I am currently architecting a data platform for a retail giant. We are torn between Snowflake’s ease of use and Databricks' flexibility with Spark and Delta Lake. Given the need for real-time streaming and heavy ML integration, which ecosystem offers better long-term scalability and cost-efficiency for a 50TB+ environment?
3 answers
Snowflake is exceptional if your team primarily consists of SQL-proficient analysts who need a near-zero management warehouse. However, for a 50TB+ environment requiring heavy Machine Learning and real-time streaming, Databricks often wins out due to its native integration with Apache Spark and MLflow. While Snowflake has introduced Snowpark to handle Python/Java, Databricks was built for complex data science workflows from the ground up. In terms of cost, Databricks can be cheaper if you manage your clusters well, but Snowflake’s predictable pricing is easier for budgeting.
Does your current infrastructure lean more towards a structured relational model, or are you dealing with a high volume of unstructured data that requires deep learning processing?
I'd suggest Databricks for this specific use case. The Delta Lake format provides the ACID compliance you need while keeping the data open and accessible for diverse ML libraries.
I agree. Having that open-source flexibility prevents vendor lock-in, which is a massive risk when you hit the 50TB threshold in a proprietary system.
We are dealing with about 40% unstructured data including JSON logs and images. This is why we are leaning toward a Lakehouse architecture that handles both types natively without complex ETL.