Built for teams running
Apache Spark Development Services
Scala Teams covers the full range of Spark work: new pipeline builds, migrating existing PySpark jobs to native Scala, performance tuning for jobs that have outgrown their current architecture, and version upgrades across Spark and Scala releases. Whether you need one engineer embedded in your data team or a full engagement covering pipeline architecture and delivery, the engagement scales to the work.
The Spark Ecosystem Our Engineers Already Know
No ramp-up time spent learning tools. They've shipped with all of this before.
Apache Spark
Structured Streaming
Spark MLlib
Kafka
Scala 3
Parquet
Delta Lake
Hadoop
Airflow
Docker
Scala vs. PySpark: What Actually Changes
Most teams start in PySpark because Python is the more familiar entry point. Here's what changes once a pipeline outgrows that layer.
| What matters | Native Scala Recommended | PySpark |
|---|---|---|
| API access | Full Spark API, no translation layer | Subset exposed through Python bindings |
| Performance on large jobs | Direct JVM execution | Serialization overhead between Python and JVM |
| Type safety | Compile-time error catching | Runtime errors more common |
| Best fit | High-throughput, long-lived pipelines | Fast prototyping, smaller jobs |
Native Scala · Recommended
Full Spark API, no translation layer
PySpark
Subset exposed through Python bindings
Native Scala · Recommended
Direct JVM execution
PySpark
Serialization overhead between Python and JVM
Native Scala · Recommended
Compile-time error catching
PySpark
Runtime errors more common
Native Scala · Recommended
High-throughput, long-lived pipelines
PySpark
Fast prototyping, smaller jobs
Most teams start in PySpark because Python is the more familiar entry point. That works well for prototyping and smaller jobs. It starts to strain once pipelines grow in complexity or data volume, which is usually when teams look to migrate the core pipeline layer to native Scala rather than continuing to work around a translation layer.
Weighing a different engagement model? See the full comparison.
How Scala Teams Deploys
Single Engineer
Need one senior Spark developer to own a specific pipeline or migration? We match and deploy fast.
Dedicated Team
A full-time, long-term team embedded in your data engineering org, with daily standups and direct communication.
Full-Cycle Delivery
A managed engagement covering pipeline architecture, QA, and delivery, from day one to shipped.
Scoped Engagement
For a defined migration or build, we deploy the right engineers against a defined timeline.
Where Scala Teams Delivers Spark Work
Data pipelines
Large-scale batch and streaming jobs built for throughput and reliability.
Real-time processing
Spark Streaming and structured streaming pipelines paired with Kafka.
Legacy migration
Moving PySpark jobs or outdated Spark and Scala version pairings to a modern, maintainable stack.
Spark and the Infrastructure Behind Production ML
Most machine learning work doesn't stall on the model. It stalls on the pipeline feeding it. Training data has to be cleaned, joined, and moved at scale before a model ever sees it, and that infrastructure is where Spark does its heaviest lifting, especially through Spark MLlib, Spark's native library for distributed machine learning.
Building that pipeline layer in Scala keeps it in the same language Spark itself is written in, which means fewer translation layers between your data engineering and your model training, and pipeline code that holds up under production load instead of breaking under it. Scala Teams engineers build and maintain this infrastructure directly: feature pipelines, training data preparation, and the serving infrastructure that gets a model's output back into your product reliably.
Evaluating Scala specifically for its role in AI and ML infrastructure? See how Scala fits into modern ML pipelines.
What to Look for When You're Evaluating a Spark Partner
Native Scala fluency, not just Spark familiarity
Plenty of engineers have touched Spark through PySpark without ever working in Scala directly. Ask specifically about Scala-native pipeline experience, not just Spark exposure.
Version and compatibility expertise
Spark and Scala version pairings are a common source of build failures. A partner who can speak concretely to this has been through it before.
Experience with pipelines at your scale
A pipeline that works cleanly at a small data volume can behave very differently at production scale. Ask for evidence of both.
Clear communication standards
What matters is how async handoffs work, how decisions get documented, and what happens when something goes sideways.
How It Works
Frequently Asked Questions
Is Apache Spark written in Scala?
Yes. Spark's core is built in Scala, which is why engineers working natively in Scala have direct access to the full API surface and performance characteristics that other language bindings add overhead around.
When should I migrate from PySpark to Scala?
Usually when pipeline complexity, data volume, or performance requirements outgrow what a Python-to-JVM translation layer can handle efficiently. Teams often start in PySpark for speed of prototyping and migrate the core pipeline to Scala once it's running production-critical workloads.
How long does it take to hire a senior Spark developer?
Traditional recruiting for Spark specialists who work natively in Scala can take months, since most of the hiring pool defaults to Python-first candidates. Scala Teams compresses that to days.
What does it cost to hire a Spark developer through Scala Teams?
Engagements are scoped to the work, not billed by headcount. A single embedded engineer, a dedicated team, and a fixed-scope migration each carry a different cost profile, so we give you a direct number once we understand what you need rather than a range that means nothing.
Can I hire just one Spark developer, or does this require a full team?
Either. Some clients bring on a single senior engineer to own a specific pipeline or migration, others need a full engagement for a larger data initiative. The model and the accountability are the same either way.
Is Scala Teams a staffing agency for Spark developers?
No. A staffing agency places individual candidates and steps back once the contract is signed. Scala Teams stays accountable to what ships.
Is Scala Teams an outsourcing company?
Yes. Our engineers work outside your direct headcount. What sets us apart is accountability: we stay responsible for the quality of what ships, instead of stepping back once someone is placed.