Clear, owned outputs.
- Spark applications and pipelines
- Performance analysis with recommendations
- Optimized job configurations
- Test suites
- Migration plan (where relevant)
Apache Spark processes large volumes of data in parallel, for batch and streaming workloads. Well-written Spark jobs are fast and economical; poorly written ones are slow and expensive. We develop, test and tune Spark applications, and help teams move Spark to managed platforms.
The problems Apache Spark solves.
PySpark and Scala batch and streaming jobs, tuned for speed and cost.
Jobs take hours and often fail on large inputs.
Data skew causes a few tasks to run far longer than others.
Streaming jobs fall behind at peak volumes.
Spark code has no tests and is hard to change.
What Apache Spark covers.
Spark development
PySpark, Scala and Spark SQL applications.
Structured Streaming
Near-real-time processing with checkpoints and recovery.
Performance tuning
Partitioning, joins, caching and resource configuration.
Skew and memory issues
Diagnosing and fixing skew, spills and out-of-memory errors.
Testing
Unit and integration tests for Spark code.
Platform migration
Moving to Databricks, EMR, Dataproc or Synapse Spark.
What we tune in a slow Spark job.
Most slow or expensive Spark jobs suffer from the same handful of problems. We profile with the Spark UI before changing anything.
| What we check | Why it matters |
|---|---|
| Partitioning | Too few partitions leave executors idle; too many create scheduling overhead. Input and shuffle partitions are sized to the data. |
| Data skew | One key holding most rows makes one task run for hours. Salting, skew hints or adaptive query execution spread the work. |
| Joins | Broadcasting small tables avoids shuffles; sort-merge joins on large ones need matching partitioning. |
| File layout | Thousands of small files slow every read. Compaction, Z-ordering or liquid clustering keep tables efficient. |
| Caching and spills | Caching only what is reused, and watching for memory spills to disk that signal undersized executors. |
| Cluster sizing | Node type, autoscaling limits and spot instances matched to the job, not a single cluster for everything. |
Why it's worth it.
Faster processing
Tuned jobs finish sooner and more reliably.
Lower compute cost
Efficient jobs need fewer resources.
Maintainable code
Tests and structure make changes safe.
When Apache Spark is the right move.
- A nightly batch job that no longer fits its window.
- Streaming event data for operational analytics.
- Moving on-premises Spark to the cloud.
Technologies & platforms
- Apache Spark
- PySpark
- Scala
- Spark SQL
- Structured Streaming
- Databricks
- Amazon EMR
- Google Dataproc
- Apache Kafka
How Apache Spark work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Apache Spark questions.
PySpark or Scala?
PySpark is most common and suits teams that work in Python. Scala can help in specific performance-sensitive cases. We work in either and match your team.
Can you fix a slow Spark job without rewriting it?
Often, yes. Many gains come from partitioning, join strategies, file sizes and configuration rather than rewrites.
Do we still need Spark for smaller data?
Not always. For modest volumes, a warehouse or single-node tools can be simpler and cheaper. We'll tell you if Spark is overkill.
Talk to us about apache spark.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.