01
Pipelines & ELT
Reliable ingestion from SaaS, databases, and files with tested transformations and lineage.
Data
The plumbing your AI depends on.
ELT pipelines, lakehouse architecture, streaming, and governance — the unglamorous engineering that makes trustworthy analytics and AI possible.
What we build
02
Lakehouse architecture
Bronze-silver-gold layers on Databricks or Fabric — one platform for BI and ML.
03
Streaming with Kafka
Event-driven pipelines for real-time dashboards, alerts, and operational systems.
04
Data quality & governance
Contracts, tests, and cataloging so every metric has an owner and a definition.
How we deliver
Step 01 — Assess
Use-case discovery, data and platform readiness, and a business case with measurable outcomes for data engineering.
Step 02 — Build
Senior-led delivery in weekly increments — architecture, security, and quality gates baked into every sprint.
Step 03 — Operate
Production monitoring, SLAs, and continuous improvement through our managed services team in Chennai.
Tools & platforms
DatabricksMicrosoft FabricSnowflakeKafkadbtAirflowPythonSpark
In depth
Data engineering: the unglamorous work that makes AI possible
Every analytics dashboard and every AI model is downstream of data engineering. Pipelines that break silently, definitions that drift between teams, and warehouses full of stale extracts are why data initiatives stall. We build the governed, observable data platforms that make everything downstream trustworthy.
Our builds center on lakehouse architectures — Microsoft Fabric, Databricks, Snowflake — fed by batch and streaming ingestion (Kafka, CDC) and organized in medallion layers with dbt-style transformations. Data contracts, lineage, and quality tests run in CI, so a broken source fails loudly in development, not quietly in a board report.
The same governed layer feeds BI dashboards, machine learning features, and RAG retrieval for LLMs — one platform, many consumers, no parallel truths.
Contracts and lineage
Schema contracts and end-to-end lineage make every metric traceable to its source.
Quality tested in CI
Data tests run like software tests — failures block deployment, not decisions.
One platform, many consumers
BI, ML, and GenAI read from the same governed layer.
Frequently asked questions
Have a different question? Talk to an engineer, not a salesperson.
Our reports disagree with each other — can you fix that?
Yes — that is usually a modeling and governance problem, not a tooling one. We build a single semantic layer with owned definitions.
Batch or streaming?
Batch unless a decision genuinely needs sub-minute data. Streaming has real costs; we recommend it only where latency earns money.
How do you hand over?
Everything is code — versioned, documented, and tested — with runbooks and training so your team owns the platform.