Azure Synapse vs Delta Lake: What are the differences?

Introduction

Azure Synapse and Delta Lake are two powerful technologies used for big data processing and analytics. While they share some similarities, they also have key differences that set them apart. In this article, we will explore six key differences between Azure Synapse and Delta Lake.

1. Data Integration and Analytics Capabilities: Azure Synapse is a fully managed analytics service that brings together big data and data warehousing into one unified platform. It offers capabilities for data ingestion, data preparation, data warehousing, big data analytics, and machine learning. On the other hand, Delta Lake is an open-source storage layer that provides ACID transactions, scalable metadata management, and data quality guarantees. It enables data pipelines for data lake use cases and provides advanced data reliability features such as schema enforcement and time travel.

2. Data Processing Paradigm: Azure Synapse supports both batch and real-time data processing. It provides capabilities for running SQL queries, Spark jobs, and streaming analytics. Delta Lake, on the other hand, is optimized for batch processing and provides built-in support for Apache Spark. It leverages the power of Spark's distributed processing to handle large datasets efficiently.

3. Data Lake Storage Integration: Azure Synapse allows you to ingest and analyze data from various sources, including Azure Data Lake Storage, Azure Blob Storage, and on-premises data sources. It provides seamless integration with Azure Data Lake Storage and offers capabilities for data lake exploration and management. Delta Lake, on the other hand, is a storage layer that can be integrated with Azure Data Lake Storage Gen2. It provides enhanced reliability and performance optimizations for data stored in Azure Data Lake.

4. Data Lake Management Capabilities: Azure Synapse includes built-in capabilities for data lake management, such as data catalog, data wrangling, data governance, and data lineage. It provides a unified experience for managing data assets stored in Azure Data Lake. Delta Lake, on the other hand, focuses on providing reliability and advanced features for data lake use cases. It offers features like data versioning, schema evolution, and data quality checks.

5. Scalability and Performance: Azure Synapse is designed for scalability and high-performance analytics. It can handle large datasets and offers capabilities for elastic scaling to meet the demand of heavy workloads. Delta Lake, on the other hand, provides scalability through Apache Spark's distributed processing model. It can handle petabytes of data and is optimized for efficient data processing.

6. Pricing Model: Azure Synapse follows a pay-as-you-go pricing model, where you pay for the resources used during data processing and analytics. The cost is based on factors such as data storage, data processing, and data transfer. Delta Lake, on the other hand, is an open-source technology and can be used for free. However, if you choose to use a managed service like Azure Databricks for running your Delta Lake workloads, you will need to pay for the Azure Databricks resources.

In summary, Azure Synapse and Delta Lake are both powerful technologies for big data processing and analytics. However, Azure Synapse is a fully managed analytics service that provides integration with various data sources and offers comprehensive data management capabilities. On the other hand, Delta Lake is an open-source storage layer optimized for batch processing and provides advanced data reliability features.