Molecula’s Ability to Supercharge Snowflake

By: Laura Komkov

Molecula’s Ability to Supercharge Snowflake

Checkout out our on-demand webinar from February 9th to learn more about Molecula’s feature store.

Molecula has so many possible functions — it can knock out aspects of your stack or, if you’re not ready for major adjustments yet, Molecula has the ability to supercharge some of the aspects of your current stack. Let’s start with how Snowflake and Molecula can work together.

TL;DR Version: Snowflake assists with moving organizations away from legacy data warehouses, creating more flexibility with and access to their data. However, Snowflake can get extremely expensive very quickly if you’re using large amounts of storage or compute. It also cannot handle high-concurrency queries without negative impacts on query performance, and it does not extract features, a requirement for machine-scale analytics and AI, from your data.

Molecula can integrate directly with Snowflake allowing your organization to reduce data storage and compute footprint, thereby dramatically lowering your Snowflake costs. At the same time, Molecula’s feature-based format prepares you for all AI/ML initiatives and allows for high-concurrency queries and ultra-low latency access to all of your data. Reduced costs + added use cases = more value from your data immediately.

Now let’s get into a bit more detail…

Snowflake

Snowflake was built on the idea of migrating your legacy data warehouse to the cloud and has evolved into an intelligent analytics platform that powers a variety of different use cases. It’s built on top of the Amazon Web Services and Microsoft Azure clouds and can be used both online and offline where data can be moved into Snowflake easily using an ETL solution.

By separating storage and consumption functions, Snowflake allows for a good deal of flexibility when it comes to pricing, paying only for what you use. The types of queries, performance requirements, and scale of data all have a huge impact on Snowflake’s cost and must be taken into consideration when using the platform. As organizations graduate from reporting and explaining with human-scale data to predicting and prescribing real-time business outcomes, they typically see their consumption costs with Snowflake skyrocket. In addition, to achieve these predictive and prescriptive outcomes, Snowflake requires additional technologies that enable advanced analytics and AI. These additional technologies come with added costs and allow solely for leverage of data within Snowflake itself, so businesses still end up lacking in the performance they’re hoping to see. 

Snowflake can be an extremely powerful tool for enabling business intelligence initiatives, but falls short when attempting to power machine-scale analytics and AI projects. Migration to the cloud simply lifts and shifts legacy approaches away from on-premise solutions, but it does not reliably move the needle when it comes to achieving desired business outcomes. 

If your business does not need scalable, millisecond query performance, and real-time data, Snowflake can be an excellent solution. It integrates seamlessly with visualization tools like Tableau and is operationally ready across several cloud environments, allowing for human-scale analytics. Snowflake has a tendency to slow down when queries become too complex or highly-concurrent and when data volume is large. 

This is where Molecula can help.

Molecula

Molecula is an enterprise feature store that simplifies, accelerates, and controls big data access to power machine-scale analytics and AI. Continuously extracting features, reducing the dimensionality of data at the source, and routing real-time feature changes into a central store enables millisecond queries, computation, and feature re-use across formats and locations without copying or moving raw data. The Molecula feature store provides data engineers, data scientists, and application developers a single access point to graduate from reporting and explaining with human-scale data to predicting and prescribing real-time business outcomes with all data.

Molecula + Snowflake

Molecula continuously extracts and updates features from Snowflake as well as any other data source you would like to leverage for machine scale analytics and AI. The result is ‘AI ready data’ that can be queried in real-time and leveraged for your most advanced analytics and AI projects across organizations. Data Science teams would finally have access to all data, the ability to leverage any feature sets and re-use features. None of your raw data is copied or cached in the process (only your features are copied), and no raw data flows into Molecula, which allows greater security for cross-team projects. 

Molecula’s overlay feature store allows your organization to graduate beyond the human-scale queries that Snowflake enables, and into real-time, high-concurrency queries for machine-scale analytics and ML. All the while, Molecula reduces your data footprint up to 85%, and reduces your compute storage and costs.

Snowflake vs. Molecula Cheat Sheet