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Databricks vs Snowflake: What are the differences?
Developers describe Databricks as "A unified analytics platform, powered by Apache Spark". Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. On the other hand, Snowflake is detailed as "The data warehouse built for the cloud". Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
Databricks and Snowflake are primarily classified as "General Analytics" and "Big Data as a Service" tools respectively.
Instacart, Auto Trader, and SoFi are some of the popular companies that use Snowflake, whereas Databricks is used by Auto Trader, Snowplow Analytics, and Fairygodboss. Snowflake has a broader approval, being mentioned in 40 company stacks & 45 developers stacks; compared to Databricks, which is listed in 7 company stacks and 4 developer stacks.
Pros of Databricks
- Best Performances on large datasets1
- True lakehouse architecture1
- Scalability1
- Databricks doesn't get access to your data1
- Usage Based Billing1
- Security1
- Data stays in your cloud account1
- Multicloud1
Pros of Snowflake
- Public and Private Data Sharing6
- Good Performance3
- Multicloud3
- Great Documentation2
- User Friendly2
- Serverless2
- Innovative1
- Economical1
- Usage based billing1
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What is Databricks?
What is Snowflake?
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What companies use Databricks?
What companies use Snowflake?
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What tools integrate with Databricks?
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