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.
What Is Azure Synapse?
Azure Synapse offers data integration, big data analytics, and enterprise data warehousing services through its unified limitless analytics platform. Synapse offers sub-second analytical queries on large volumes of data on a petabyte scale and data lake exploration with T-SQL. In addition, its code-free ETL/ELT integration makes it accessible to individuals with little technical expertise, improving fast time to analytics for BI workloads.
Azure Synapse at a Glance
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.
What Is Databricks?
Databricks is a lakehouse platform with the best qualities of a data lake and warehouse to produce a cost-effective data management and governance solution. As a result, Databricks helps organizations build a single, continuous data system instead of managing multiple data architectures simultaneously. In addition, Databricks uses Delta Lake architecture, an open-source storage format, to confer data governance, reliability, and ACID-compliance transactions on a data lake.
Databricks at a Glance
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.
High-Level Comparison: Azure Synapse vs. Databricks
| Category | Azure Synapse | Databricks |
| Processing & Performance | Contains open-source Spark and built-in support for .NET applications used for performing big data analytics and ML tasks. | Uses the optimized Spark version, allowing use of GPU clusters that provide over 50x performance as against the regular Spark performance. |
| Architecture | 3-component architecture; Storage, processing, and Visualization. Storage utilizes Azure Data Lake; the Visualization layer utilizes BI tools. | Uses a lakehouse architecture via the Delta lake. The lakehouse architecture combines the best features of the data warehouse and lake to produce a robust data solution. |
| Ease of Use | Easy-to-use interface suitable for users familiar with SQL and data analysis. There is little need for configuration and setups. | Uses a lot of open-source ML libraries and requires familiarity with Apache tools. Databricks is geared towards a more technical audience with experience managing clusters and configuration updates. |
| Machine Learning Development | Although it contains support for AzureML, there is limited Git support for versioning during the experimental stages of ML model development. This causes friction during collaboration. | Databricks ML workflows contain GPU-enabled clusters and robust Git support for a more efficient and collaborative ML process. |
| Notebooks and Versioning | Uses the Nteract notebook without automatic versioning. Changes must be saved for other notebook co-authors to view changes. | Allows notebooks and automatic versioning. Any changes made by co-authors are automatically saved. |
| Security | Uses Microsoft Purview, encryption, and masking to ensure security, prevent injection attacks, and control data access. | Databricks ensures proper governance and security via customer keys, encryption, and role-based access control. |
| Price | Azure Synapse analytics is on a Pay-As-You-Go (PAYG) pricing model, allowing its users to only pay for what they use. | Pricing is also a PAYG model based on the total consumed Databricks Units (DBU). Customers can get discounts off the standard on-demand price by committing to certain usage periods. |
| Programming Languages Support | Azure Synapse supports Python, Java, Scala, and SQL. | Support is available for Python, R, and SQL. |
| Integrations | Azure Synapse comes with Azure tools like Purview for governance, Data Factory for ETL movements, and Power BI for analytics. Additionally, it works with Spark in Spark pools to run notebooks. | Databricks requires integration with third-party libraries and other API configurations to achieve effective governance and security. For instance, Databricks provides customer-managed keys for users via the AWS Key Management Service (KMS) and Azure Key Vault for Azure Databricks. |
Key Distinctions: Features, Architecture, and Integrations
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.
Azure Databricks vs. Databricks
Azure Databricks and Databricks are robust data solutions for serving data organizations today. Databricks is a cloud data lakehouse platform providing a unified solution for workloads like analytics, machine learning, and data science. Azure Databricks is Databricks’ integrated solution with the Azure cloud platform, providing data scientists, ML engineers, and analysts easy access to a workspace for performing data engineering, analysis, and ML tasks. In addition, this integration grants Databricks users complete access to services in the Azure portal. Handling Databricks can be technical for users with limited Spark knowledge because it requires numerous integrations, configurations, and maintenance to perform optimally. The integration with Azure provides a single platform with access to Azure services like Data Factory, Azure Synapse, and Azure Data Lake Storage, creating an end-end solution for your analytics and ML workloads.
StreamSets: A Partner for Either Choice
Changing business needs may mean changing data solutions. Whether organizations choose Databricks or Azure Synapse, StreamSets provides a platform that supports integrating these solutions with multiple data sources to develop reusable pipelines. StreamSets offers broad compatibility, allowing organizations to connect and integrate with numerous data platforms like Databricks and Azure Synapse to pull data from multiple sources and create pipelines for your data needs. For instance, StreamSets supports Azure Synapse SQL, which helps load data into Azure synapse, and uses the StreamSets Databricks Executor to perform ML tasks in Azure Databricks. The reusability of StreamSet pipelines also enables organizations to reuse a single pipeline fragment for multiple use cases without developing one from scratch.
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