Data Foundation
Big Data Engineering
Overview
When data volume or velocity outgrows a single warehouse, the engineering changes. We design distributed systems that stay fast and affordable as they grow.
Outcomes we measure
- End-to-end event latency
- Throughput at peak load
- Cost per million events processed
- Recovery time after failure
Problems we solve
Sound familiar?
- 01
Batch jobs that used to take an hour now run all night and still miss the morning deadline.
- 02
Streaming data from devices, apps or transactions is collected but never used in time.
- 03
Clusters are tuned by trial and error and costs spike unpredictably.
What we deliver
Concrete deliverables, not slideware.
Streaming platforms
Kafka or managed equivalents, schema registry, stream processing and exactly-once delivery where it matters.
Distributed processing
Spark and Flink jobs designed for partitioning, skew and cost, not just correctness.
Storage architecture
Partitioned, compacted open table formats with retention and tiering policies.
Performance engineering
Profiling, tuning and capacity plans backed by load tests.
How it works
From first call to production.
- 01
Workload analysis
Measure volumes, latency needs, access patterns and current bottlenecks.
1–2 weeks
- 02
Architecture and proof
Design and benchmark the target architecture on representative data.
2–4 weeks
- 03
Build and harden
Implement, load-test and add monitoring and failure handling.
6–12 weeks
- 04
Operate
Capacity planning, upgrades and on-call support if you need it.
Ongoing, optional
Tech & frameworks
Tools we use — in your stack.
- Apache Spark
- Apache Kafka
- Apache Flink
- Confluent
- Amazon Kinesis
- Azure Event Hubs
- Apache Iceberg
- Delta Lake
- Trino
Related projects
Where we've done this.
Case studies
Case studies for this service are being prepared. Ask us for references on a call.
FAQ
Frequently asked questions
Data Foundation
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Scope it with a senior practitioner
Book a 45-minute readiness call.
Where you are, what to fix first, and what it would take.