Internet of Things


Production floor generating thousands of sensor readings per second. Crate collects them to feed the machine learning process optimizing the production parameters.

IoT applications generate a tremendous amount and variety of data, including sensor readings, telemetry data, time series, geolocation, and machine logs. This is often accumulated with high velocity and variety of data formats, precision, and structure.

This creates a number of database challenges for IoT app developers:

  • Ingesting tens of thousands of readings per second?
  • Analyzing data in real time, as new data streams into the database?
  • Scaling to store and analyze months or years of historic data? 
  • Querying structured and unstructured data (e.g. machine logs)?
  • Performing time series, geospatial, text search and other advanced analytics?

Crate benefits

Crate is an open source SQL database that is ideal for IoT applications. Compared to other databases, Crate provides the following benefits:

Fast Performance - Crate is designed to ingest and query IoT data very fast by dividing the work across a cluster of inexpensive servers and executing it in parallel.

Simple Scalability - You add more containers, and you have more capacity, even just for a few minutes. Crate adjusts data to optimize performance and fault tolerance. No DBA required. 

Flexible Analytics - Crate processes tabular and unstructured data (like JSON and full text) equally well. No need to implement and integrate separate SQL and NoSQL databases to handle multi-model workloads.

Ad-hoc Analysis - Unlike other big data SQL DBs, Crate is able to run arbitrary queries against billions of rows of data very fast, allowing you to explore and work with IoT data freely, even for advanced queries like time series, geospatial and text search.

Easy to integrate and develop - Crate is a SQL database with standard database connection drivers (like JDBC) that enable it to work with any popular business intelligence and visualization tools, or Spark to leverage machine learning.

Example architecture

CrateDB allowed us to explore business cases that were simply not possible on Microsoft SQL Server.

Case study

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