Both Azure Synapse and Azure Databricks are the leading data platforms, and both have contributed to improving data analytics and management for businesses. This blog will provide a review on both and help you learn Azure Databricks in comparison with Synapse. They do have common ground to some extent, but they are not the same thing. Databricks is pretty much run by Apache Spark, whereas SQL Data Warehouse manages synapse Analytics.

Azure Databricks is an analytics platform that is Apache Spark-based and is used to enhance the Microsoft Azure cloud services platform. Invented by Apache Spark’s founders, Databricks is integrated with Azure to provide a one-click setup, streamlined workflows, and a highly interactive workspace that allows for collaboration between data engineering, data science, and business intelligence personnel.

Azure Databricks enables you to query data with data lake and keep your data in azure data lake storage. You can handle both batch and streaming data. The data processed can then be utilized by data teams to generate data analytics. Azure Databricks supports three environments: SQL, data science and engineering, and Machine Learning.

Since Databricks presents itself more as a data lake than a data warehouse, it wins against Azure Synapse when it comes to use cases like ETL processes, data science, and Machine Learning. Azure Databricks is operating with advancements to the spark engine, cross-platform compatibility, and a mature workspace. Nonetheless, Azure Synapse Analytics convenes as a core integrated platform.

Azure Synapse is a vast analytics service that brings together enterprise data warehousing and Big Data analytics to enable data engineers and data scientists to manage as well as analyze data. It provides you with the freedom to query data on your terms, using any serverless on-demand or provisioned resources—at scale. Azure Synapse analytics brings these two worlds in conjunction with a cohesive experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Azure declared rebranding of the Azure SQL Data Warehouse into Azure Synapse Analytics. But then, this was not a new name for the same service. Azure has added many new functionalities to Azure Synapse to bridge the gap between big data and data warehousing technologies. For instance, the dynamic data masking feature prevents non-privileged users from authorizing the masked data and thus automatically protects sensitive data. Owing to how it facilitates data scientists, Azure Synapse has secured a place among the top big data analytics systems.

Azure Synapse manifests as a stronger data warehouse when compared to Azure Databricks, as it is closely integrated with Azure tools and enables thorough data transformation, cleansing, and overall governance.

• Limitless Scale
• Deeper Insights
• Real-Time Analytics Experience
• Advanced Security and Privacy

• Open-source Spark & Delta
• Synapse Pipelines
• On-demand Pool
• Provisioned Pool
• Synapse SQL
• Studio

Through the new features in Synapse, we see some comparable functional capabilities as in Databricks, which raises how Synapse compares to Databricks and when to use which.

• Business intelligence and machine learning integration with Microsoft Dynamics software.
• Cloud-native HTAP integration enables access to the freshest operational data.
• Advanced security features to protect real-time data.
• Unified analytics experience within the Azure Synapse studio.
• Workload optimization.

• Azure Databricks & Azure Synapse for Ad-Hoc Data Lake Discovery
• Azure Synapse for SQL Analyses & Data Warehousing
• Azure Databricks for Machine Learning Development
• Azure Databricks for Real-time transformations
• Azure Synapse for Reporting and Self-Service BI

Azure Synapse Analytics enables cost-effective data management and analytics by leveraging the cloud’s power and scale: Azure Synapse will break the silos that exist today and allows insights for all by integrating with Power BI and Azure Machine Learning and AI.

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