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Author : Ashutosh Gupta
Databricks and Snowflake are two of the leading vendors in a rapidly evolving cloud data platform landscape. This blog post aims to provide a high-level comparison of Databricks and Snowflake by comparing their capabilities, especially as it applies to the financial services industry. Financial services firms deal with enormous (and rapidly increasing) volumes of data from various sources such as customer profiles, transactions, credit ratings, loans, etc. Efficient data management is crucial for reducing operational costs, ensuring data consistency and improving security, and enhancing customer experience. In addition, as banks start incorporating data science, machine learning, and AI in their core operations, the data architecture needs to be flexible and efficient to accommodate these new demands.
Before comparing the capabilities of Databricks and Snowflake, it is useful to understand their respective origins. Both firms started 12-15 years ago, so they are both still evolving. Snowflake started as a cloud-based data warehousing platform that enables the storage, processing, and exploration of data – its primary focus was on business intelligence, storing and querying data at scale, though more recently they have begun to offer data science on the cloud. Databricks on the other hand started with data engineering and data science – incorporating the Apache Spark framework for big data workloads and MLflow for machine learning lifecycles. Databricks then expanded into cloud data warehousing, leveraging its storage framework based on the open-source Delta Lake project.
There is now a significant overlap in capabilities between Databricks and Snowflake– just the starting points and therefore, the key strengths and focus areas are different. Databricks has been more focused on advanced analytics and handling complex data processing tasks, whereas Snowflake is optimized for storing and analyzing structured data, with a strong focus on ease of use. It is our view that it will be easier for Databricks to move from AI / Analytics workloads as their specialty to offering cloud data warehousing than trying to move in the other direction, which is what Snowflake has been trying to do.
In terms of cost, Databricks can be harder to forecast as pricing is based on compute units whereas Snowflake is typically per-second billing, therefore more predictable. Overall, Databricks is more cost-effective for long-running big data jobs whereas Snowflake is cost-effective for short, ad hoc SQL queries.
Finally, when it comes to choosing between Databricks and Snowflake, a lot will depend on the individual firm’s data strategy, usage patterns, data needs and volumes, and workloads. Snowflake is a solid choice for standard data transformation and analysis, for example, credit risk dashboards, regulatory reports, customer 360, etc. On the other hand, many firms choose Databricks for its advanced capabilities in streaming, ML, AI, and data science workloads, especially for raw unstructured data and Spark support for multiple languages – e.g. fraud detection using real-time streaming data, sentiment analysis, risk modeling, etc.