Amazon Web Service Data Sync 1 Streamline Icon: https://streamlinehq.com
STREAMING ASSET

Set your streaming platform up right

Stream sensor and app data reliably on your own cloud, with no heavy platform to learn or lock into.
Schedule Call →
View solution →
TRUSTED BY CLIMATE INNOVATORS
groendus-eg
iwell-eg
fudura-eg
Group 166
greenfusion-eg
snappcar-eg
ctrlchain-eg
sensorfact-eg
nextsense-eg
shypple-eg
prets-eg
Repowered
beyond-weather-eg
crisp-eg
wunderflats-eg
rubio-eg
peak-eg
graduate-ventures-eg
moveenergy-eg
aenu-eg
THE BUSINESS PROBLEM

Streaming data should steer your operations without adding a platform to run

The tooling costs more than the problem

Managed streaming platforms are a heavy commitment for a handful of streams and hard to walk away from once your data lives there.

Bad readings surface downstream

A meter drops out or sends nonsense, and you find out in a dashboard or a model output days later.

Every integration is bespoke

A new site, a new sensor type, a new protocol. Each one becomes its own pipeline to build, monitor and keep alive with the team you already have.
THE SOLUTION

A portable streaming foundation, designed for real operations

Our dockerized Python framework integrates with your cloud and data stack. It packages what we learned building production streams into lightweight code that your team owns and extends.

Ingest: read from any streaming source

A new site or source publishes to the broker, and the pipeline you already run picks it up.

Any source, one contract
One contract covers checkpointed streams and brokers that ack each message, so a Pub/Sub or SQS receiver slots in without pipeline changes.
At-least-once delivery
Checkpoints are written and events are acked only after a successful write, so a crash or redeploy loses no data.

Process: your logic on every event

This is where you do the work. Write the transforms in plain Python, and the framework handles delivery and failures.

Caught before it spreads
Batches that fail validation go to a dead-letter queue for review, and clean data keeps flowing.
Route once, consume many times
One central stream splits of into dashboards, models and operations, without duplicating any data.
Schemas can evolve
Protobuf, Avro and JSON decode into one typed model per stream, so producers can add or rename fields without breaking consumers.

Serve: data where each team needs it

Analytics and operations read from the same stream, so what you monitor is what you steer on.

One source for dashboards and models
No separate extract for reporting that drifts from what operations sees.
Operational feedback
Predictions and setpoints flow back to the assets that produced the data.

Operate: run it in your own cloud

A single container with a health check, deployable on Kubernetes or a managed container service.

Keyless by design
Cloud-native identity instead of long-lived credentials your team has to rotate.
Observable end to end
OpenTelemetry traces and metrics feed straight into the infra you already run.
WHY IT WORKS

The operational strengths built into every stream

Performant

Concurrent workers and backpressure keep pace with live load without any process idling or running out of memory.

Observable

OpenTelemetry support connects built-in metrics on pipeline mechanics, such as latency and queue backlog, to the logging services you already use.

Secure

Modern keyless authentication gives access control without another secret to manage or rotate every 6 months.

Robust

Delivery is at-least-once. The broker marks events as processed once they are stored, and failed events go to a dead-letter queue, so neither a crash nor a bad reading costs you data.

From initial prototype to scalable production systems

100+ companies trusted us to build
their data & AI layer since 2018

Shypple
Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.
  • Mobility & Transport
  • LLM Systems
Next Sense
Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.
  • Buildings & Industry
  • LLM Systems
Beyond Weather
AI infrastructure to commercialize complex weather forecasting models from research to production environment.
  • Climate Intelligence
SnappCar
New ML Ops setup delivers 50% faster queries and significant cost reduction for peer-to-peer car-sharing platform.
  • Mobility & Transport
  • ML Systems
Groendus
Build an AI forecasting system for Groendus that outperforms the benchmark for energy consumption.
  • Energy & Electrification
  • Data Systems
CtrlChain
Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.
  • Mobility & Transport
  • ML Systems
Next Sense
Co-designing a multi-agent AI system with sustainability experts to scale building performance analysis without compromising quality.
  • Buildings & Industry
  • LLM Systems
Shypple
Turning manual document-heavy logistics workflows into scalable, automated AI operations for a fast-growing freight platform.
  • Mobility & Transport
  • LLM Systems
CtrlChain
Built a scalable data, analytics, and ML platform with an FTL spot pricing model in under 12 months.
  • Mobility & Transport
  • ML Systems
Sendcloud
Aligning sales, marketing, and customer success around a single ML-driven metric to unlock sustainable growth at scale.
  • Mobility & Transport
  • ML Systems
Beyond Weather
AI infrastructure to commercialize complex weather forecasting models from research to production environment.
  • Climate Intelligence
Crisp
Implement a new forecasting system for Crisp that improves prediction accuracy (WAPE) by >20%.
  • Other
  • ML Systems
FAQ

Frequently asked questions

If your use cases require more complex logic like stateful joins, windowing across streams or volumes over 100.000 messages per second, then a platform like Flink or Databricks makes more sense. However, they are often a substantial investment and can be difficult to move away from. We recommend them when a streaming setup is genuinely complex and we’re happy to advise when that is the right call.

Yes. Connectors come ready for Kafka, Kinesis, Pub/Sub, Event Hubs and MQTT for ingestion. For writing we integrate with S3, GCS or Azure Blob Storage or straight into your Data warehouse. Custom connectors can be added for any source. Logs and traces go to the monitoring your team already uses, through OpenTelemetry.

You receive readable Python code that your team can own, adjust and extend. That gives you flexibility without locking your operations into a proprietary platform. We plan the hand-over from the start of a project, so your team can run and extend it on its own.

A single pipeline instance handles roughly 100 to 10,000 events per second. For more than 100,000 messages per second sustained, we recommend Flink or Databricks.

Connectors, reliability and retry logic come ready-made, so most of the work is your own transforms and sources. In a Design Sprint, we plan that work and turn it into a roadmap.