Your engineers and customers read from the same data.
We replace overloaded legacy databases with a lakehouse: one data store on S3 that serves your internal analysts and your external customers.
At a glance
- Build
- 8–20 weeks
- then a 3-week handoff
- Data shape
- IoT, events, batch, streaming
- Audience
- Internal and external
- First deliverable
- 48 hours
One pipeline behind every report.
From raw sources to dashboards, in your AWS account.
- S3 lakehouse with schemas you can extend
- Ingestion with replay and freshness monitoring
- Data contracts checked on every PR
- BI layer in Power BI, Tableau or Superset
- Customer-facing APIs with SLAs and rate limits, if you serve external users
Four stages, from source inventory to serving.
- 01Data landscape and source inventoryEvery system of record catalogued, on-prem, cloud and SaaS, down to the Excel workbook someone runs overnight. We find out what is truly authoritative.
- 02Lakehouse architectureS3 as source of truth, in bronze, silver and gold layers (raw, cleaned, aggregated). Redshift or Athena for serving, chosen on your query patterns, not the vendor pitch.
- 03Ingestion pipelinesEvent-driven where latency matters, batch where it doesn’t, matched to the shape of your data. Reconciliation and replay are built in from day one.
- 04Serving and accessDashboards and data products for internal and external users, all backed by the same pipeline. No engineering copy versus customer copy.
Chosen for your query patterns, not the vendor pitch.
Contracts signed off before we wire up a source.
An aerospace OEM’s engineers and airlines now read engine telemetry from one pipeline.
Aerospace OEM, 2023
Not a fit if you…
- Just need a dashboard. With one source and one audience, buy Metabase and move on.
- Are looking for a managed data-platform vendor. We design and hand off; we don’t run it.
- Need pure data science: modeling, forecasting, research. We build the platform, and someone else builds the models.
Questions about the data stack.
How much does a data platform cost?
A scoped lakehouse with 3–5 sources runs $100–200k. Replacing a legacy warehouse that serves internal and external audiences runs $250–500k.
Redshift or Snowflake?
It depends on your query patterns and existing stack. Redshift suits AWS-native shops with heavy joins over structured data, Snowflake suits multi-cloud and elastic ad-hoc work. We tell you which fits, not which we prefer.
Can you integrate with dbt?
Yes, dbt is our default for transformation on most engagements. The models, tests and CI that runs them on every PR live in your repo.
What about streaming and real-time?
We use Kinesis, MSK and Lambda for ingestion, and materialized views or streaming SQL for serving. We also tell you when real-time costs far more than a 5-minute refresh would.
Do you handle data quality?
Yes. Great Expectations, dbt tests and custom assertions run inside the pipeline, and freshness, row counts and schema drift show on the dashboard your engineers already use.
Tell us what you're building.
We reply within one business day.
First call: 30 minutes, on a Tuesday or Thursday, with two engineers, not a sales rep. If we’re the wrong fit, we’ll say so and name a firm that is.