FAQ's
Due to its open-source roots, Databricks is more widely used by developers than Snowflake. Databricks is constructed using three key open-source platforms: Delta Lake, Apache Spark, and MLflow. Beyond simply SQL, it also supports a wide range of other programming languages. The benefit Databricks has inside its OS origins has been clearly recognised by Snowflake, which is much more of a closed ecosystem. Snowflake has also made a number of attempts, such as by supporting Apache Iceberg, a well-known open table format for analytics in an effort to interact with the open source community
For data science and machine learning use cases, Databricks was initially developed as a data lake based on open-source Spark. At the same time, Snowflake built a cloud data warehouse that could be utilised for business intelligence analytics. The majority of clients will utilise both Databricks and Snowflake for their areas of expertise respectively.
Organisations can utilise Databricks ETL as a data and AI solution to speed up the operation and functioning of ETL pipelines. The platform offers data management, security, and governance features and may be utilised in a number of sectors.
The platform includes scalable services to create enterprise data pipelines, so it has everything you need whether you're a data scientist, data engineer, developer, or analyst. The platform is very flexible and simple to master in around a week.
Some of the competitors of Snowflake are Amazon Redshift, MongoDB, Oracle database, Db2, Cloudera Enterprise Data Hub, etc.