01
Architecture and System Design
We design data flows end to end, from capture and transport to modeling, storage, and serving. The result is a clear architecture, documented decisions, and a roadmap your team can own.
We build the systems that store, move, and answer your data.
Stratorys designs data and software architecture for teams working at scale. We help choose the right storage and query engines, shape reliable data flows, and turn knowledge earned through practice into code that lasts.
Raw data becomes queryable data
SELECT insight
FROM production_systems
WHERE latency < budget
Storage and engine selectionData flow architectureSchema and modelingBackend and service designKnowledge as codeReliability in production
Understanding a production system is, first, a data problem.
To understand how a system really behaves, and where it breaks, you have to collect, move, store, and query a great deal of data. The volumes are large, the questions are demanding, and the answers are only useful if they come back fast.
Performance is decided early through choices that are easy to get wrong, including how data is modeled, where it lives, and how it is interrogated. The choice of a storage and query system is rarely neutral. We help you make that choice deliberately, then build the architecture around it.
Six disciplines. Two ways to work.
You can bring us in as advisors to shape the right decisions, or as builders to deliver them. We work across the full path from data capture to query.
02
Storage and Query Engine Selection
Columnar, time series, search, OLAP, or embedded. We benchmark options against your real workload and recommend the engine that meets your performance and cost constraints, with the evidence behind it.
03
Data Flow and Pipeline Engineering
Batch and near realtime pipelines built for predictable throughput and operational clarity, from ingestion through transformation to serving.
04
Build and Refactor
Hands on implementation for new services, major refactors, and performance critical components, often in Rust and against engines like ClickHouse and DataFusion.
05
Reliability Enablement
Observability, failure handling, release strategy, and runbooks help systems stay stable in production long after we leave.
06
Knowledge as Code
We capture domain and operational knowledge as executable, versioned artifacts. Schemas, contracts, transformations, and documentation live in your repository, not in someone's head.
Learn more about Knowledge as CodeConsulting
Advisory engagements cover architecture reviews, engine selection, technical roadmaps, and mentoring. They are short, focused, and built to support decisions.
Delivery
For delivery work, we implement, refactor, and harden systems alongside your team, then hand over something you can run and evolve yourselves.
What the work looks like in practice.
A good engagement leaves more than advice behind. It leaves a decision your team can defend, a system your team can run, and the reasoning needed to evolve it.
Choosing an analytical backend
Context. A product team needs to serve interactive analytics over growing event data, with clear latency and cost limits.
Work. We describe the workload, shortlist engines such as ClickHouse, DataFusion, and Postgres, then benchmark representative queries against realistic data and concurrency.
Output. The team receives a written recommendation, the benchmark evidence behind it, and the conditions that should trigger a future review.
Making a data pipeline easier to own
Context. A team relies on a pipeline whose rules live in tickets, chat, and the memory of a few engineers.
Work. We map the flow, identify the contracts between producers and consumers, encode the schemas and expectations, and add tests where failures would be expensive.
Output. The repository explains the pipeline, CI catches breaking changes earlier, and operations have runbooks close to the code they describe.
Knowledge that runs, gets reviewed, and survives turnover.
Most of what a team knows about its data lives in conversations, spreadsheets, and a few people's heads. When they leave, it leaves with them. We make that knowledge explicit through schemas, data contracts, transformations, tests, and documentation that are versioned, reviewable, and executable.
The result is a system that explains itself, onboards faster, and changes safely.
Explore Knowledge as Code// knowledge, versioned
contract orders {
order_id Uuid @primary
amount Decimal @>= 0
placed_at Timestamp
}
// reviewed in PR and tested in CI
assert freshness(orders) < 5m
Three principles guide the work.
Simple by design
We remove complexity rather than manage it. Systems that are easy to understand are easy to evolve, operate, and trust.
Measured delivery
We set clear technical goals and validate decisions against production metrics, not opinions. If we cannot measure it, we do not claim it.
Reliable in production
Observability, resilience, and runbooks are part of the design from day one, so the system holds up under real load, not just demos.
Built on a fast, modern data stack.
We favor tools that are fast, predictable, and well understood. We go deep rather than wide.
RustClickHouseDataFusionBallistaApache ArrowParquetKafkaSQLOpenTelemetry
Data Engineering
Query engines, pipelines, and analytical backends with predictable performance.
Software Architecture
Backend services and boundaries shaped for maintainability and safe evolution.
Performance Engineering
Benchmarking, profiling, and tuning against real workloads and budgets.
Reliability
Observability, resilience patterns, and operational practice.
The kinds of systems we're built for.
Observability and telemetry
Logs, metrics, and traces unified for fast incident triage and clear service ownership.
Analytical and OLAP backends
Columnar stores and query engines tuned for interactive analytics at volume.
Realtime ingestion
Resilient pipelines that hold up on weak networks, with resume and backpressure built in.
Data governance and compliance
Service oriented workflows for ingestion, extraction, and auditable processing.
Investigative and graph tooling
Tracing relationships across large transaction and entity graphs with clear outputs.
Security operations
Platforms for alerting, triage, and response coordination with reliable incident tracking.
Small by design, with the right people involved.
Stratorys works with a trusted network of senior engineering partners. When a project needs more hands or deep specialist knowledge, we bring in the right people rather than the nearest ones. That may mean functional programming, big data platforms, or domain expertise. You get the focus of a small team and the reach of a large one.
Questions, answered plainly.
What does Stratorys do?+
We design and build data and software systems for teams working with large volumes of data. That means selecting storage and query engines, shaping reliable data flows, designing service architecture, and capturing knowledge as code. We can work as advisors or as builders.
What technologies do you specialize in?+
Primarily Rust, and high performance data engines such as ClickHouse, DataFusion, Ballista, Apache Arrow and Parquet alongside streaming with Kafka, SQL, and observability tooling. We go deep on a focused stack rather than spreading thin.
Do you do consulting or hands on delivery?+
Both. Consulting engagements are short and built to support decisions. They cover architecture reviews, engine selection, and roadmaps. Delivery engagements are hands on builds where we implement and harden systems with your team and hand over something you can run yourselves.
What is "knowledge as code"?+
It's the practice of turning what your team knows about its data into executable, versioned artifacts. Schemas, data contracts, transformations, tests, and documentation live in your repository. Knowledge becomes reviewable and survives people leaving.