Virtual Global Trading 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 across smart meters, power plants, and grid infrastructure.

Efficient energy data management

Virtual Global Trading leverages QuestDB to receive data from a broad array of smart meters, power plants, sensors, and other devices that monitor energy grid usage. Data is time-bound for billing and tracking purposes, and a dedicated time-series database ensures clean, timely arrival of the information required for downstream analytics.

SQL, clean and simple

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

Data from various sensors arrives in real time, then is processed and aggregated by time. This supports dynamic pricing calculations and real-time information for both customers and internal applications.

The following query illustrates how deduplication, aggregation, and calendar-aligned sampling work together:

• A subquery filters and deduplicates data by measuredUTC and importedUTC using LATEST ON.
• The outer query aggregates the filtered data, sums value, and computes the minimum of measuredUTC and importedUTC.
• SAMPLE BY groups the data into yearly intervals and aligns it to the calendar in the 'Europe/Zurich' time zone.

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';

Virtual Global Trading's efficient data management

Virtual Global Trading previously relied on MongoDB for time-series data. Limitations around time-based aggregations and updates prompted a move to QuestDB. With QuestDB, the team overcame ingestion and analytics bottlenecks and can now support predictive energy analytics at scale.