As a data engineer, I’ve been intrigued by the claim that Databricks can outperform Snowflake in certain scenarios. Having used Snowflake for a while, I couldn’t help but wonder: how can Databricks be faster when running a query? To understand this, let’s dive into a specific use case.
Imagine we have 10 TB of CSV data in an AWS S3 bucket, partitioned by date. We want to query this data using SQL, without filtering by date. On Snowflake, we’d ingest the data into an internal table, converting it to a columnar proprietary format optimized for querying. We’d also cluster the table on date and enable search optimization.
In contrast, Databricks doesn’t create a proprietary database file format. Instead, it uses the underlying S3 bucket as data and creates a table based on that. This raises the question: how can Databricks be faster than Snowflake, given that Snowflake has optimized the data for querying?
The key lies in how Databricks processes data. While it may not convert the data to a proprietary format, it uses the power of Apache Spark to parallelize queries and leverage the scalability of the cloud. This allows Databricks to handle large datasets more efficiently than Snowflake, especially when it comes to complex queries.
Additionally, Databricks’ architecture is designed for fast data processing, with features like caching and query optimization. This enables it to outperform Snowflake in certain scenarios, even without converting data to a proprietary format.
In conclusion, while Snowflake’s optimized data format has its advantages, Databricks’ architecture and parallel processing capabilities make it a strong contender for fast data processing. Understanding the strengths of each platform is crucial in choosing the right tool for your data engineering needs.