Are you struggling to generate critical business insights from big data? It can be challenging for organizations as most of the data is unstructured. It requires domain-specific expertise and precise monitoring to analyze such data using traditional methods. It is where big data analytics platforms like Azure Synapse and Databricks Workspace come in. They offer functionalities like data warehousing, cloud-based data engineering, ETL pipelines, and machine learning workflows. Though both the platforms come with similar features, they have some stark differences too.
In this article, we will highlight the features of Databricks vs. Azure Synapse while attempting to bring out their distinctions across use cases.
Formerly known as Azure SQL Data Warehouse, the Azure Synapse is a limitless analytics service that comes with big data analytics, data integration, and enterprise data warehousing. Here, users have the freedom to use Azure Synapse serverless or with dedicated options.
Databricks is a data processing engine built upon Apache Spark; it allows for distribution across nodes. It is a centralized platform that offers a unique UI to its users. If you are a data-science-driven organization working with several open sources and machine libraries, Databricks SQL analytics could be your go-to option.
Azure Synapse offers end-to-end analytics solutions; it brings big data analytics and enterprise data warehouse into a unified platform. Whereas Databricks offers more than big data analytics; it lets users build complex and scalable ML products. For better understanding, we have collated a comparative study between Azure Synapse and Databricks based on use cases -
Synapse serves as a warehouse tool as well as an interface tool with both Spark and SQL engines. Databricks is a notebook tool that supports spark-based data engineering. Also, Synapse works with open-source Delta Lake whereas Databricks offers some additional optimizations.
Both the platforms are powered by Apache Spark. While Synapse has an open-source version, Databricks use an optimized version that offers increased performance. With Databricks, users can process data faster and have higher concurrence.
Both Synapse and Databricks support Notebooks. In the case of Synapse, one user must save the notebook before another can view the changes. Databricks supports real-time co-authoring with automated version control.
Both come with in-built support for machine learning workflows. But unlike in Synapse, Databricks provides GPU-based clusters and strict version control using Git.
The Azure Synapse architecture consists of data processing, data storage, and visualization layers. It integrates power BI, SQL engine, and big data apps into one platform. But the Databricks architecture is not entirely a data warehouse.
For creating a project in Synapse, one can select a Data Lake as the primary data source. After mounting the Data Lake on Azure Synapse, the users can use Notebooks or Scripts for data analysis. In the case of Databricks, there is no need for mounting of Data Lakes. Additionally, it provides users with reliability, security, and performance enhancement.
Deciding between Azure Synapse and Databricks can be difficult. One cannot solely look at the pros and cons, given that both are great options in their respective ways. It you rely heavily on open-source tools, Databricks can be a more feasible choice. But the factor you should consider is whether your organization is already well-versed in the Azure platform. If so, then going for Azure Synapse would be a more productive choice.
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