From high costs to near real-time insights with Zerobus Ingest
Beusa Energy, a 30-year energy sector company spanning electric hydraulic fracturing, mobile power generation, electrical distribution, field gas processing and industrial manufacturing, migrated its high-frequency telemetry ingestion from a custom SQL Statement API pipeline to Databricks' Zerobus Ingest direct-write API. The migration required only swapping the write path (same .NET worker, same MQTT subscriptions), cutting ingestion cost from roughly 689 DBU per GB to about 0.29 DBU per GB, a 99% cost reduction, while eliminating the need for a separate streaming broker tier. Today more than 6,000 devices stream telemetry at 1 Hz across about 250 remote assets, ingesting ~22 million rows daily into a unified lakehouse with end-to-end latency of about three seconds, governed by Unity Catalog. Beusa is now using this high-frequency telemetry, combined with maintenance history, to train predictive-maintenance models forecasting remaining useful life of assets, moving from condition-based to predictive and eventually prescriptive maintenance.
Overview
Beusa Energy, a 30-year energy sector company spanning electric hydraulic fracturing, mobile power generation, electrical distribution, field gas processing and industrial manufacturing, migrated its high-frequency telemetry ingestion from a custom SQL Statement API pipeline to Databricks' Zerobus Ingest direct-write API. The migration required only swapping the write path (same .NET worker, same MQTT subscriptions), cutting ingestion cost from roughly 689 DBU per GB to about 0.29 DBU per GB, a 99% cost reduction, while eliminating the need for a separate streaming broker tier. Today more than 6,000 devices stream telemetry at 1 Hz across about 250 remote assets, ingesting ~22 million rows daily into a unified lakehouse with end-to-end latency of about three seconds, governed by Unity Catalog. Beusa is now using this high-frequency telemetry, combined with maintenance history, to train predictive-maintenance models forecasting remaining useful life of assets, moving from condition-based to predictive and eventually prescriptive maintenance.
The challenge
As Beusa Energy's device counts and data volumes grew, the cost-per-GB of its custom MQTT-to-lakehouse pipeline (a .NET worker writing to Delta tables via the SQL Statement API) became visibly unsustainable -- the SQL Statement API was not designed for high-frequency operational telemetry.
The solution
Beusa migrated its high-frequency telemetry ingestion to Databricks' Zerobus Ingest direct-write API, requiring only a single change: swapping the SQL Statement API call for the Zerobus gRPC endpoint, with the same .NET 9 worker, same MQTT subscriptions, and same Sparkplug B and JSON payload handling. This avoided standing up a separate streaming broker tier such as Kafka, Azure Event Hubs, or HiveMQ.
Reported business value
Ingestion cost dropped from roughly 689 DBU per GB to about 0.29 DBU per GB, a 99% cost reduction. Today more than 6,000 devices stream telemetry at 1 Hz across approximately 250 remote assets, ingesting ~22 million rows daily, with end-to-end latency from sensor to queryable Delta table of around three seconds, governed by Unity Catalog.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other energy entries in the register.
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