Data and AI
Big Data Engineering
Handle the volume without the meltdown
What this involves
How we approach big data engineering at Oasis Hive.
Past a certain volume, the tools that got you there stop working. We design storage and processing that scales horizontally and stays understandable to the next engineer.
Streaming and batch platforms that ingest at scale and stay affordable and queryable.
What we do
Platform architecture
Storage, compute and query layers designed around your access patterns and budget.
Batch and streaming ingestion
Reliable pipelines with backfill, replay and exactly-once handling where needed.
Performance and cost tuning
Partitioning, compaction and query tuning to keep latency and bills sane.
Governance and lineage
Cataloguing, access control and lineage so people trust what they query.
What you end up with
- Ingestion that keeps up with your peak volumes
- Query performance that stays usable as data grows
- Costs you can explain to a finance team
- Lineage and access control your auditors accept
Tools and technologies
We choose per project rather than by habit. These are the ones we most often reach for here.
Questions about this service
Delivered properly
One team from scoping through to support
The people who design the system are the people who answer for it in production, so nothing falls between suppliers.
Proof
Where we have done this
How the work runs
Six steps, no surprises
Step 01
Listen and map
We interview the people doing the work, review the systems in play and write down how things actually flow.
Step 02
Design the solution
We propose what to build first, what to leave alone, what it will cost and what we are assuming. Options come with trade-offs.
Step 03
Build in increments
Working software every two weeks, demonstrated against real data. You can redirect early while changes are still cheap.
Step 04
Verify and secure
Automated tests, security review and load checks run throughout, not at the end, so findings are fixed while the context is fresh.
Step 05
Launch with support
We plan the cutover with you, migrate data carefully, train your team and stay close through the first weeks in production.
Step 06
Run and improve
Monitoring, support and a regular review of what to improve next. Maintained systems stay valuable.
Often paired with
Services that work well alongside this one
Data Analytics
Decisions backed by numbers you trust
Data modelling, pipelines and dashboards that give leaders one agreed version of the truth.
Machine Learning
Models that earn their place in production
Forecasting, classification and anomaly detection built to run reliably in production.
Cloud and DevOps
Ship changes confidently, several times a day
Infrastructure as code, CI/CD and observability, so releases become routine.
Next step
Let us scope big data engineering properly
A short discovery gives you a written specification, a schedule and a fixed price. No obligation to continue afterwards.