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Cloud Data & Analytics Platforms Built for Indian Enterprises
Cloud data and analytics services design, build and run the platforms that turn operational data into trusted reports, live operational views and predictions. NTSPL covers twelve capabilities, from data strategy and CDC ingestion through warehouses, lakehouses, streaming, BI and ML, to governance, DPDP-aligned privacy and analytics FinOps, on AWS, Azure and Google Cloud in Indian regions.
Cloud Data & Analytics services
Cloud Data & Analytics at a Glance
Indian enterprises rarely lack data; they lack data they can act on the same day and defend in front of an auditor. Typical symptoms are a board pack with two versions of revenue, a core system that cannot be queried in business hours, dashboards nobody opens, and a cloud bill for analytics that grew faster than its users. Each is a different problem with a different fix, which is why we split this family into twelve focused services rather than one platform pitch.
Every engagement follows the same principles: start from the decision the data must support, keep one governed copy of each entity in open formats, keep personal data tokenised and inside Indian regions, and measure success in business terms such as faster closes, fewer stock-outs or recovered claims. You can engage us for a single capability or a sequenced programme across all four pillars.
Four Pillars of the Cloud Data & Analytics Practice
Plan and modernise
Decide the target state before buying tools, then exit legacy platforms safely. Strategy work maps decisions to domains, owners and a costed roadmap; modernisation moves Teradata, Netezza, Oracle, SQL Server warehouses and Hadoop clusters to cloud platforms with automated code conversion and reconciled parallel runs.
Data Strategy & Architecture
Define data domains, choose lake, warehouse or lakehouse, set the operating model and a costed 18-month analytics roadmap on AWS, Azure or Google Cloud.
Data Modernization & Migration
Move Teradata, Netezza, Oracle or SQL Server warehouses and Hadoop to BigQuery, Redshift, Fabric or a lakehouse with code conversion and parallel runs.
Build the data platform
Engineer the layers every analytics use depends on: log-based CDC and event ingestion with data contracts, zoned object-storage lakes, modelled warehouses with certified marts, and lakehouses on Iceberg or Delta that give BI and ML one governed copy of each entity.
Data Integration & Ingestion
Change data capture, SaaS connectors and event ingestion from ERP, core and loan systems into your cloud lake or warehouse, with replay and data contracts.
Cloud Data Lake
Zoned, catalogued data lakes on S3, ADLS Gen2 or Cloud Storage with open formats, lifecycle tiering and tag-based access for log and semi-structured data.
Cloud Data Warehouse
Dimensional models, tested ELT in dbt or Dataform and isolated compute on Redshift, Fabric Warehouse, BigQuery or Snowflake for finance-grade reporting.
Lakehouse Architecture
One copy of data for BI and ML: Iceberg or Delta tables with ACID upserts, medallion layers and a shared catalogue on Databricks, Fabric, AWS or BigQuery.
Deliver insight and prediction
Put data in front of the people who act on it: streaming pipelines for decisions that lose value within minutes, semantic models and role-based dashboards that managers actually open, and forecasting, churn and propensity models deployed with MLOps and measured against holdout groups.
Real Time & Streaming Analytics
Sub-minute analytics on event streams with Kafka, Flink, Dataflow or Event Hubs: windowed KPIs, live alerts and ops dashboards sized for Indian peaks.
Business Intelligence & Dashboards
Governed semantic models, KPI trees and role-based dashboards in Power BI, Looker or Amazon Quick Sight, designed around the decisions each manager makes.
Predictive Analytics & ML
Demand forecasts, churn and propensity models on governed data, deployed with MLOps on SageMaker AI, Azure Machine Learning or Google Cloud, and monitored.
Govern, protect and optimise
Keep data trustworthy, lawful and affordable: critical-data-element governance with lineage and quality scorecards, tokenisation, masking and consent filters aligned to the DPDP Act, and query-level FinOps that tunes slots, capacity units, clusters and table design so platform spend tracks the value each team gets.
Data Governance & Quality
Business glossary, catalogue, column-level lineage, stewardship and automated data quality scorecards on Purview, Dataplex or SageMaker Catalog.
Data Security & Privacy
Tokenise and mask personal data in lakes and warehouses, enforce row and column security, and bind analytics to DPDP Act purposes, consent and erasure.
Analytics FinOps
Cut BigQuery, Redshift, Fabric, Databricks and Snowflake spend through capacity right-sizing, partitioning, query tuning and per-team guardrails.
Business Outcomes Our Data Programmes Are Measured On
How We Deliver Data & Analytics on Each Hyperscaler
On AWS we centre platforms on Amazon S3 with Apache Iceberg (including S3 Tables), AWS Glue and Lake Formation for catalogue and access, Redshift for warehousing, Kinesis, MSK and Managed Flink for streaming, Amazon Quick Sight for BI and SageMaker AI for ML. Mumbai is primary, Hyderabad hosts DR copies.
On Azure we build on Microsoft Fabric and OneLake for organisations invested in Microsoft 365 and Power BI, and Azure Databricks with Unity Catalog for heavy engineering and ML. Microsoft Purview governs catalogue, lineage and quality. Deployments sit in Central India (Pune), with South India (Chennai) for resilience.
On Google Cloud we converge lake and warehouse in BigQuery with BigLake tables over Cloud Storage, use Datastream, Pub/Sub and Dataflow for ingestion and streaming, Dataplex Universal Catalog for governance, Looker for semantic modelling and BigQuery ML or Gemini Enterprise Agent Platform for models, in Mumbai and Delhi.
Cloud Data & Analytics in Practice: Industry Scenarios
Two-wheeler lender sees EMI bounces by 11 a.m. instead of the next morning
OTT platform cuts lake query scans 86% and decides content renewals on watch data
FMCG distributor settles brand scheme claims in 9 days instead of 47
Steel plant traces every coil defect back to heat chemistry within 20 minutes
Ride-hailing app rebalances driver supply every 30 seconds across 2,400 city zones
Cloud Data & Analytics: Frequently Asked Questions
Which data and analytics service should we start with?
Start with Data Strategy & Architecture if more than one business unit or platform is involved, or if KPIs are disputed. Start directly with a build service, such as a warehouse, lakehouse or ingestion, when the target platform is already agreed and one team owns the outcome. Analytics FinOps can run in parallel with anything.
Do you work with only one cloud provider?
No. We deliver on AWS, Azure and Google Cloud, and on Snowflake and Databricks across them. We usually recommend one primary cloud for data, aligned with where your ERP and identity already live, and share curated data across clouds only where a clear business case exists.
How do you keep analytics data compliant in India?
Data platforms are deployed in Indian regions, personal data is tokenised at ingestion, columns carry policy tags for masking, and each dataset records its permitted DPDP purposes. Logs are retained for 180 days to align with CERT-In directions, and RBI, SEBI or IRDAI requirements shape residency and audit evidence.
How are engagements priced?
Assessment and strategy work is usually fixed-price over 6-8 weeks. Build services are priced per release, typically 8-16 weeks each, with an agreed scope of sources, tables or models. Cost-tuning sprints are fixed-fee and often paid back within a quarter. Managed operations are available on a monthly retainer.
Can NTSPL run the platform after it is built?
Yes. We can operate pipelines, monitor freshness and quality, handle schema changes, manage capacity and run monthly governance and cost reviews under a managed services agreement, or train your team and hand over runbooks, code repositories and dashboards so you run it in-house.
How do your data services connect to generative AI work?
Generative AI applications depend on the same governed data, catalogues and access controls. Our data services provide those foundations, while our Generative AI family builds assistants, conversational analytics and AI data preparation on top, so both share one security and governance model.