Data Engineering

Data pipelines that power decisions at scale.

End-to-end data infrastructure — from raw ingestion to analytics-ready models — built for teams that need reliable, fast, and cost-efficient data.

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What We Build

Data engineering capabilities

Data Warehousing

Snowflake, BigQuery, and Redshift architectures optimized for your query patterns and cost targets.

Real-Time Streaming

Kafka, Kinesis, and Flink pipelines for sub-second data delivery at any scale.

dbt Transformations

Modular, tested, and documented data models with lineage tracking and CI/CD for your warehouse.

Analytics Engineering

Semantic layers, metrics stores, and self-serve analytics infrastructure for your data teams.

ML Pipelines

Feature stores, training pipelines, and model serving infrastructure for production ML workloads.

Data Governance

Data cataloging, lineage, quality monitoring, and access controls for enterprise compliance.

Case Studies

Data stacks we've built

Supply Chain & Logistics

Logistics Company

SnowflakeKafkadbtAirflow

Challenge

Data was siloed across 12 systems. Analytics ran on stale data 3 days old, making real-time operational decisions impossible.

Solution

Built a unified data platform on Snowflake with Kafka for real-time ingestion, dbt for transformation, and Airflow for orchestration. Reduced data latency from 3 days to 90 seconds.

90s
Data latency (was 3 days)
12→1
Data silos consolidated
3x
Analyst productivity
Advertising Technology

AdTech Platform

ClickHouseOLAPColumnar StorageCost Optimization

Challenge

Processing 2B+ events per day was costing $400k/month on Spark. Query times averaged 45 minutes for standard reports.

Solution

Migrated to ClickHouse for OLAP workloads with a custom partitioning strategy. Implemented columnar storage and materialized views for common query patterns.

78%
Infrastructure cost reduction
45min→8s
Query time improvement
2B+
Events/day processed
Retail & Consumer Goods

Retail Chain

ProphetXGBoostFeature EngineeringMLOps

Challenge

Demand forecasting was manual and inaccurate, causing $8M in annual overstock and stockout losses.

Solution

Deployed an ML-powered demand forecasting pipeline using Prophet + XGBoost, trained on 5 years of POS data with external signals (weather, events, promotions).

$8M
Annual loss reduction
91%
Forecast accuracy
40%
Inventory reduction
Client Testimonials

What Indian clients say

“

Our data was scattered across SAP, Salesforce, and 6 custom databases. Cloudian.IO unified everything into a Snowflake warehouse with real-time Kafka feeds. Our analysts now get answers in seconds, not days.

Suresh Patel
Chief Data Officer, Tata Consumer Products Tech
Mumbai, India
8s
Query time (was 2hrs)
6→1
Data silos unified
4x
Analyst productivity
“

Cloudian.IO built our dbt transformation layer from scratch. The data models are clean, well-tested, and our team can actually maintain them. We went from zero data trust to full confidence in our metrics.

Meera Reddy
Head of Analytics, Swiggy Data Platform
Bengaluru, India
100%
Data test coverage
₹1.8Cr
Annual infra savings
3 wks
Full stack delivery
“

We process 500M events daily from our IoT fleet. Cloudian.IO designed a Kafka + ClickHouse pipeline that handles our peak loads without breaking a sweat — and cut our cloud bill by 62%.

Aditya Bansal
VP Engineering, Ola Electric Data
Bengaluru, India
500M
Events/day processed
62%
Cloud cost reduction
<1s
Stream processing latency

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