Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.
 
So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.


In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

<span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.\n \n So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.\n\n In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads."}'>Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process. So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds. <span data-sheets-userformat='{"2":15293,"3":{"1":0},"5":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"6":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"7":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"8":{"1":[{"1":2,"2":0,"5":{"1":2,"2":0}},{"1":0,"2":0,"3":3},{"1":1,"2":0,"4":1}]},"10":2,"11":4,"12":0,"14":{"1":2,"2":0},"15":"Calibri","16":11}' data-sheets-value='{"1":2,"2":"Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast enough—and they still rack up a huge bill in the process.\n \n So when Ramp’s data engineering team needed to serve complex analytics queries on the critical path of their production application, they knew they needed a new tool for the job. Enter Materialize, the first operational data warehouse. Like a traditional data warehouse, Materialize centralizes the data from all of a business’s production systems, from application databases to SaaS tools. But unlike a traditional data warehouse, Materialize enables taking immediate and automatic action when that data changes. Queries that once took hours or minutes to run are up-to-date in Materialize within seconds.\n\n In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads."}'>In this talk, we’ll present how Ramp is unlocking new real-time use cases using Materialize as their operational data warehouse. The best part? The team still uses dbt for data modeling and deployment management, just like they are able to with their traditional batch workloads.

San Diego

Online

Staff Software Engineer, Data Platform

Operationalizing Ramp’s data with dbt and Materialize

Modernization & migration

Live streamed

Traditional data warehouses excel at churning through terabytes of data for historical analysis. But for real-time, business-critical use cases, traditional data warehouses can’t produce results fast...

Nikhil Benesch

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