Snowflake VS Databricks

 

Databricks is also cloud-based but is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework to make management of infrastructure easier. Databricks positions itself as a data lake rather than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.

Databricks can be used to handle raw unprocessed data in large volume. Databricks is delivered as SaaS and can run on AWS, Azure, and Google Cloud. There is a data plane as well as a control plane for backend services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Snowflake includes a storage layer while Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

For those wanting a top-class data warehouse, Snowflake wins. But for those needing more robust ELT, data science, and machine learning features, Databricks is the winner.


Snowflake vs. Databricks: Support and Ease of Use Comparison

The Snowflake data warehouse is said to be user-friendly, with an intuitive SQL interface that makes it easy to get set up and running. It also has plenty of automation features to facilitate ease of use. Auto-scaling and auto-suspend, for example, help in stopping and starting clusters during idle or peak periods. Clusters can be resized easily.

Databricks, too, has auto-scaling of clusters but it is not so user friendly. The UI is more complex as it is aimed at a technical audience. It requires more manual input when it comes to things like resizing clusters, updating configurations, or switching options. There is a steeper learning curve to overcome.

Both offer online support. Snowflake provides 24/7 live support while Databricks offers support during business hours.

Snowflake wins this category.



Snowflake vs. Databricks: Integration Comparison

Snowflake is on the AWS Marketplace but is not deeply embedded within the AWS ecosystem. In some cases, it can be challenging to pair Snowflake with other tools. But in other cases, Snowflake is wonderfully integrated. Apache Spark, IBM Cognos, Tableau, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish.

Both tools support semi-structured and structured data. Databricks has more versatility in terms of supporting any format of data including unstructured data. Snowflake is adding support for unstructured data now, too.

Databricks wins this category.

Snowflake vs. Databricks: Conclusion


Snowflake and Databricks are both excellent data platforms for analysis purposes. Each has its pros and cons. Choosing the best platform for your business comes down to usage patterns, data volumes, workloads, and data strategies.

Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is more suited to streaming, ML, AI, and data science workloads courtesy of its Spark engine which enables use of multiple languages. Snowflake has been playing catchup on languages and recently added support for Python, Java, and Scala.

Some say Snowflake is better for interactive queries as it optimizes storage at the time of ingestion. It also excels at handling BI workloads, and the production of reports and dashboards. As a data warehouse, it offers good performance. Some users note, though, that it struggles when faced with huge data volumes as would be found with streaming workloads. On a straight competition on data warehousing capabilities, Snowflake wins.

But Databricks isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Snowflake for ELT, data science, and machine learning. Users store data in managed object storage of their choice and doesn’t get involved in its pricing. It focuses on the data lake and data processing. But it is squarely aimed at data scientists and highly capable analysts.

In summary, Databricks wins for a technical audience. Snowflake is highly accessible to technical and less technical user base. Databricks provides pretty much every data management feature offered by Snowflake and a lot more besides. But it isn’t easy to use, has a steep learning curve, and requires more maintenance. But it can address a much wider set of data workloads and languages. And those familiar with Apache Spark will tend to gravitate towards Databricks.

Snowflake is better set up for users that want to deploy a good data warehouse and analytics tool rapidly without bogging down in configurations, data science minutia, or manual setup. And this isn’t to say, either, that Snowflake is a light tool or for beginners. Far from it. But it isn’t high-end like Databricks, which is aimed more at complex data engineering, ETL, data science, and streaming workloads. Snowflake, in contrast, is a warehouse to store production data for analytics purposes. And it is good for beginners, too, and for those that want to start small and scale up gradually.

Pricing comes into the selection picture, of course. Sometimes Databricks will be much cheaper due to the way it allows users to take care of their own storage. But not always. Sometimes Snowflake will pan out cheaper.