Despite significant advancements in the speed and capacity of modern computers over the past 20 years, there is often still a noticeable delay between importing data and being able to act on the processed and queried results. For instance, it's not uncommon for batch processes querying large datasets to be queued, sometimes running overnight in extreme cases. While such delays may not be a major issue in scenarios like business intelligence gathering, in many other contexts, the time lag between data entering a database and initiating action based on its processing can be crucial.

One of CrateDB's primary applications is in the manufacturing sector, where Industrial Internet of Things (IIoT) sensors on high-speed machinery generate real-time data. In these scenarios, rapid data ingestion, processing, and device adjustment are crucial. Similar requirements exist in geospatial applications and certain financial processing tasks. We spoke with Simon Prickett, CrateDB's Developer Relations Lead, to explore the platform's capabilities and standout features. "Our strength lies in reducing the delay between receiving data and making it queryable," Prickett explained. "If you receive data, queue it, and delay processing, it takes much longer to detect, for instance, that your 24/7 conveyor belt is overheating. CrateDB is optimized for concurrent read and write workloads."

CrateDB achieves its exceptionally high data ingestion rates through clustering. "The cluster self-manages and scales as needed by adding nodes. That's why we call it a real-time analytics database," Prickett noted. The platform's flexibility extends to its ingestion and processing capabilities, enabling users to scale operations based on demand. CrateDB supports various data formats, from structured relational tables to semi-structured JSON documents and fully unstructured binary or vector data. "CrateDB provides a unified space for all these data types," Prickett said.

Its schema mutability enables CrateDB to replace multiple databases. "Users can define a table schema for known data. If new records don't match the schema, the database can auto-extend the schema and start indexing immediately," Prickett explained. Users can choose to index everything, selectively index parts, or reject mismatched records, offering a balance of flexibility and control.

While indexing introduces some storage overhead, this is negligible in production environments like real-time financial transaction checks or monitoring fast-paced manufacturing lines. "The key benefits are flexible querying and lower operational costs. You don't need a DBA to add an index after a complex query slows the database—it just doesn't happen," he said.

Integration with existing systems is straightforward since CrateDB is wire-compatible with Postgres, allowing teams to adopt it with minimal disruption. For additional support, CrateDB follows the open-source model, offering paid services for advanced needs. Typically, CrateDB operates alongside other databases, either on-premises, in hybrid setups, or in environments requiring continuous operation, even during internet outages or cloud service interruptions. "It's unlikely CrateDB would replace a system of record database," Prickett noted. "But as a complementary solution for specialized, innovative tasks? Absolutely."

CrateDB offers extensive resources for potential users, including a video-based learning academy and a fully managed cloud option. Organizations are encouraged to test the platform with their own data in simulated production environments. The platform's open-source foundation and active community ensure ongoing growth and development.