In this blog series, we’ve already discussed how incrementally maintained views (Part 1), offloading real-time use cases (Part 1), data mesh (Part 2), and normalized data models (Part 2) can save you money on your data warehouse bill.

In this latest post (Part 3), we’ll examine how sinking precomputed results and real-time analytics can reduce your data warehouse bill. You can find more cost reduction strategies in our new white paper: Top 6 Strategies for Reducing Data Warehouse Costs

5. Sink Precomputed Results

One of the critical benefits of an operational data warehouse is continuous data transformation. Materialize leverages streaming data, SQL support, and incremental updates to continuously and cost-effectively transform data.  

This is ideal for real-time use cases that demand constant computation, such as user-facing analytics and fraud detection. But you can also harness Materialize’s transformations throughout your data stack.

With Materialize, you can sink results to external tools. Sinks are the inverse of sources and represent a connection to an external stream where Materialize outputs data. 

When a user defines a sink over a materialized view, source, or table, Materialize automatically generates the required schema and writes down the stream of changes to that view or source. In effect, Materialize sinks act as change data capture (CDC) producers for the given source or view.

This enables many use cases in your external tools. For instance, you can maintain the most up-to-date query results in Materialize, and then sink them with your analytical data warehouse. 

This will allow you to access the freshest query results in your analytical data warehouse without having to compute them. Instead of engaging in expensive transformations, the analytical data warehouse can focus on simpler aggregations. 

You can produce historical analytics based on how the computed results changed over time. This kind of analysis augments your standard historical reporting. 

And you won’t need to recompute these queries in your analytical data warehouse, saving you money on compute costs. This is how sinking results from Materialize can cut costs across other tools in your data stack.  

6. Real-Time Analytics

One of the promises of a modern data warehouse is that multiple teams and users can retrieve, query, and analyze data simultaneously. However, this flexibility can also drive costs upward. Cost centers develop when various teams are working on the same data warehouse, performing tasks in parallel.

For instance, consider a real-time analytics use case on a data warehouse. 

Users have a need for near real-time analytics, but this is hard to achieve on batch warehouses. Usage spikes as a result. When users continuously update their dashboards throughout the day, costs for query recomputation grow considerably. Also, when many different users run reports with high frequency, usage costs rise. 

Without set limits on usage, users can continue to refresh their analytics as much as they want, and continuously incur costs. But putting hard limits on usage isn’t the answer. Your users are refreshing their dashboards and reports because they need up-to-date results, not because they want to waste money. 

They want real-time analytics to perform analysis and make decisions. The goal, then, is not to limit usage. It’s to enable real-time analytics without driving up costs.

With operational data warehouses such as Materialize, you can build real-time dashboards and reports without subsuming query recomputation costs. 

Materialize combines real-time data, continuous data transformation, and incremental updates. This allows you to cost-effectively generate real-time analytics by incrementally updating materialized views. 

Stream your real-time results to BI tools for visualization. Materialize is PostgreSQL wire-compatible and can connect to BI platforms such as Looker, Metabase, and more. Now your users can leverage real-time BI without constant refreshes. 

But this is just one use case for real-time analytics in Materialize. 

Increasingly, we’re seeing our customers use Materialize and real-time analytics in their user-facing apps. They’re also leveraging these analytics to power their business processes. Our customer shared one of these use cases for real-time analytics with us in Onward Delivery’s Customer Story

“Our data team was serving real-time delivery status and location analytics to customers with two weeks of work and minimal ongoing maintenance.”

- Clayton Von Hovel, Data Engineer at Onward Delivery

With Materialize, you can operationalize real-time analytics, and also lower costs for querying at a high frequency.  

Download Our Free White Paper Now

Our recent blog series highlighted several strategies for reducing data warehouse costs. You can find them all in our recent white paper: Top 6 Strategies for Reducing Data Warehouse Costs.

Download the free white paper now to learn how your team can save costs with your data warehouse!

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