Big Data Analytics & Engineering
We design, build and operate big-data platforms that turn raw event firehoses into governed, query-ready, AI-ready datasets, with the latency, cost and reliability your roadmap actually needs.
Pipelines that get cheaper as they get bigger.
Most big-data platforms get more expensive every quarter as data volumes grow, the bill compounds faster than the insight does. Our approach inverts that. We build on lakehouse foundations where storage is cheap, compute is elastic and the architecture pays you back as you scale. Streaming where it matters, batch where it does not and ruthless cost discipline at every layer.
Lakehouse-first
Open formats (Iceberg, Delta), open compute (Spark, Trino, DuckDB) and decoupled storage. Lock-in becomes a choice, never a default.
Streaming where it matters
Real-time for revenue-critical signals, batch for everything else. We do not over-engineer, but we never under-engineer either.
Cost as a first-class metric
Per-pipeline, per-team chargeback dashboards from day one. Engineers see the price of every query they write.
Why big-data spend is on every CFO's 2026 review.
Three forces are reshaping the economics of big data and the teams adapting are pulling away from those running yesterday's playbook.
Volumes are doubling every 18 months while budgets aren't. The teams that win make the curve bend, not just absorb it.
A 2025 Databricks study found that two-thirds of warehouse compute serves dashboards no one opens. Lakehouse architectures expose and reclaim that waste.
AI projects are choking on disconnected silos. Production-grade big-data platforms are now the gating dependency for the whole AI roadmap.
Big Data Analytics services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Streaming and batch pipelines
Kafka, Flink, Spark Structured Streaming and Airflow, end-to-end pipelines with sub-second SLAs alongside daily reconciliation jobs.
Lakehouse architecture
Iceberg, Delta or Hudi on S3, GCS or ADLS. ACID semantics, time travel, schema evolution and cheap storage in a single substrate.
Real-time analytics engines
ClickHouse, Druid, Pinot or DuckDB tuned for sub-second OLAP at scale. Dashboards stop timing out, even on billion-row tables.
Data quality and contracts
Great Expectations, Soda, dbt tests and schema contracts wired into CI. Data breakages caught at the boundary, not in the dashboard.
Cost and performance tuning
Partition strategy, Z-ordering, compaction policies, query rewriting. Snowflake and BigQuery bills routinely cut 40-60% within 90 days.
Multi-tenant data platforms
Workspace isolation, fine-grained access control, chargeback dashboards and quota enforcement, ready for enterprise scale from day one.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Cut event-pipeline latency from 4 hours to 12 seconds
Replaced a Spark-batch RTB pipeline with a Flink streaming job on Iceberg, advertisers saw bid-quality dashboards refresh in real time.
Six reasons enterprises run Big Data Analytics with Infivit.
Built for the 2026 reality of Big Data Analytics: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic data buzzwords.
Sub-second pipelines, not nightly batch.
Kafka, Flink and Pulsar pipelines with sub-second SLAs replace yesterday's ETL drag. Decisions and AI features run on data that is seconds old, not 18 hours stale.
60% lower warehouse spend.
Iceberg, Delta and Hudi architectures plus aggressive query optimization. Cut Snowflake and BigQuery bills in half without sacrificing performance or governance.
No vendor lock-in, ever.
Open table formats, open compute engines, your data in your buckets. Switch warehouse vendors as a refactor, never as a rewrite.
Data contracts gate every release.
Great Expectations, Soda and dbt tests run in CI. Producers and consumers agree in writing, breakages caught at the boundary, never in the boardroom dashboard.
Column-level lineage across 200+ tools.
When a number changes, you know exactly why and what depends on it. Audit trails that satisfy DPDP, GDPR and your CFO simultaneously.
Pipelines run like services, not scripts.
SLOs, error budgets, on-call rotations and post-incident reviews. Your data platform earns the same uptime your APIs already do.
The questions you were already going to ask.
Got a big data analytics problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.