Case Study

Virtual Global Trading AG leverages QuestDB for efficient energy data management

Virtual Global Trading uses QuestDB to manage time-series data for energy production and consumption, enabling dynamic pricing and efficient energy distribution.

Real-Time Analytics
Receives and organizes high volumes of data from sensors and applications
Data Deduplication
Clean data input prevents predictive errors and ensures accurate results
Scalable Architecture
Utilizes QuestDB with Azure container instances for easy scalability and efficient data management
Virtual Global Trading Banner Image
Virtual Global Trading Dashboard

Efficient Energy Data Management

Time-series data, handled with precision

Virtual Global Trading leverages QuestDB to receive data from a broad array of smart meters, power plants, sensors, and other devices which monitor energy grid usage. Data is time-bound for billing and tracking purposes. A specialized time-series database ensures clean, timely arrival of key data.
Real-Time Monitoring
QuestDB enables Virtual Global Trading to process and aggregate time-series data from smart meters and power plants instantly.

“QuestDB's superior query speed and efficiency for time-series data, the ability to efficiently align data to specific time zones, and simplification of data handling with out-of-the-box functionalities for time-series data were key factors”

Flavio Mueller
Data Scientist, Virtual Global Trading
QuestDB SQL
SELECT
  datapointName,
  meteringPointID,
  source,
  sourceID,
  interval,
  status,
  MIN(measuredUTC) AS measuredUTC,
  MIN(importedUTC) AS importedUTC,
  SUM(value) AS value
FROM
  (
    SELECT
      datapointName,
      meteringPointID,
      value,
      source,
      sourceID,
      interval,
      status,
      measuredUTC,
      importedUTC
    FROM
      <DataTable>
    WHERE
      measuredUTC >= '2015-10-31T00:00:00.000000Z'
      AND measuredUTC < '2025-11-01T02:00:00.000000Z'
      AND meteringPointID = <SomeID>
    LATEST ON importedUTC PARTITION BY measuredUTC
  )
SAMPLE BY 1y
ALIGN TO CALENDAR TIME ZONE 'Europe/Zurich';

SQL, clean and simple

Time-series extensions for precise queries

Virtual Global Trading uses powerful SQL queries to manage and aggregate time-series data efficiently. Time-series extensions like SAMPLE BY enhance the precision of these queries, enabling better data handling and visualization.

Data for various sensors arrive, then are processed and aggregated by time. This leads to dynamic pricing calculations and real-time information for both customers and internal applications.

This powerful query is broken down as such:
  • A subquery filters and deduplicates data by measuredUTC and importedUTC
  • The outer query aggregates the filtered data, sums up value and takes the minimum of measuredUTC and importedUTC
  • SAMPLE BY then groups the data by yearly intervals, then aligns to the calendar in the Europe/Zurich time zone

Virtual Global Trading's Efficient Data Management

Predictive Analytics for Energy

Virtual Global Trading previously utilized MongoDB for time-series data. However, MongoDB's limitations for time-based aggregations and data updates prompted the transition to QuestDB. With QuestDB, Virtual Global Trading has overcome their ingestion and analytics bottlenecks.
Read the docs

"The ability to use InfluxDB Line for data ingestion and the helpfulness of the web console for SQL queries were also significant."

Gregor Martinovic — CTO, Virtual Global Trading

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