Snowflake is a great platform! It’s a simple cloud-based data platform that, in many ways, is the standard for how cloud data platforms should function – near infinite scale, simple interface, standard skill sets (e.g., SQL), and easy setup. So why do we recommend considering Databricks for customers focused on Advanced Analytics workloads?
In a given week, we often hear from customers that they “are not ready for AI.” Or “AI is later on the roadmap for us.” These statements are made for good reasons. The reality is that many companies are still struggling with basic data capabilities. Often still trying to accumulate clean data that can be served up in reports and data exploration tools. And legacy data tools are fragile, expensive, and inflexible. So, the thought goes, if I’m running my business blind, I don’t have time to invest in AI.
De-prioritizing an AI strategy is a mistake. Why? Because more than likely your competition hasn’t. One only needs to look at other market examples to understand AI’s disrupting effect (e.g., Netflix, Uber, Tesla, Davinci Surgery, etc.). So without a strategy to use Artificial Intelligence to improve business outcomes, you leave the door open for your competitors to disrupt the market. This is why at Macula, we believe you don’t simply need a new data strategy; you need a business strategy based in the realities of our data-first world that includes AI/ML.
“In short, software is eating the world.” – Marc Andressen
“Every company is not just a software company, but also a data company.” – Matt Turck
Databricks is the clear winner for delivering Artificial Intelligence solutions compared to Snowflake. But don’t take our word for it. Listen to the industry research from Gartner. Databricks is a Gartner Magic Quadrant “Leader” in both the Cloud Database Management Systems (DBMS) AND Data Science and Machine Learning categories. In contrast, Snowflake is only a “Leader” in the Cloud DBMS category. Yes, you can add 3rd party tools to Snowflake that enable AI scenarios, but the platform itself was not built with this in mind. So why not choose a platform built from the ground up with both scenarios in mind?
Databricks named leader by Gartner - Databricks
Databricks supports the complete Machine Learning lifecycle and many more ML features – feature store, model registry, tracking, Auto ML and many open-source libraries, to name a few things. Databricks also excels in features for advanced ETL/ELT transformation of data.
Still not convinced? Give us a call and we can talk about how Databricks bring unique solutions to your specific business context.
According to the Barcelona Supercomputing Center, a neutral 3rd party organization that benchmarks database system performance and cost attributes, Databricks is “2.7x faster and 12x better in terms of price performance.” So, choosing Snowflake over Databricks hurts your bottom line.
Databricks Breaks Data Warehousing Performance Record (analyticsindiamag.com)
Snowflake stores your data in their tenant and uses their proprietary data format, while Databricks keeps your data in your data lake and uses open-source Delta Lake technology. You can always directly access your data without going through the Databricks API, while you will always pay to access your data through Snowflake.
Apache Spark™ - Unified Engine for large-scale data analytics
MLflow - A platform for the machine learning lifecycle | MLflow
