How Yellowbrick Data Warehouse Outperforms Snowflake

Budgeting Made Simple

Snowflake’s consumption model, where you purchase credits up front and then burn through them while your virtual data warehouses are running, makes it near impossible to determine the costs because you don’t know how long your queries will take to run, particularly in a mixed workload environment.

This is especially the case for ad hoc, discovery workloads that are very data and query dependent. This restricts innovation, as business analysts and data scientists/engineers must now become cost-conscious or risk exceeding their analytics budget given the open-ended nature of this type of work. Snowflake’s autoscaling makes the cost situation even harder to predict, as scaling out your cluster dynamically in response to new workloads or concurrent users means doubling the size of the cluster. You might find yourself doubling costs just because you’ve added a single new user to the system.

Snowflake isn’t interested in making its software more efficient as it would impact its business model. Since Snowflake has no workload management, the only way to improve performance is to double the nodes and the costs.

With Yellowbrick, you don’t buy credits, you simply decide what size of data warehouse you want in terms of the number of nodes and pay a flat subscription for the software over a 1- or 3-year term for that number of nodes.

• It’s an “all you can eat” deal – you can consume as much of Yellowbrick as you like, 24x7 and 365 days of the year, without worrying about cost overruns at a fixed cost. The more queries you run, the lower cost per query.
• If you need to scale, you can add nodes to Yellowbrick one node at a time and we’ll only charge you a pro rated amount for the extra nodes you use – no huge step changes in cost.
• Yellowbrick was designed from the ground up to be the most efficient database in the world and we do more with less, saving you money

Query Performance Comparison

Yellowbrick is regularly benchmarked against other data warehousing solutions during the procurement process. In this document we present query performance comparisons from real world benchmarks against Snowflake. This benchmark was conducted by a customer using its own data and query workloads.

Snowflake is an MPP cloud data warehouse, which is delivered “as-a-service” on all three major public clouds. Advocates of Snowflake cite its elasticity features, its ability to share data easily and its compelling user experience. While it is certainly true that Snowflake has made data warehousing more accessible to many smaller enterprises, its performance struggles when data volumes and concurrency requirements are higher and mixed workloads get more complex. Snowflake’s answer to higher concurrency and query volumes is to spin up more virtual data warehouses, which means partitioning up your users and workloads manually. This quickly becomes cost prohibitive, particularly if you are running business-critical “always-on” applications.

A customer in the digital marketing space wanted to reduce the time to insight into campaign performance for its clients, who are primarily in the financial services sector. The customer decided to replace its existing 40-node Vertica data warehouse platform to drive the improvements in performance needed from its advertising platform. The customer ran proof of concept exercises with Snowflake and Yellowbrick to identify the replacement. As part of the POC, it conducted query performance tests using 12 representative SQL workloads against its own data. The data consisted of 3.5 trillion records distributed across 82 tables, with a volume of approximately 500 TB.