Build Data Pipelines That
Hold Up at Scale
| → Apache Spark, Kafka, and Akka Streams expertise | → Real-time and batch processing at enterprise scale |
| → Engineers with production big data experience | → Integrates directly with your existing stack |
Most big data problems are not tool problems. They are engineering problems. We put senior Scala engineers on your data infrastructure so your pipelines run reliably, your streaming systems stay real-time, and your architecture is built to last.
WHY SCALA FOR BIG DATA DEVELOPMENT
Scala is the native language
of big data infrastructure.
Apache Spark is written in Scala, and so is Kafka Streams. When you build data systems in Scala, you are not wrapping an API. You are working in the same language as the tools themselves.
01
Native Spark performance
Spark is built in Scala. Writing pipelines in Scala gives you full access to the Spark API without the overhead or limitations of Python or SQL wrappers. You get the performance the framework was designed to deliver.
02
Type safety at pipeline scale
Strong static typing catches schema mismatches and transformation errors at compile time, not at 3am when your pipeline has silently corrupted a dataset. In distributed systems, that difference is measured in incidents and engineering hours.
03
Functional programming fits data
Immutability and pure functions make data transformations easier to reason about, test, and debug, especially in distributed environments where side effects are hard to trace and failures are expensive to reproduce.
04
First-class streaming
Akka Streams and Kafka Streams integrate naturally with Scala, letting you build real-time event-driven pipelines without the impedance mismatch that comes from stitching together tools that were not designed to work together.
05
Proven in production at scale
LinkedIn, Airbnb, and Netflix built their data infrastructure on Scala and Spark. It is not a niche choice. It is what the industry reached for when the stakes were highest and the data volumes were largest.
06
JVM interoperability
Scala runs on the JVM and works seamlessly with existing Java libraries, Hadoop tooling, and enterprise connectors. You get the full ecosystem without rewrites, without bridges, and without giving up the performance characteristics you rely on.
WHAT WE BUILD
End-to-end Scala big data capabilities.
Scala Teams builds the data infrastructure that moves, transforms, and makes sense of large volumes of data. Built on Scala and the JVM, for companies where reliability and scale are not optional.
Batch and distributed processing
Large-scale Spark ETL and analytics pipelines.
We design and build Spark jobs for large-scale ETL, data transformation, and analytics workloads. Whether you are processing terabytes nightly or running distributed SQL against a data lake, we build pipelines that are fast, testable, and built to be maintained by the next engineer on the team.
Real-time event streaming
Kafka and Akka Streams for high-throughput pipelines.
We build Kafka producers, consumers, and stream processing topologies using Kafka Streams and Akka Streams. Our engineers design systems for high-throughput, low-latency event pipelines that stay reliable under load and degrade gracefully when they don't.
Ingestion and transformation layers
From raw source to clean, structured output.
We build ingestion layers that handle schema evolution, data quality checks, and transformation logic with the reliability your downstream teams depend on. Built in Scala with strong typing so schema changes surface at compile time, not in production.
Data lake and lakehouse architecture
Delta Lake and Iceberg on Scala and Spark.
We design and implement data lake and lakehouse architectures using Delta Lake, Apache Iceberg, and cloud-native storage on Scala and Spark. Built for query performance, data governance, and the long-term flexibility that evolving data platforms require.
ML pipeline infrastructure
The Scala foundation that puts models into production.
We build the Scala data infrastructure that machine learning systems depend on, feature pipelines, model serving layers, and distributed training orchestration. Built for repeatability and scale on Apache Spark and the JVM.
Pipeline monitoring and reliability
Observability built in from the start.
We build observability into your data infrastructure from day one: lineage tracking, data quality monitoring, alerting, and SLA enforcement. You know when something breaks before your stakeholders do, and you know exactly where to look when it happens.
How we work
Three ways to engage a Scala data team.
Whether you need one Scala data engineer embedded in your team or a full team to own your data infrastructure, we match the model to what you actually need.
01
Scala dedicated data team
A full team of senior Scala data engineers working exclusively on your data infrastructure. We staff, manage, and deliver so you can stay focused on the product and the roadmap.
02
Scala data staff augmentation
One or more senior Scala data engineers embedded directly in your team. Production-ready from day one. They know Spark, Kafka, and the ecosystem and contribute without a ramp-up period.
03
Scala data project delivery
A defined data infrastructure project with a clear scope and timeline. We take ownership from architecture through delivery: pipelines, streaming systems, storage architecture, and handoff.
HOW IT STARTS
From the first conversation to engineers contributing.
We cut out the process overhead that slows most engineering partnerships down. A focused conversation, the right engineers, and a fast path to contributing.
01
We learn what you're building
We start with a focused call to understand your ML infrastructure, your stack, and what you're trying to solve. You talk to someone who knows Scala and ML, not a sales process.
02
We match you with the right engineers
Based on your stack and requirements, we recommend the right engagement model and match you with Scala engineers who have solved similar problems in production. We align on scope, timeline, and expectations before anything starts.
03
Engineers start within days
Once agreed, we move fast. Engineers onboard into your tools and workflow and start contributing within days, not weeks. We stay close throughout to make sure the work is right and the relationship is working.
Your data infrastructure deserves engineers who have done this before.
You've got a data engineering problem to solve. We've spent years building the Scala infrastructure that makes these systems work in production. Tell us what you're building and we'll take it from there.