Azure Synapse vs Databricks: Choosing the Right Big Data Platform

In the rapidly evolving landscape of big data analytics, organizations are faced with the challenge of selecting the right platform that aligns with their data processing needs.

Two prominent contenders in this space are Azure Synapse and Databricks. In this article, we will delve into a comprehensive comparison of these platforms.

What is Azure Synapse?

1. Azure Synapse Analytics, SQL Data Warehouse a Microsoft’s integrated analytics service.
2. It bridges the gap between data warehousing and big data analytics.
3. Azure Synapse allows to analysis of vast amounts of data in real time.
4. Azure Synapse provides unified and real-time analytics, offers integration with ADLs, PowerBI, and other services of Microsoft, and based on the workload can up or down resources(scalability).

What is Databricks:

1. Databricks powered by Apache Spark is a cloud-based, open, and collaborative environment for data engineering, data science, and machine learning.
2. It offers a unified analytics platform, and collaborative workspace for data engineering, data science, and machine learning.
3. Includes machine learning capabilities and based on the workload can up or down resources(scalability).

Azure Synapse vs Databricks:

1. Synapse has an open-source Spark version with built-in support for .NET, whereas Databricks has an optimized version of Spark which offers increased performance and this allows users to select GPU-enabled clusters that will process data faster and have a higher data concurrency.
2. Synapse successfully integrates analytical services to bring enterprise data warehouse and big data analytics into a single platform, whereas, on the other hand, Databricks not only does big data analytics but also allows users to build complex ML products.
3. Databricks uses a group of magic commands which are known as DBUtils but Microsoft has invested time into Synapse to bring out the equivalent known as MSSparkUtils.

When to use Databricks and When to use Synapse

Given that there are so many new features in Synapse now and lots of similar functionalities between the two it raises the question about when to use which.

• Both can access the data from Data Lake however you need to mount the Data Lake in Databricks first whereas this is not needed in Synapse.
• They both use Spark, but Synapse is open source and tends to be on a different version than Databricks whereas Databricks has a data processing engine built on a version of Spark offering high performance.
• Notebooks are used in both with the main difference being that Databricks allows co-authoring in real time and Synapse requires the notebook to be saved before the other person can see the changes.
• Synapse has a traditional SQL engine and will feel familiar to the traditional BI developer, but it does have a spark engine that will fit the data scientists and analysts. It is a Data Warehouse and an interface tool. Databricks on the other hand is not a Data Warehouse tool but a spark-based notebook tool and has a focus on spark.

Both Azure Synapse and Databricks are robust platforms with unique strengths. The choice between them depends on the organization’s specific needs, existing infrastructure, and the nature of your data analytics and processing tasks.

Note:- Databricks is a lakehouse platform that combines the best features of data lakes and data warehouses, such as scalability, reliability, and governance2.

Synapse is an analytics platform that integrates data integration, data warehousing, and big data analytics into a single service3.

Other useful resource:

Differences Between Azure Synapse and Databricks

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