Critical Differences: Azure Synapse vs Databricks

Azure Synapse and Azure Databricks are both robust Azure analytics solutions within the Microsoft ecosystem, but they have different strengths and use cases.

Azure Synapse is a cloud-based analytics service that combines big data and data warehousing. It allows users to analyze large volumes of data using SQL and integrate with Power BI, Azure Machine Learning, and other Azure services. Synapse provides a unified experience for data engineers, scientists, and business analysts to collaborate and access data. It also has built-in security features to help protect sensitive data.

Azure Databricks is an Apache Spark-based analytics platform that offers a collaborative workspace for data engineers, data scientists, and machine learning engineers. It allows users to build and train machine learning models at scale and provides integration with other Azure services like Azure Machine Learning, Azure Data Lake Storage, and Azure SQL Database. Databricks offers an interactive notebook experience, which allows users to explore data and collaborate on projects in real time.

When choosing between Azure Synapse and Azure Databricks, you must consider your organization's specific needs. Azure Synapse may be the better choice if you need to integrate big data and data warehousing and work with SQL. On the other hand, if you need a platform to build and train machine learning models, and collaborate on data science projects, then Azure Databricks may be the better choice.

While both services are designed for big data analytics and offer overlapping features, they differ in their target audiences and use cases. Azure Synapse is better suited for data warehousing, business intelligence, and enterprise-level analytics needs. At the same time, Azure Databricks is ideal for data scientists and engineers who need to perform advanced data analytics and machine learning.

Azure Synapse vs. Databricks: What is the Difference?

Azure Synapse enables the successful integration of analytical services to bring enterprise data warehouse and big data analytics onto a single platform. While Databricks, along with big data analytics, also allows users to build complex ML products at scale.

Listed below are a few key differences between Azure Synapse vs. Databricks:

• Data Processing
• Smart Notebooks
• Developer Experience
• Architecture
• Leveraging Lake
• Machine Learning Development

Let's discuss them in detail:

Data Processing: Apache Spark powers both Synapse and Databricks. An open-source Spark version offers built-in support for .NET applications with 50 times increased performance. And, with optimized Apache Spark support, Databricks allows users to choose GPU-enabled clusters with faster data processing and higher data concurrency.

Smart Notebooks: Both Azure Synapse and Databricks support Notebooks. It enables developers to perform quick experiments. Synapse offers co-author a notebook though it is with the condition that a user has to save the notebook before another user can realize the changes. Unfortunately, it does not have automated version control. However, Databricks Notebooks supports automated version control with real-time co-authoring.

Developer Experience: With Synapse, Developers can Spark the environment only through Synapse Studio, while there's no support for any other local IDE (Integrated Development Environment). Further, it also lacks Git integration with Synapse Studio Notebooks. And Databricks enhances developer experience with Databricks UI and Databricks Connect. Additionally, it remotely connects via Visual Studio or Pycharm within Databricks.

Architecture: Azure Synapse's architecture comprises - Storage, Processing, and Visualization layers. It is with a traditional SQL engine and a Spark engine for Business Intelligence and Big Data Processing applications. While in contrast, Databricks architecture is not entirely a Data Warehouse. It accompanies a LakeHouse architecture that combines the best elements of Data Lakes and Data Warehouses for metadata management and data governance

Leveraging Lake: When creating a project in Synapse, you can choose the Data Lake as the primary data source. And once it is mounted on Synapse, it allows the users to query from Notebooks or Scripts and analyze their unstructured data. However, Databricks does not require mounting Data Lakes. Rather it enables users to leverage delta lakes by providing an open format storage layer. It delivers reliability, security, and performance on existing data lakes.

Machine Learning Development: Azure Synapse offers built-in support for Azure ML to finalize Machine Learning workflows. However, there's no support for Git and other collaborative environments. Databricks incorporates optimized ML workflows that provide GPU-enabled clusters and facilitate tight version control using Git.

In summary, Azure Synapse and Azure Databricks are both robust big data analytics solutions provided by Microsoft Azure, but their focus and capabilities differ. Businesses should consider their specific use case and target audience to determine which solution would be the best fit.