Large reporting jobs
Data prepared at a scale where one database no longer copes.
AI Development
Apache Spark development services beside our reporting work
We have no published Spark build. We have shipped Power BI reporting on Azure SQL and a Python data back end, and we show where Spark would take over when data outgrows them.
0Published builds on Spark
3 monthsGap between Spark feature releases
18 monthsMaintenance for an LTS release
Nov 2027End of the Spark 3.5 extended LTS
What we build
What we would take on with Spark
Pipelines
Jobs scheduled beside the reports, as on the Power BI builds.
Spark upgrades
Moves onto a release that still gets patches.
Lake and warehouse feeds
Data landed for dashboards in Power BI.
Architecture
Where Spark would sit
Spark in front of our reporting data
Sources
Azure SQLJobs and results
MQTT telematicsPosition and engine data
Processing
Apache SparkWhere it would sit
Python 3.13Back end language
Reporting
Power BIOperational dashboards
Illustration. A dashed outline marks where the technology would sit. We have no published build on it.
Versions
How long a Spark release is maintained
The Spark versioning policy
Feature releasesQuarterly, with six months of bug fix releases.
Major releasesEvery 12 months as x.0.0, with six months of patches.
LTS releasesThe last release in each major line gets 18 months.
Spark 4.x LTSPlanned as Spark 4.5.0, not 4.3.0.
Spark 3.5Extended LTS to November 2027, security fixes only.
Security patchesCritical fixes are backported to all maintained branches.
Source: Apache Spark versioning policy, spark.apache.org/versioning-policy.html, checked 4 November 2026.
Builds
The closest builds we can show you
Statutory inspection, United Kingdom
Work-at-height anchor and torque platform
Azure SQL data feeding PDF certificates and Power BI dashboards.
70%Faster reporting cycle
Education, Norway
National education analytics platform
Power BI insight for teachers and administrators on a .NET and Angular platform.
No lossData moved from OPAL 1.0 to OPAL 2.0
Fit check
When Spark is the right call
Pick Apache Spark when
- Data has outgrown one database server
- Jobs must process large files in parallel
- Your team already writes Spark in Python
Look elsewhere when
- You need a published Spark build to inspect, which we do not have
- Data that fits in Azure SQL, where we have proof
- One nightly script, where a scheduler is enough
Explore
Same job, other tools
Pairs with
Where we used it
Stack
What Spark usually sits beside
Python
AWS
Azure
Apache Airflow
Kubernetes
DockerFAQs
What buyers ask first
How long is a Spark release maintained?
Feature and major releases get about six months of bug fix releases. The last release in each major line is the LTS and is maintained for 18 months.
Do you have a published Spark build?
No. We say so on the first call and show the nearest work, which is Power BI reporting on Azure SQL and a Python data back end.
Is Spark 3.5 still supported?
It has an extended LTS to November 2027 with security fixes only.
Who owns the code and the cloud accounts?
You do, from the first commit. Repositories and cloud accounts stay in your name.
Tell us how big your data has grown
Thirty minutes on your data, your jobs and your Spark version. No sales pitch.