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.
What is Databricks?
What is Snowflake?
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Why do developers choose Databricks?
Why do developers choose Snowflake?
What are the cons of using Databricks?
What are the cons of using Snowflake?
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I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.
What's nice too is that it has SQL-based ML tools, and it has great GIS support!