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Data Engineering & Analytics

Transform Raw Data into Actionable Business Intelligence

Data is the new oil — but only if it's refined. We build end-to-end data pipelines, warehouses, and analytics dashboards that turn your scattered data into real-time insights your leadership team can actually use to make faster, smarter decisions.

50+

Data Pipelines Built

10TB+

Data Processed Daily

85%

Decision Speed Gain

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Our Comprehensive Solutions

End-to-end services designed to deliver measurable business outcomes — not just deliverables.

Data Pipeline Engineering

Real-time and batch ETL/ELT pipelines using Apache Spark, Airflow, Kafka, and dbt — ingesting from 50+ sources into a single source of truth.

Data Warehouse Architecture

Snowflake, BigQuery, Redshift, or Databricks Lakehouse — we design your warehouse for query performance, cost optimization, and future scale.

BI Dashboard Development

Looker, Tableau, PowerBI, or custom dashboards — beautiful, real-time visualizations that answer business questions at a glance, not after a SQL query.

Machine Learning Pipelines

Feature engineering pipelines, model training infrastructure, MLflow for experiment tracking, and model serving with sub-100ms inference latency.

Cloud Data Infrastructure

AWS, GCP, or Azure data stack — IAM security, VPC networking, cost monitoring, and auto-scaling infrastructure managed as code via Terraform.

Data Quality & Governance

Automated data validation with Great Expectations, data lineage tracking, cataloging with Atlan/DataHub, and GDPR/DPDP Act compliance automation.

Case Studies & Success Stories

Real results delivered for real clients across India

Case Study

FinTech: Real-Time Fraud Detection Pipeline

Problem: A payment processor was losing ₹2 Cr/month to fraud because their batch analysis ran only once daily — fraudsters had a 24-hour window.

Solution: Built a real-time streaming pipeline with Kafka, Flink, and a trained ML model deployed on SageMaker — fraud detection under 50ms per transaction.

Result: Fraud losses dropped 85% within 30 days. False positive rate under 0.1%. The system now processes 5M transactions/day with sub-second latency.

85%

Fraud Reduction

<50ms

Detection Time
"Saral's data team understood our fraud patterns better than our own risk analysts. The real-time pipeline paid for itself in 3 weeks."— Ravi M., CRO — Mumbai
Case Study

Healthcare: Unified Patient Data Platform

Problem: A hospital chain had patient data siloed across 8 different systems — no single view of patient history, duplicate tests, and ₹5 Cr in annual waste.

Solution: Built a data lake on AWS with S3, Glue ETL, Redshift for analytics, and a unified patient 360 dashboard. HL7/FHIR compliant with full data governance.

Result: Duplicate tests reduced 60%. Patient wait time dropped 35%. Unified analytics helped identify ₹8 Cr in operational savings in the first year.

60%

Fewer Duplicate Tests

₹8 Cr

Op Savings
"For the first time in 15 years, our doctors can see a patient's complete history in one screen. Clinical decisions improved dramatically."— Dr. Lakshmi P., CIO — Chennai

Our Proven Process

How we consistently deliver exceptional results

1

Data Audit & Strategy

We map every data source, assess quality, identify gaps, and create a phased roadmap — quick wins first, complex transformations later.

2

Infrastructure Setup

Cloud data stack provisioning, security configuration, networking setup, and access controls — GDPR/DPDP Act compliant from day one.

3

Pipeline Development

ETL/ELT pipelines, data modeling, warehouse schema design, and dashboard development — all tested with your actual data, not sample datasets.

4

Enablement & Handover

Your team gets documentation, runbooks, access credentials, and 2 weeks of hands-on training — you own your data, we just build the foundation.

Why Choose Saral Groups?

What sets us apart from traditional agencies and freelancers

Real-Time or Batch — You Choose

Kafka streaming for sub-second latency or Airflow+dbt for cost-efficient batch — we architect based on your actual latency requirements, not tech hype.

Privacy & Compliance First

GDPR, India DPDP Act 2023, HIPAA — our data architectures include privacy by design. PII masking, encryption at rest, and audit trails built in.

Cost-Optimized from Day 1

We've seen too many ₹50L/month data bills. We design for cost efficiency with query optimization, partition pruning, and spot instance usage from the start.

Your Team, Empowered

We don't build black boxes. Every project includes documentation, training, and 30 days of handholding — your team runs the stack independently after we leave.

Client Testimonials

Trusted by businesses across India — here is what they say

★★★★★

"Our data was scattered across 12 systems. Saral built a unified data warehouse in 8 weeks. Our leadership now makes decisions on real-time dashboards instead of 2-week-old Excel reports."

Suresh G.
Suresh G.CTO, Logistics — Bangalore
★★★★★

"The Kafka pipeline Saral built processes 2M events per minute without breaking a sweat. Our engineers learned more in 3 months working with their team than in 2 years on their own."

Priya K.
Priya K.Head of Data — FinTech, Mumbai
★★★★★

"We were spending ₹8L/month on BigQuery with 80% wasted queries. Saral optimized our data model and reduced the bill to ₹2.2L/month while making queries 3x faster."

Amit S.
Amit S.Engineering Director, E-Commerce — Delhi

Ready to Get Started?

Book a free consultation with our experts. No commitment, no pressure — just honest advice on how we can help your business grow.

Schedule Free Consultation → 📞 +91 79883 75156

Expert Insights

Actionable knowledge from our team of specialists

Data Stack

The Modern Data Stack for Indian Startups: A Practical Guide (2026)

dbt, Airbyte, Snowflake, Looker — what to adopt at each stage from seed to Series C, with real cost benchmarks from 30+ implementations.

Real-Time

Kafka vs Kinesis vs Pub/Sub: Choosing a Streaming Platform

Throughput, latency, operational complexity, and cost comparison across the three major streaming platforms for different use cases.

Data Governance

India's DPDP Act and Your Data Stack: A Compliance Checklist

What the Digital Personal Data Protection Act 2023 means for your data engineering — consent management, data deletion, and audit requirements.

Frequently Asked Questions

We're drowning in data but can't use it. Where do we start? + This is the most common problem we solve. We start with a data audit — mapping every data source (databases, APIs, spreadsheets, logs), assessing data quality, and identifying the highest-value use cases. Then we build a centralized data warehouse and the pipelines to feed it. Within 4-6 weeks, you'll have clean, queryable data and your first dashboard.
What's the difference between a data warehouse and a data lake? + A data warehouse stores structured, processed data optimized for querying and reporting (think: clean tables ready for BI tools). A data lake stores raw data in its native format (JSON, logs, images) and is better for data science and ML use cases. We typically recommend a warehouse-first approach, adding a lake when you're ready for advanced analytics.
How do you ensure data quality in the pipelines? + We implement automated data quality checks at every pipeline stage: schema validation, null/missing value detection, duplicate identification, range and format checks, and referential integrity validation. Failed checks trigger alerts and can automatically quarantine bad data before it pollutes your warehouse. We also maintain data lineage tracking so you always know where data came from.
How do you handle real-time data vs batch processing? + For real-time needs (fraud detection, live dashboards, instant alerts), we use Apache Kafka or AWS Kinesis for streaming. For batch processing (daily reports, ML training data), we use Airflow or dbt with scheduled runs. Most clients use a hybrid — real-time for operational needs, batch for analytical workloads. We design the right mix for your use case.
What cloud platforms do you work with? + We work across AWS (Redshift, Glue, Kinesis, S3), GCP (BigQuery, Dataflow, Pub/Sub), and Azure (Synapse, Data Factory). We recommend based on your existing infrastructure, budget, and team expertise. Multi-cloud and hybrid (on-prem + cloud) architectures are also supported.
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