Big Data Engineering
When the data outgrows the spreadsheet, engineering takes over: ingestion pipelines, warehouses, and processing built so volume never costs you trust in the numbers.
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{ 01 } — Data process
Volume is easy. Trust is the hard part.
Any stack can store terabytes. The engineering is in pipelines that validate at entry, lineage you can trace from a dashboard back to a source row, and costs that scale slower than the data. We build those properties in from the first connector — retrofitting them is the expensive way.
Ingest
- Source mapping & connectors
- Streaming or batch — decided by latency needs, not trend
- Validation & schema contracts at entry
- Failure, retry & replay handling
- Idempotent loads — reruns never double-count
Organize
- Warehouse & lake modeling
- Partitioning & tiering for cost
- Lineage & documentation as part of the build
- Access controls & data contracts
- Retention & archival policy, decided deliberately
Serve
- Query layers & department marts
- Dashboard & BI feeds on freshness SLAs
- ML-ready, documented datasets
- Data APIs & reverse ETL into operational tools
- Cost & performance tuning on cadence
Big-data tooling is oversold: plenty of “big” workloads fit a well-tuned warehouse at a fraction of a cluster's cost and complexity. We size the stack to the measured workload — volumes, query patterns, latency needs — and put that sizing decision in writing before anything is provisioned.
When you genuinely need distributed processing, we build it with the same discipline: schema contracts at ingestion, lineage traceable end to end, and cost-per-query visibility so growth stays budgeted rather than discovered. Volume is a storage problem; trust is an engineering problem.
The quiet test of a data platform is what happens at 3am when a source changes its schema. Ours fail loudly — validation blocks the bad load, the alert names the source, and the replay procedure is documented. Silent corruption is the failure mode we engineer out first.
{ 03 } — What we build
From firehose to insight.
Data pipelines
Reliable ingestion from apps, devices, and third parties — validated at entry, idempotent on replay, alerting on failure.
Warehouses & lakes
Modeled, partitioned storage that analysts can query without an engineer on call — documented down to the column.
Stream processing
Real-time aggregation and alerting where minutes genuinely change a decision — and honest advice where batch is enough.
Cost engineering
Partitioning, tiering, and query tuning that keep growth affordable — with cost per query visible, not buried in the cloud bill.
Data quality & lineage
Contracts at entry, tests between layers, and lineage from any dashboard number back to the source row that produced it.
Platform rescues & migrations
Inherited pipelines untangled and legacy stacks migrated incrementally — no big-bang cutover, no lost history.
{ 04 } — Pipeline stack
Pipelines built on boring, proven parts.
Exotic data tooling is how budgets die. Every layer here is battle-tested, documented, and hireable-for — so the platform outlives the engagement.
{ 05 } — Ways to engage
Three ways to start, sized to the problem.
Platform sizing audit
A short, fixed-price assessment: volumes, query patterns, and costs measured — ends with a sizing verdict and an architecture you could hand to any team, including one that is not us.
- Workload measured, not guessed
- Right-size verdict in writing
- Useful even if you build elsewhere
Build + handover
We design and build the platform — pipelines, warehouse, quality layer, serving — then hand over with lineage, documentation, and training on a stack you can hire for.
- Your cloud, your keys
- Boring, hireable-for technologies
- Runbook + replay procedures included
Data platform retainer
Ongoing engineering for platforms in production — new sources modeled in properly, costs tuned on cadence, incidents owned end to end.
- Monthly capacity, no re-scoping
- Cost & performance reviewed on cadence
- New sources modeled, not bolted on
{ 06 } — What you receive
A platform, and the proof it can be trusted.
Pipelines without contracts, lineage, and runbooks are just scheduled risk. Every engagement ships the platform and the artifacts that keep it honest.
The measured workload and the stack it justifies — including “a tuned warehouse is enough” when it is.
Connectors with validation, retries, replay, and idempotency — reruns never double-count, failures never pass silently.
Partitioned, documented storage that analysts query without an engineer on call — access controlled by design.
Schema contracts, tests between layers, and lineage from any number on a dashboard back to its source row.
Cost per query, per pipeline, per month — growth visible before the invoice arrives, tuning ranked by payback.
Failure playbooks, replay procedures, and team training on a mainstream stack — operating it never depends on us.
{ 07 } — The symptoms
Signs the data has outgrown the setup.
Scale problems announce themselves quietly, then all at once.
{ 08 } — What changes
From data everywhere to answers on time.
Before
Nightly jobs fail silently; someone notices at 11am.
After
Pipelines alert on failure and data-quality checks block bad loads.
Before
Every department keeps its own spreadsheet version of the truth.
After
One governed warehouse — the same number in every meeting.
Before
A new report costs a week of engineering time.
After
Modeled, documented tables — analysts self-serve in hours.
Before
The cloud bill grows faster than the data.
After
Partitioning, tiering, and cost-per-query visibility — growth budgeted, not discovered.
Before
An upstream schema change corrupts reports downstream, silently.
After
Contracts at entry — the bad load is blocked and the alert names the source.
Where this applies
Get expert guidance on your data platform.
Book a free consultation call — a senior team member replies within one business day with real thoughts, not a sales script.
Frequently asked questions
Maybe not — and that is a good outcome. The sizing audit often shows a tuned warehouse suffices; you only pay for distributed complexity when the measured workload demands it, and the verdict comes in writing either way.
PostgreSQL and cloud warehouses first; Spark, Kafka, and object-store lakes when scale requires — mainstream, documented, handover-friendly. Exotic tooling needs to earn its place, and it rarely does.
Validation and schema contracts at ingestion, tests between layers, lineage tracking, and reconciliation between source and warehouse — drift surfaces in alerts, not board meetings.
Decided by the workload, honestly: most reporting is happy at hourly or daily refresh, and streaming earns its complexity only where minutes change a decision — alerts, fraud, live operations. We will tell you which is which.
Yes — rescues are common. We start with a lineage and dependency map, stabilize what runs today, then migrate incrementally with old and new reconciled against each other. No big-bang cutover, no lost history.
Yes — clean, modeled, documented data is exactly the foundation our AI solutions build on. Grounded assistants and reliable evals both start with a warehouse you can trust.