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CrateDB leverages smart grid monitoring and IoT devices to provide the energy industry with a scalable, real-time database solution. Its integration with AI and machine learning enables predictive maintenance, optimizing energy distribution and usage. This enhances grid reliability, efficiency, and enables data-driven decision-making for sustainable energy practices.

Smart Grid Monitoring

Real-time Data Analysis: CrateDB enables real-time analysis of data from smart meters, sensors, and other grid devices, providing insights into power consumption patterns, grid congestion, and equipment performance.

Predictive Analytics: By leveraging historical data and machine learning algorithms, CrateDB can predict potential grid issues, such as equipment failures or overloads, allowing utilities to proactively address them and prevent costly outages.


Predictive Maintenance

Minimized Downtime: CrateDB's predictive maintenance capabilities enable utilities to detect potential equipment failures before they occur, allowing for proactive maintenance scheduling. This minimizes unplanned downtime, ensuring continuous energy supply and optimizing grid reliability.

Extended Asset Lifespan: By identifying maintenance needs based on actual equipment condition rather than fixed schedules, CrateDB helps utilities optimize maintenance activities. This approach reduces unnecessary maintenance costs and extends the lifespan of critical energy infrastructure, maximizing return on investment.


Energy Consumption Analytics

Granular Data Analysis: CrateDB allows utilities to analyze energy consumption data at a granular level, including individual customer usage patterns, peak demand periods, and trends over time. This detailed analysis enables utilities to identify opportunities for efficiency improvements and demand-side management strategies.

Optimized Resource Allocation: By gaining insights into energy consumption patterns, utilities can optimize resource allocation, such as adjusting generation and distribution strategies to meet changing demand patterns. CrateDB's real-time processing capabilities facilitate timely decision-making, ensuring efficient resource utilization and grid stability.


Billions are invested in smart energy systems and revolutionize how energy is produced, delivered, and consumed; IoT innovation drives new solutions daily; Smart energy relies on solid and scalable data management. By reading these two case studies below, discover how companies in the energy industry leverage CrateDB to power real-time analytics in high-volume data architectures.


Case Study #1: Gantner Instruments

Gantner Instruments collaborates with the University of Cyprus to operate a state-of-the-art Smart Micro Grid, dedicated to investigating the control capabilities of renewable energy sources in the power grid and propelling the energy transition forward. They leverage CrateDB to analyze the vast amount of data generated in real time, enhancing their processes through machine learning (ML). With CrateDB, they gain access to their extensive data within microseconds at the frontend, ensuring optimal performance.

Key objectives

  • Intelligent Power Grid Management.
  • Collect and evaluate power grid data quickly and synchronously so that the network can be optimally controlled in real-time to allow for a higher proportion of renewable energies.

Main technical challenges

  • View all data in a central console for real-time data virtualization or integrated into existing systems via API.
  • Enable AI driven optimizations of the power grid by allowing data to be written back into the backend.


200,000 8,000
parameters every minute parameters per energy asset

Why CrateDB?

  • Ability to analyze huge amounts of data generated in real-time and used to improve processes through machine learning.
  • Ensure instant time series data analysis with unlimited scalability.
"CrateDB is the only database that gives us the speed, scalability and ease of use to collect and aggregate measurements from hundreds of thousands of industrial sensors for real-time visibility into power, temperature, pressure, speed and torque."
Jürgen Sutterlütti VP Energy Segment and Marketing Gantner instruments

The Results

With the Advanced System Monitoring and Analytics solution, GI provides real-time control and AI for tomorrow’s smart grid services. Enabled by CrateDB, this next-generation multi-service monitoring and control system allows energy providers to
  • Improve system performance
  • Decrease Levelized Cost of Electricity (LCoE)
  • Utilize AI-driven analytics
  • Enhance observability
  • Manage smart grid controls
“With CrateDB the data is hot at all times and can therefore be captured and processed quickly. With the help of CrateDB, we can make large amounts of data available in microseconds at the frontend.”
Jürgen Sutterlütti VP Energy Segment and Marketing Gantner instruments Learn more


Case Study #2: Best.Energy

Key objectives

  • Real-time Usage Statistics and Customer Insights.
  • Provide real-time monitoring and statistics to customers connecting their IoT sensors to the platform.

Main technical challenges

  • Highly concurrent read & write loads from the thousands of sensors and users.
  • Combining current customer data with historical behavior data.
  • Custom dashboards generating super complex queries.
1,400,000 Many formats
metrics per minute Table, time-series, JSON, full-text, geospatial, vector

Why CrateDB?

  • Ultra-fast data aggregations – customers getting real-time insights.
  • Super complex queries running in sub-second response times.
  • Scaling on the cloud without any hassle. Storing all historical data.
  • Very cost- and space-efficient compared to other databases.
  • CrateDB support being a game-changer.
"Being able to store the different types was very important (...) if we didn't want to store our total values, we could store them as objects."
Max Sangster Head of Cloud Services