Azure Databricks · Unified Analytics India

Unlock Your Data at Scale with
Azure Databricks

Azure Databricks is the unified analytics platform for data engineering, data science, and ML — combining Apache Spark, Delta Lake, and MLflow on a single collaborative platform. SchwettmannTech builds enterprise data lakehouses on Azure Databricks for Indian organisations: ingesting from SAP, D365, and IoT sources; transforming at petabyte scale; and serving ML features and BI analytics — all on a secure, governed Unity Catalog.

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Azure Databricks Certified Partner India

Data Lakehouse Architecture Delivered

Spark Pipelines for Indian Enterprises

10×

Faster ETL vs traditional data warehouse

Delta

Lake ACID transactions & time travel

Unity

Catalog for data governance & lineage

MLflow

Experiment tracking & model registry

Azure Databricks · Lakehouse Dashboard

Live

4.2TB

Processed Today

↑ Delta Lake

2,840

Spark Jobs

↓ 99.8% Success

12 min

Pipeline P95

↓ 68%

₹0

Idle Waste

↓ Auto-terminate

Delta Lake Lakehouse — Bronze/Silver/Gold

D365 + SAP + IoT ingestion · ACID · Time travel

Running

Spark ETL — 1.2TB Daily Batch

4.2TB processed today · Auto-scaling cluster · 99.8%

Active

MLflow — 14 Models in Registry

Demand forecast · Fraud · Churn · Auto-retraining

Trained

Unity Catalog — Governance Active

Row-level security · Lineage · DPDP tagging

Governed

Databricks Assistant: Delta Lake time travel query identified that the revenue discrepancy originated in a batch job on 2024-11-14. Recommended: add data quality constraint on order_amount column to prevent future NULL propagation.

10×

Faster ETL vs Traditional Data Warehouse

Delta Lake

ACID Transactions & Time Travel

Unity Catalog

Governance, Lineage & Data Classification

MLflow

Experiment Tracking & Model Registry

Delta Lake LakehouseApache SparkBronze Silver GoldUnity CatalogMLflow TrackingFeature StoreAuto LoaderDatabricks SQLDelta SharingPhoton EngineD365 ConnectorSAP IngestionDelta Lake LakehouseApache SparkBronze Silver GoldUnity CatalogMLflow TrackingFeature StoreAuto LoaderDatabricks SQLDelta SharingPhoton EngineD365 ConnectorSAP Ingestion

Services

Azure Databricks Implementation Services

From lakehouse architecture design to production ML pipelines — our certified Databricks engineers build the data foundation for AI-ready Indian enterprises.

Data Lakehouse Architecture

Design and build a medallion architecture (Bronze/Silver/Gold) on Azure Data Lake Storage Gen2 with Delta Lake. Ingest from Dynamics 365, SAP, Oracle, IoT, APIs, and legacy databases using Auto Loader and Delta Live Tables — providing ACID transactions, time travel, and schema enforcement at petabyte scale.

Delta Lake · Medallion Architecture · Auto Loader · Delta Live Tables

Apache Spark Data Engineering

Build production Spark ETL/ELT pipelines in Python/SQL on Databricks — replacing slow, brittle legacy SQL Server Integration Services or Azure Data Factory pipelines. 10–50× faster processing, auto-scaling clusters, and built-in job orchestration with Databricks Workflows.

Apache Spark · Databricks Workflows · Python · SQL · Cluster Auto-scale

Databricks SQL Analytics

Enable business analysts to query the lakehouse directly with Databricks SQL — connecting Power BI, Tableau, or Looker via JDBC/ODBC. Sub-second query latency on Delta tables using Photon engine. SQL serverless warehouses auto-scale to zero — zero cost when not querying.

Databricks SQL · Power BI · Photon · SQL Warehouse · BI Connectivity

ML & MLflow Pipelines

Build ML training pipelines on Databricks using PySpark ML, scikit-learn, XGBoost, and PyTorch — with MLflow for experiment tracking, model versioning, and registry. Deploy models to Databricks Model Serving endpoints or Azure ML for real-time inference from D365 and Power Apps.

MLflow · Model Registry · Feature Store · D365 Integration · Azure ML

Unity Catalog & Governance

Implement Unity Catalog for centralised data governance: column-level permissions, row-level security, data lineage, PII tagging for DPDP Act compliance, and audit logs. Share governed Delta tables across workspaces and with external consumers via Delta Sharing.

Unity Catalog · Row-level Security · Data Lineage · DPDP Tagging · Audit

D365 & SAP Lakehouse Integration

Ingest Dynamics 365 Finance, Sales, and Supply Chain data via Synapse Link for Dataverse into Databricks Delta Lake — enabling advanced analytics and ML on CRM/ERP data that D365 native reporting cannot deliver. SAP extraction via Azure Data Factory or Qlik Replicate.

Synapse Link · Dataverse Export · SAP Extraction · D365 Analytics

Capabilities

Complete Capability Coverage

Our certified team covers every facet of this service — from strategy and implementation to managed operations and continuous optimisation.

🏠Lakehouse

The Databricks Lakehouse

Delta Lake combines the reliability of data warehouses (ACID, schema enforcement) with the flexibility of data lakes (open format, any data type) — eliminating the need for separate warehouse and lake architectures, and the complex pipelines that keep them in sync.

ACID Transactions
Schema Enforcement
Time Travel Queries
Open Delta Format

Performance

Photon Engine Speed

Databricks' native Photon query engine delivers sub-second interactive queries on Delta tables — matching dedicated data warehouse performance on open Delta format files, without proprietary lock-in.

Sub-second Queries
10× vs Spark SQL
No Proprietary Format
Auto-scaling Clusters

🧬ML

Feature Store & ML

Databricks Feature Store centralises ML features computed from your lakehouse data — ensuring training and serving use identical features, eliminating training-serving skew that degrades model performance in production.

🔗Streaming

Real-time Delta Streaming

Auto Loader + Delta Live Tables handles streaming ingestion from Kafka, Event Hubs, and IoT Hub — updating Delta tables in near real-time for dashboards and ML feature pipelines that need fresh data.

Governance

Unity Catalog Security

Column masking for PII, row filters by user role, table-level ACLs, and complete data lineage from source to consumption — all managed in a single Unity Catalog metastore across all Databricks workspaces.

💰Cost

Databricks Cost Control

Auto-terminating clusters, spot instance pools, SQL serverless auto-scale to zero, and cluster policies that prevent engineers from accidentally running expensive clusters. Our FinOps programme typically reduces Databricks costs 35–50% vs unmanaged usage.

India

India Enterprise Data Patterns

We've built Databricks lakehouses ingesting from India-specific sources: GSTIN validation tables, RBI regulatory reporting, Indian fiscal year handling (April–March), and GST invoice data from Tally, SAP, and Oracle ERP systems common in Indian enterprise.

Delivery

Our Databricks Lakehouse Delivery

A structured 6–8 week process to design and deploy your enterprise data lakehouse on Azure Databricks.

Phase 1 — Weeks 1–2

Architecture Design & Source Assessment

Define lakehouse blueprint and source systems

Assess all data sources, volumes, velocity, and downstream consumption patterns. Design medallion architecture, cluster strategy, Unity Catalog structure, and integration patterns with D365, SAP, and other source systems.

Data Source AuditLakehouse DesignUnity Catalog Blueprint

Phase 2 — Weeks 2–4

Ingestion & Bronze Layer

Build all source connectors and raw landing

Implement Auto Loader pipelines for all sources. Configure Delta Live Tables for streaming sources. Build Bronze layer with schema validation and source watermarking. Set up Unity Catalog with initial data classification.

Auto LoaderDelta Live TablesBronze IngestionWatermarking

Phase 3 — Weeks 4–6

Silver & Gold Transformation

Business logic and analytics-ready datasets

Implement Silver layer transformations — deduplication, PII masking, standardisation, and business rule application. Build Gold layer aggregates for BI consumption. Connect Power BI and Databricks SQL.

Silver TransformGold AggregatesPower BI ConnectDatabricks SQL

Phase 4 — Weeks 6–8

ML Pipelines & Governance

MLflow, Feature Store & Unity Catalog hardening

Build ML training pipelines with MLflow experiment tracking. Configure Feature Store for D365-connected ML features. Harden Unity Catalog — row-level security, lineage, DPDP tagging, audit logging.

MLflow PipelinesFeature StoreDPDP GovernanceAudit Logging

Industries

Azure Databricks for Indian Industries

Data lakehouse and ML pipelines built for India-specific data sources, regulatory requirements, and business processes.

BFSI · Regulatory Reports

Healthcare · ABDM Data

Manufacturing · IoT Lake

Retail · Transaction Lake

Telecom · CDR Analytics

EdTech · Learning Data

Infra · Project Analytics

Energy · Smart Meter

India-Specific Databricks Patterns

SchwettmannTech has implemented Databricks lakehouses with India-specific requirements: RBI regulatory reporting pipelines (CRILC, SMA classification), GST reconciliation tables (GSTR-1 vs GSTR-2A matching), April–March Indian fiscal year partitioning, and DPDP Act PII tagging in Unity Catalog. All data processed and stored in Azure India regions for data residency compliance.

Data Platform Impact

Proven Results: Azure Databricks Results

Outcomes from SchwettmannTech's Databricks implementations across Indian enterprises.

10×

Faster ETL processing vs legacy SQL-based pipelines

60%

Reduction in data infrastructure costs vs separate lake + warehouse architecture

Delta Lake

ACID transactions eliminated data quality incidents from concurrent pipeline writes

4 hrs

→ 20 min: RBI regulatory report generation after Databricks lakehouse go-live

Customer Stories

What Our Clients Say

"SchwettmannTech migrated our 8-year, 12TB transaction data warehouse to Azure Databricks Delta Lake. The migration took 10 weeks — our monthly regulatory reports that took 4 hours to generate now run in 18 minutes. We decommissioned an on-premise SQL Server cluster saving ₹45L/year in licensing and hardware costs."

Prashant Kumar

Chief Data Officer · Private Bank, Mumbai

"We process 80 million CDRs daily from our telecom network. SchwettmannTech built our Databricks streaming lakehouse — Auto Loader ingests from Kafka, Delta Live Tables clean and enrich, and our fraud detection ML model scores in near real-time. Fraud detection latency dropped from 4 hours to 8 minutes. The Unity Catalog governance keeps our TRAI regulatory team satisfied with full data lineage."

Neha Rajan

VP Data Engineering · Telecom Operator, Delhi

"Our D365 Finance data needed advanced analytics that native Power BI couldn't deliver. SchwettmannTech connected Synapse Link to export Dataverse tables to Databricks, built Gold layer financial aggregates, and connected Databricks SQL to Power BI. Our CFO now has real-time consolidated P&L across 12 legal entities — something that previously took 3 days of manual spreadsheet work."

Vijay Subramaniam

CFO · Manufacturing Group, Chennai

FAQs

Common Azure Databricks Questions

Planning a data lakehouse or big data platform? Our Databricks architects provide free architecture assessments.

Book Lakehouse Assessment

Azure Databricks excels at: large-scale Spark data engineering (ETL at TB/PB scale), ML model training with MLflow and Feature Store, data science collaborative notebooks, and streaming analytics. Azure Synapse Analytics excels at: T-SQL data warehousing, tightly integrated Power BI, and SQL-first analytics teams. In practice, many Indian enterprises use both — Databricks for complex data engineering and ML, Synapse for SQL-accessible data warehouse layers. SchwettmannTech integrates both in hybrid architectures where each platform handles its strengths.

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark — solving the core problems of data lake unreliability. Without Delta Lake, data lakes suffer from partial writes (pipeline failure leaves corrupt data), no support for updates/deletes (critical for GDPR/DPDP compliance), and schema drift causing downstream failures. Delta Lake's key features: ACID transactions prevent partial writes; time travel enables point-in-time queries and rollback; DML (UPDATE, DELETE, MERGE) enables GDPR deletion compliance; schema enforcement prevents silent data quality degradation. All our Databricks implementations use Delta Lake exclusively.

The primary integration pattern is Synapse Link for Dataverse — Microsoft's feature that continuously exports all Dataverse (D365) entity changes to Azure Data Lake Storage Gen2 in Delta Lake format. SchwettmannTech configures Synapse Link and sets up Databricks Auto Loader to ingest the exported Delta tables, apply Silver layer transformations, and serve Gold layer analytics back to Power BI and Databricks SQL. This enables complex analytical queries on D365 Finance, Sales, Supply Chain, and HR data that the native D365 reporting engine cannot perform — cross-entity joins, historical trend analysis, and ML feature engineering on CRM/ERP data.

Databricks costs have two components: (1) SchwettmannTech implementation — typically ₹15L–₹35L for a full lakehouse build (6–8 weeks) depending on source system count and ML scope; (2) Azure running costs — Databricks DBU (Databricks Unit) charges plus Azure VM and storage costs. A mid-scale enterprise processing 500GB/day with 10 Spark jobs running daily typically incurs ₹1.5L–₹3L/month in Azure Databricks costs. Our FinOps programme usually reduces this by 30–50% through cluster policies, spot instances, and auto-termination — often recovering implementation cost within 6 months.

Yes — ML models trained and registered in Databricks MLflow are deployed as real-time REST endpoints via Databricks Model Serving or exported to Azure ML managed endpoints. Power Apps can call these REST endpoints directly from Power Automate flows — for example, a Canvas App credit application form calls a Databricks-hosted credit scoring model for instant risk scoring. D365 plugins invoke the model API server-side for decisions embedded in CRM workflows. We've built this pattern for BFSI (credit scoring), retail (product recommendation), and manufacturing (predictive maintenance) clients.

Build Your Enterprise Data Lakehouse on Databricks

Book a free Azure Databricks Architecture Assessment. We'll evaluate your data sources, design your lakehouse architecture, and provide an implementation roadmap with TCO modelling.

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