ingest data from various sources like Kafka, Azure Event Hubs, and Azure Data Lake Storage into a unified data lake.

• Batch processing: They support batch processing of large volumes of data using tools like Apache Spark and Databricks.

• Stream processing: It provides a reliable and scalable way to process real-time streaming data with Apache Spark Structured Streaming.

• Machine learning: Delta Lake can be used as a storage layer for machine learning models to enable faster and more reliable model training and deployment.

Delta Lake transformations in Azure

Delta Lake Transformations in Azure Data Factory provide various activities to help transform and manipulate data in Delta Lake tables. These activities are designed to simplify and automate the process of building data pipelines for Delta Lake.