Making the right technology decisions early on requires deep knowledge of both platform intricacies and one’s capabilities to cross check strengths and limitations. The right partner can help you navigate those questions but also provide systems, accelerators, and solutions that can be scaled across future needs as well. From reusability of pipelines, automated data migration for multiple use cases to other integrations, a solid foundation for a Modern Data Platform allows you to build on depending on changing business needs. The current economic uncertainties make investment decisions even more complex and increasing tech ROI even more important.
Databricks is optimized for data science and machine learning workflows, with features such as collaborative notebooks, advanced analytics tools, and support for popular machine learning frameworks. It is ideal for organizations that need to build and deploy machine learning models or conduct advanced analytics.
Azure Synapse is designed for big data processing and data warehousing, with features such as powerful query engines, flexible data storage options, and support for SQL-based analytics. It is ideal for organizations that need to process large volumes of structured and unstructured data.
This the table below covers key differences between the two as you start to explore the unique strengths of these tools and how they fit into your current process.
| Category | Azure Synapse | Databricks |
| Processing & Performance | Azure Synapse includes open-source Spark and built-in support for .NET applications for big data analytics and machine learning tasks. | Databricks leverages an optimized version of Spark that allows for the use of GPU clusters, resulting in over 50x better performance compared to regular Spark. |
| Architecture | Azure Synapse uses a 3-component architecture consisting of Storage, Processing, and Visualization. The storage layer utilizes Azure Data Lake, while the visualization layer uses BI tools. | Databricks employs a lakehouse architecture through the Delta Lake, which combines the strengths of a data warehouse and lake to create a robust data solution. |
| Ease of Use | Azure Synapse has an easy-to-use interface that is suitable for users familiar with SQL and data analysis. There is little need for configuration and setups. | Databricks utilizes many 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 Azure Synapse supports AzureML, there is limited Git support for versioning during the experimental stages of ML model development. This can cause friction during collaboration. | Databricks offers ML workflows with GPU-enabled clusters and robust Git support, making the ML process more efficient and collaborative. |
| Notebooks and Versioning | Azure Synapse uses the Nteract notebook without automatic versioning. Changes must be saved for other notebook co-authors to view changes. | Databricks allows for notebooks and automatic versioning. Any changes made by co-authors are automatically saved. |
| Security | Azure Synapse employs Microsoft Purview, encryption, and masking to ensure security, prevent injection attacks, and control data access. | Databricks ensures proper governance and security through customer keys, encryption, and role-based access control. |
| Price | Azure Synapse Analytics utilizes a Pay-As-You-Go (PAYG) pricing model, allowing its users to only pay for what they use. | Databricks also uses a PAYG pricing 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. | Databricks provides support for Python, R, and SQL. |
| Integrations | Azure Synapse comes with Azure tools such as 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. |
Pros of Databricks:
- Databricks offers a collaborative workspace that allows teams to easily work together on data analysis and machine learning projects.
- Databricks provides an integrated platform for data engineering, data science, and business analytics.
- Databricks has native integrations with popular data processing and storage technologies such as Spark, Delta Lake, and MLflow.
- Databricks provides a user-friendly interface for creating and managing machine learning models.
- Databricks supports multiple programming languages such as Python, R, and SQL.
Cons of Databricks:
- Databricks can be expensive for small teams or individuals.
- Databricks requires some level of technical expertise to set up and use effectively.
Pros of Synapse:
- Synapse offers a fully integrated analytics service that includes big data processing, data warehousing, and data integration.
- Synapse integrates with other Microsoft Azure services, such as Power BI and Azure Machine Learning, to provide a complete end-to-end data analytics solution.
- Synapse has a powerful data ingestion capability that allows users to load data from multiple sources.
- Synapse offers a managed service that eliminates the need for users to manage the underlying infrastructure.
- Synapse provides a collaborative workspace for data analysts and data scientists to work together on projects.
Cons of Synapse:
- Synapse is tightly integrated with Microsoft Azure and may not be suitable for organizations that use other cloud providers.
- Synapse’s query performance may not be as fast as other cloud data warehouses in certain scenarios.
Ultimately, the choice between Databricks and Synapse depends on your specific needs and priorities. If you’re looking for a collaborative workspace for data analysis and machine learning, Databricks may be a better choice. If you need a fully integrated analytics service that includes big data processing, data warehousing, and data integration, Synapse may be a better fit.
Regardless of which direction is determined to be the best fit or how far along you happen to be in that journey, proSkale‘s flexible delivery model helps you navigate the process and achieve a scalable Modern Data Platform. From narrow automation solutions for migrations, and cost monitoring to delivering an entire Modern Data Platform see how we can partner for immediate ROI.
