Big Data Development Services
Pipelines, warehouses and analytics that keep pace with your data
We build the data infrastructure that turns scattered operational records into something a business can actually act on: reliable ingestion, a warehouse modelled for real questions, and analytics your team trusts enough to make decisions from.
- Batch and streaming ingestion pipelines
- Data warehouse and lakehouse modelling
- Workflow orchestration and dependency management
- Data quality, validation and lineage tracking
- BI enablement and self-service reporting layers
- A single source of truth in place of conflicting spreadsheets
- Query performance that holds up as volume multiplies
- Analytics your team trusts, because lineage is transparent
- Infrastructure ready for machine learning when you need it
- Data warehousing
- Real-time analytics
- Customer data platforms
- Reporting automation
Common questions
Do we need a data warehouse, or is our database enough?
If reporting queries are slowing down your application, or answering a question means joining across three systems by hand, you need a warehouse. If neither is true, you do not yet, and we will tell you that rather than build one. Analytics on a production database is a fine starting point and a poor destination.
Batch or streaming?
Batch unless a decision genuinely depends on data being seconds old. Streaming is materially harder to build, test and operate, and the majority of dashboards described as real-time are read once a day. We will ask what decision changes with fresher data, and if the answer is none, batch is correct.
How do you make sure the numbers can be trusted?
Tests on the data itself, not just the code: row counts within expected bounds, referential integrity, freshness checks, and reconciliation against the source system. Plus visible lineage, so when someone asks where a figure came from there is an answer. A dashboard nobody trusts gets replaced by a spreadsheet within a month.
What if our sources disagree with each other?
They will, and resolving that is the actual project. Two systems will define a customer differently, or close a month on different days, and no amount of tooling settles which is right - that is a business decision. We surface the contradictions and get them decided explicitly rather than picking silently in code, because a silent choice becomes an unexplained discrepancy later.