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Technology Platforms

CrateDB empowers Technology Platforms with real-time analytics, scalable data storage, and predictive maintenance capabilities, enabling enhanced user experiences, optimized operations, and efficient IoT data management.


Real-time Data Analytics

CrateDB provides technology platforms with the ability to perform real-time analysis of large volumes of data from diverse sources. This enables platforms to gain actionable insights, detect trends, and make data-driven decisions to optimize their operations and enhance user satisfaction.


Scalable Data Storage

CrateDB offers a scalable and flexible database solution for storing and managing structured and unstructured data. Technology platforms can use CrateDB to efficiently store and retrieve data, ensuring high performance and reliability as their user base and data volume grow.


Predictive Maintenance

By leveraging CrateDB's capabilities for real-time data processing and predictive analytics, technology platforms can implement predictive maintenance strategies. This allows them to monitor the health and performance of their infrastructure and equipment, detect potential issues early, and schedule maintenance proactively to minimize downtime and maximize uptime.


User Behavior Analysis

CrateDB enables technology platforms to analyze user behavior data, such as clicks, views, and interactions, in real-time. By understanding how users engage with their platforms, companies can optimize user experiences, personalize content and recommendations, and improve customer satisfaction and retention.

IoT Data Management

For platforms that incorporate IoT devices and sensors, CrateDB provides a robust solution for managing and analyzing IoT data streams. This enables platforms to monitor and control connected devices, extract valuable insights from sensor data, and optimize IoT deployments for improved efficiency and performance.

Case Study #1: ABB

Less than 20% of data generated by industrial companies is used and ABB's flagship digital solution wants to change that. ABB Ability Genix, launched in 2020, caters to multiple sectors, combining data-centric approaches with AI/ML and domain knowledge to deliver contextually rich data. It expands beyond sensors and devices to include engineering, design, and IT data, integrating them into the platform with pre-made adapters and ABB's domain knowledge.

Key objectives

  • Perform very fast time-series queries, both with hot and cold data.
  • Process different workloads with no impact on performance.
  • Detect real-time issues through window function aggregation.

Why CrateDB?

  • With horizontal scalability, ABB can adapt to every customer situation by adding as many nodes as needed.
  • Data Tiering (Warm/Hot/Cold) allows to optimize hardware costs
  • Multi-platform support to be deployed anywhere


After exploring the market, we found out that CrateDB was the best fit, considering our requirements and use-cases.
Marko Sommarberg Lead, Digital Strategy and Business Development ABB
1 Mill values/sec 30k to 120k event/sec
Data ingestion Event retrieval

Case Study #2: Bitmovin

Bitmovin is a leading video streaming company that built the world’s first commercial adaptive streaming player and deployed the first software-defined encoding service that runs on any cloud platform. Its portfolio includes services for scalable video encoding, the Bitmovin Player for playing videos on all platforms and devices, and a complete end-to-end streaming solution with Bitmovin Streams. The company offers a product for monitoring and analyzing video streaming data, which is used by the biggest media companies around the world to deliver high-quality video experiences to end users.

Key objectives

  • Collection and analysis of huge and fast-moving data sets in real-time
  • Process extremely high data throughput in a cost-efficient manner
  • Easy to scale database with SQL as query language
  • Detect real-time issues through window function aggregation
  • Be able to process structured and semi-structured data (such as log files) in high quantities and at very high throughput speeds

Why CrateDB?

  • The data aggregation performance required by the video analytics component would not be possible with other comparable solutions
  • The capability of scaling the clusters of the database itself
  • Adaptation to peak loads with logical replication
  • On-Premise solution so that Bitmovin can manage their tech stack on their own
  • Ongoing support from CrateDB to optimally adjust the configuration of the cluster ahead of events with high data load requirements
  • No need for a complex architecture or advanced knowledge of SQL to get started.
Nodes Terabytes of storage Events in the largest tables New events per day
14 140,000 ~60 billion ~2 billion
"It is through the use of CrateDB that we are able to offer our large-scale video analytics component in the first place. Comparable products are either not capable of handling the large flood of data or they are simply too expensive."
Daniel Hölbling-Inzko Senior Director of Engineering – Analytics Bitmovin