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PARTNERApache Spark
We write and tune Spark jobs for large-scale transformation, streaming and machine learning, on Databricks, EMR, Dataproc or your own clusters.
Readable, tested PySpark or Scala code for heavy transformations.
Event processing from Kafka, Kinesis or Event Hubs with exactly-once sinks where needed.
Fixing skew, shuffles and small-file problems that slow jobs down.
Feature preparation and model training with Spark MLlib or integrated libraries.
Step 1
We agree the decisions the data should support and the measures that matter.
Step 2
We profile your sources, note quality issues and confirm what is feasible.
Step 3
Pipelines, models and reports are delivered one subject area at a time.
Step 4
Documentation, access controls and training so your team can run and extend it.
Databricks reduces operational work; self-managed Spark can cost less at steady scale. We compare both for your team.
Yes. We usually build on your existing cloud, warehouse and BI licences, and recommend new tools only when there is a clear gap.
Share your goals and current setup. We will come back with questions, a suggested approach and the next steps.
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