Snowflake OR Databricks?
Subra Venkatesh
Sr Director - Data, Analytics & AI | Data Engineering & Platforms | ex-Salesforce | ex-PayPal
While both Snowflake and Databricks offer data processing and analytics capabilities, each has its own unique strengths. Some argue that it is not even a fair comparison (apples to oranges). There are several articles on this but the gist I could take away was:
Snowflake :
Separation of storage and compute: ability to separate storage and compute resources allows for more flexible scaling and can result in cost savings. Databricks requires users to provision a cluster with both compute and storage resources
Optimized for query performance: (structured and semi-structured data). Snowflake's unique architecture allows for parallel query processing across multiple virtual warehouses
Extensive ecosystem integrations: Snowflake integrates with a much wider range of third-party tools and services, including popular business intelligence and data integration platforms
Vendor Lock-In: Yes (Proprietary data format/ storage)
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Databricks :
ML capabilities: specifically designed for ML workloads, with built-in support for popular frameworks like TensorFlow, Keras, and Scikit-learn. Also provides powerful tools for data preparation, model training, and model deployment, making it a popular choice for organizations focused on ML
Unstructured data processing: Databricks is well-suited for processing unstructured data like images, audio, and video, thanks to its support for popular big data processing frameworks like Apache Spark. While Snowflake is optimized for querying structured and semi-structured data, it may not be the best choice for processing unstructured data
Collaborative workspace: provides a collaborative workspace for teams to work together on data analysis and ML projects. This allows team members to easily share code, data, and insights, improving collaboration and productivity
Job scheduling & workflow management: Databricks provides built-in job scheduling and workflow management capabilities, allowing users to automate and orchestrate complex data processing and ML tasks. While Snowflake offers integrations with popular tools, it does not provide built-in capabilities
Vendor Lock-In: No
What is your comment/ experience?
Analytics, AI & Cloud Data Architect | Solutions Whisperer | Tech Writer
2 年Few things to note. 1. Vendor lock is a myth more than a reality. Snowflake supports iceberg tables via Parquet format stored in customer storage accounts. Iceberg format is full opensource meaning it is managed by community & all features are available to anyone. This allows other query engines like spark to query the data at the same time. For Delta, it has 2 flavors, the delta format dbx uses internally is proprietary to them allowing it to perform better for SQL with proprietary Photon SQL engine which only works in databricks. (Even though they keep announcing they made Delta opensource; the roadmap & development is still largely only dbx)You simply cant move to another Spark vendor and keep using your lake the same way as most of your production SQL workloads would only work with Photon & DBX delta and run poorly on Opendsource Delta & Spark. You will have to go through migration just like any other product. 2. For ML, Snowpark dataframes and support for native Python, Java & Scala functions allow you to run the same ML funtions and 3rd party libraries but actually faster on Snowflake alongside SQL on same clusters. So ML workloads will be more performant on Snowflake. Just my 2 cents