Data lakehouses are robust data platforms that unify the best features of data warehouses and lakes to produce a rich data solution. One such platform is Databricks. Built using open standards, it helps unify your storage, analytics, and Artificial Intelligence (AI) workloads. Databricks integrates with cloud platforms; for example, Azure Databricks is an integration of the Databricks platform and Azure, which grants Databricks access to the Azure portal, helping accelerate digital innovation while boosting collaboration between data scientists and engineers. One of the services accessible via Azure Databricks is Azure Synapse, an analytics solution that blends data integration, data warehousing, and analytics into a single platform for organizations.

Let’s explore the difference between Azure Synapse, a limitless analytics platform, and Databricks, a data lakehouse.

Azure synapse offers the following key features:

• Integrated data service: Azure Synapse provides a fully integrated cloud data service offering big data analytics, integration, and enterprise data warehousing. This makes it a staple for data-driven organizations that deal with ingestion, transformation, processing, and data analysis for BI or analytics purposes.
• Handles both structured and unstructured data: Azure Synapse handles various forms of data, including log, telemetry, graph, social media, IoT, and others, which is crucial for addressing the varied nature of data faced by big data organizations.
• Data analytics at scale: Synapse offers effective workload management via workload classification to help efficiently manage resources for large Spark workloads. Additionally, the autoscale option allows users to scale workloads seamlessly, and autopause and resume can help save resources when your cluster is not in use.
• Security: Azure ensures quality data security and governance for its data workloads in several ways. These security measures include dynamic data masking, managed virtual networks, encryption, and column and row-level security.
• Support for multiple programming languages: Azure Synapse is compatible with various languages like Python, Java, SQL, and Spark SQL, making it more accessible to a broader range of users.
  

The following features characterize Databricks:

• Open: Databricks is built using Delta Lake, which uses the Parquet open-source storage format, making it flexible and platform-agnostic for integrating multiple cloud or on-premises platforms.
• Simplified governance: Databricks has a unified, single architecture that uses the best data warehouses and data lake features. This streamlined architecture means reduced data governance workloads instead of the multiple workloads required for maintaining the data warehouse and data lake different architectures simultaneously.
• Reliable data management: The various upstream and downstream processes involved with ELT/ETL movement among multiple architectures create more room for errors, reducing data quality and integrity and increasing management costs and time. However, the continuous architecture employed by Databricks reduces such movement, reducing the risk of errors/bugs and significantly reducing workload costs.
  

Architecture

Azure Synapse utilizes a 3-component architecture; Data storage, processing, and visualization in a single, unified platform. On the other hand, Databricks utilizes a lakehouse architecture that enables the best data warehouse and data lake features into one continuous platform.

Availability and pricing

Azure synapse utilizes a PAYG option where users are charged based on the resources used. These resources vary depending on the level of data exploration, warehousing, Spark pool, integration, and storage needs, like the number of TBs stored and processed, data movement, runtime, and cores used in data flow execution and debugging. The number of parameters used to determine your Azure Synapse analytics is large and can be complex. However, organizations can estimate their billing costs thanks to the Azure pricing calculator. 

For Databricks, a free plan is available but comes with limited features. Databricks also uses a PAYG pricing based on usage costs and charges per second using Databricks Unit (DBU). Organizations can get sizeable discounts when they commit to a specific usage level. A single DBU depends on workload factors like the amount of processed data, memory, vCPU power, region, and Databricks services used.

Machine learning

Most ML environments rely on versioning systems like Git to collaborate effectively to create a seamless workflow. Although Azure incorporates AzureML into its ML workflow, collaborative efforts between team members may encounter friction because of its limited Git support. On the other hand, Databricks’ robust Git support and GPU-enabled clusters enable more collaboration and tracking of ML models via versioning.

Notebooks and versioning

Notebooks are crucial for the iterative process involved with model development. Both Databricks and Azure Synapse offer notebooks for this collaborative and highly-experimental process. However, for Azure Synapse, which uses the Nteract notebook, there is no automatic versioning between notebook co-authors. Instead, someone must save the changes before they become available for fellow co-authors. Databricks, however, automatically saves changes made by co-authors working on the same notebook.

Security

Azure Synapse and Databricks ensure the safety and protection of data via different means. Databricks uses customer-managed keys, encryption, PrivateLink, firewall protection, and role-based access control to mitigate and control data access and leaks. Azure Synapse uses its integration with Microsoft Purview, dynamic data masking, encryption, and column and row-level security to manage network and data access and injection attacks and ensure effective data governance.

Capabilities and performance

Azure Synapse comes with the open-source Spark version and support for .NET applications; Databricks uses the optimized version of Apache Spark, allowing its users to use GPU-enabled clusters for their ML workloads, offering much better performance than Azure. Hence, workloads requiring fast training and inference on performing data will benefit from using Databricks.