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Use cases > Dedicated analytics

Your Production Database Wasn't Built for Analytics

When analytical queries start competing with your application workload, the answer isn't more indexes or a bigger instance. It's a dedicated analytics layer. Here's how CrateDB makes that separation fast and painless.

The problem every growing team hits

It starts small. A few analytical queries running against your production Postgres or MySQL. A dashboard here, a report there. For a while, it's fine. Then your data grows. Your query complexity grows. And one day your on-call engineer gets paged because analytical queries are locking tables, exhausting connections, and making your application slow for real users. Your database, which was designed to run your product, not power your analytics, is doing two jobs and struggling with both.

The answer isn't to tune your way out. It's to separate the workloads. Analytical offload means replicating or streaming your operational data into a dedicated analytics database, one built to handle large-scale queries, high cardinality, and concurrent analytical workloads without any impact on production. Your application keeps running fast. Your data team gets the query performance they need. And you stop choosing between the two.
cratedb analytical offload diagram

Why CrateDB works well as an analytics offload target

CrateDB is designed to receive data from production systems and make it immediately queryable at scale. A few things make it particularly effective for offload workloads:

It Speaks SQL Your Team Already Knows

Because CrateDB is PostgreSQL wire protocol compatible, your existing queries, ORM connections, and BI tools work without modification. Migrating analytics off Postgres or MySQL to CrateDB often takes days, not weeks and you're not learning a new query language or rewriting your data models.

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Data is Queryable Within Milliseconds of Ingest

CrateDB auto-indexes every field on arrival. As soon as data replicates from your production system via CDC or streaming, it's available for queries. There's no batch window, no reindex job, no lag between your production data and your analytics layer.
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It Handles the Queries that Break OLTP Databases

Large aggregations, multi-dimensional GROUP BY queries, full-text search, cross-table joins on billions of rows; these are exactly the workloads CrateDB is built for and exactly the workloads that cause problems on Postgres or MongoDB under production load.

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Schema Changes Don't Require Downtime

As your data model evolves — new fields, new event types, new metadata — CrateDB adapts automatically. You don't need to run ALTER TABLE on a production system or coordinate schema migrations across teams.

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How teams connect their production systems to CrateDB

The most common patterns are:

  • Change Data Capture (CDC): Stream changes from Postgres, MySQL, or MongoDB into CrateDB in real time using tools like Debezium, or via CrateDB Cloud's native MongoDB CDC integration. Your analytics layer stays continuously in sync with production with minimal latency.
  • Kafka / event streaming: If your architecture already publishes events to Kafka, CrateDB ingests directly from Kafka topics. No additional ETL layer required.
  • Batch replication: For less time-sensitive workloads, periodic replication via standard SQL export/import or ETL tools works well and is straightforward to set up.

 

Real teams who made this move
VoIPstudio is an award-winning VoIP phone service. They use CrateDB to manage call detail records (CDRs). MySQL had been used successfully for several years, but due to increasingly poor performance it was replaced by CrateDB.
"After a successful and problem-free implementation it became clear to us that CrateDB was the solution we were looking for. We gained better performance and new possibilities for our customers. We are going to improve and extend our cloud system using CrateDB."
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voip-1
ClearVoice provides content writing services and with an ecosystem of top writers. They use CrateDB to power their search index, which stores 20 million records and this is expected to grow to billions in the near future.

"I started looking at CrateDB and was impressed by the quality of the code. Switching from MySQL to CrateDB took a couple of days. It was very convenient to integrate CrateDB into our existing base even though it was written for a different database. The fact that CrateDB uses SQL lowers the barrier to entry when using distributed search. And on top of that, with CrateDB you can replace MongoDB and Elastisearch with one scalable package."

Jeff Nappi
Director of Engineering
ClearVoice

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Clearvoice
Spatially Health is a provider of location intelligence software.They use CrateDB for their data scientists to find geographic insights through the patterns and relationships in their data by analyzing terabytes of data. They plan to scale CrateDB to handle hundreds of terabytes as it continues to expand to other cities.

"Postgres couldn't keep up with the data we have; Datastax Enterprise had ingest scaling issues with spatial data; Cassandra didn't have spatial query operations. CrateDB was the only database we found that could smoothly process data for our users and for our data science team. We fell in love with it immediately."

Kartik Venkatesh
CTO
Spatially Health

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Spatially-Health
The GolfNow division of the NBC Universal GolfChannel is trusted by 4 million registered golfers to search for golf courses that fit preference and budget. They use CrateDB to currently perform real-time queries of more than 300 million rows of data, hundreds of times per second.

"CrateDB was a better solution for our needs than any other SQL or NoSQL database we tried. It was easy to migrate code off of our legacy SQL database and onto CrateDB to immediately benefit from its data flexibility and scalable performance."

Sheriff Mohamed
Director of Architecture
GolfNow

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golfnow
Roomonitor specializes in IoT climate control systems that monitor and control air conditioning for the hospitality industry. They use CrateDB as an IoT database able to give property managers real-time insight to monitor air conditioning costs and tenant comfort.

"CrateDB's unmatched concurrency capabilities and simple scaling made it the best solution for us. We tried other solutions, including MongoDB, but it was difficult and expensive to scale for our needs. Plus, CrateDB is SQL, which 90 percent of today's developers know well, and that makes hiring new developers easier."

Waseem Javid Nasiri
Senior developer
Roomonitor

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roomonitor
kooky is the first digital cup smart system by tracing and operating reusable food packaging. They were facing issues of simultaneously querying large volume of data from multiple heterogeneous databases and were thus limited to offer value added services in real-time to users dropping their packaging items into their machines. Thanks to CrateDB, kooky can now deliver those services by querying user history and loyalty points in real-time.

"With CrateDB it was extremely easy to have a single place that we could query through our entire system within milliseconds at any moment in time, and this was impossible before."

 

Dmytro Boguslavskyy
CTO & Co-Founder at kooky

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When analytical offload makes sense

Offloading to a dedicated analytics database is worth doing when:

  • Analytical queries are noticeably affecting application response times
  • Your team is considering scaling up your production database primarily to handle analytics load, which is an expensive solution to the wrong problem
  • You need to run complex queries (large aggregations, multi-dimensional analytics, full-text search) on data that lives in a transactional database
  • Your data science or BI team is waiting on slow queries or limited by what OLTP databases can handle
  • You're planning to add AI or vector search capabilities and don't want to introduce yet another specialized system alongside your production DB
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Documentation
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Change Data Capture (CDC)

CrateDB Cloud enables continuous data ingestion from MongoDB using Change Data Capture (CDC). CrateDB also supports popular third-party data integration frameworks and platforms for CDC, both managed and unmanaged.

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Additional resources

FAQ

Analytical offload involves transferring resource-intensive analytical workloads from operational databases (like PostgreSQL or MongoDB) to a specialized system. This separation ensures that transactional systems remain responsive, while analytics can be performed efficiently and at scale.

CrateDB enables seamless replication from operational databases using Change Data Capture (CDC). This allows for real-time analytics on fresh data, supporting ad-hoc queries, dashboards, and AI workloads without impacting the performance of operational systems.

Yes. CrateDB can complement data warehouses by handling real-time analytics, while the warehouse focuses on batch processing and long-term storage. This hybrid approach optimizes performance and reduces costs.

Key benefits include:

  • Faster insights: Millisecond query responses.
  • Cost savings: Reduced need for scaling operational databases.
  • Operational stability: Uninterrupted performance of transactional systems.
  • Simplified architecture: Elimination of complex ETL processes.

CrateDB excels in scenarios requiring real-time analytics, high ingestion rates, and support for both structured, semi-structured and unstructured data. It's ideal for applications like IoT monitoring, log analysis, and real-time dashboards.