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Service 04 — Big Data & Analytics

One pipeline. Two audiences. Zero incidents since launch.

We replaced an aerospace OEM's brittle MSSQL with a lakehouse serving their engineers and their airline customers from the same Redshift layer. Reports that used to run overnight now finish before the coffee is cold.

Engagement length
8—20 weeks
Data shape
IoT · Events · Batch · Streaming
Audience
Internal + external
First deliverable
48 hours
What's includedThe work itself

Lakehouses built for two audiences at once — and the pipelines that keep both honest.

Four stages
of delivery
DeliverablesWhat you'll have at the end

Pipelines, schemas, and dashboards your analysts can trust at 9am Monday.

i.

Lakehouse

S3 bronze/silver/gold, with schemas your team can extend without calling us.

ii.

Ingestion pipelines

Every source wired up with replay, reconciliation, and freshness monitoring.

iii.

Data contracts

Schema-level agreements between producers and consumers. Breaking changes detected at PR time.

iv.

BI layer

Power BI, Tableau, or Superset — whichever your team will still use in two years.

v.

Customer-facing APIs

If your pipeline serves external users, we build the serving layer with SLAs and rate limits.

vi.

Handoff sessions

Three weeks of pair-ops with your data engineers and analysts. We answer the questions that come up Monday morning.

StackWhat we build with

Chosen for the query pattern, not the vendor pitch.

Storage
S3 · Iceberg · Delta Lake
Warehouse
Redshift · Snowflake · Athena
ETL
Glue · PySpark · dbt · Airflow
Streaming
Kinesis · MSK · Lambda
BI
Power BI · Tableau · Superset
Quality
Great Expectations · dbt tests
Catalog
Glue Catalog · DataHub
ML-ready
SageMaker · Feature Store
Case studyAerospace OEM · 2023 — present

Every engine is streaming telemetry into a MSSQL database from 2009. Our analysts run reports overnight and pray.

Reports that used to run overnight now finish before the coffee is cold. 3× the telemetry on half the footprint.

The engineering team's dashboards and the airlines' customer-facing fleet health reports ran on two forks of the same MSSQL database that drifted apart weekly. Reconciliation was a person's Monday morning. Nobody trusted either copy.

We rebuilt it as a single lakehouse: S3 bronze for raw telemetry, silver for cleaned events, gold for the aggregates both audiences query. Engineers use Power BI, airlines hit a Redshift endpoint via API. One pipeline. Two audiences. Zero production incidents since launch.

A good fit if you —

Have two audiences fighting over the same data.

Not a fit if you —

Just need a dashboard.

ProcessFrom first call to handoff

Four stages. Plain rules at each one.

i.
Step 01

Discovery

48 hours to architecture + budget. If we can't deliver both in the same document, we refund the discovery fee.

ii.
Step 02

Architecture

Data contracts signed off before we wire up a source. Producers and consumers agree before code ships.

iii.
Step 03

Build

Your AWS account, your IAM, your repo. Incremental shipping — first business query runs by week three.

iv.
Step 04

Handoff

Three weeks of pair-ops with your analysts and data engineers. Then we go.

QuestionsCommonly asked

The honest answers.

What buyers ask us before signing
How much does a data platform cost?

A scoped lakehouse with 3–5 sources: $100–200k. Full replacement of a legacy warehouse serving both internal and external audiences: $250–500k. You get the architecture plan and budget in the first 48 hours.

Redshift or Snowflake?

Depends on your query patterns and existing stack. Redshift wins for AWS-native shops with heavy joins over structured data; Snowflake wins for multi-cloud and elastic ad-hoc workloads. We'll tell you which fits — not which we prefer.

Can you integrate with dbt?

Yes — dbt is our default for transformation on most engagements. Your models, your repo, your tests. We leave you with a CI that runs them on every PR.

What about streaming / real-time?

Kinesis, MSK, Lambda for ingestion; materialized views or streaming SQL for serving. We've built sub-second dashboards on top of kafka-scale firehoses. We'll also tell you when "real-time" is costing you 10x for a use case that's fine at 5-minute latency.

Do you handle data quality / observability?

Yes — Great Expectations, dbt tests, and custom assertions baked into the pipeline. Freshness, row counts, schema drift, and business-rule checks all visible on the same dashboard your engineers look at.

Ready to replace your brittle data spine with one pipeline, two audiences?

First call is thirty minutes, on a Tuesday or Thursday, with two engineers — not a sales rep. If we're the wrong fit, we'll name someone better.

Start a project → See the work
Service 01

AWS Architecture & Migration

The AWS environment you'll still be running in five years — built for the team that will inherit it.

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Service 02

DevOps & CI/CD Pipelines

PR merge → multi-region prod in under 12 minutes, with the audit trail a regulator would accept.

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Service 03

AI Integration & LLM Systems

Bedrock RAG that survived a Tier-1 bank's OSFI B-13 review in six weeks. Zero data egress.

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