Databricks Consulting Services for Data Engineering, Analytics and AI
Cymetrix is a certified Databricks consulting partner with delivery centres in India and client offices in the USA, UK, Poland, and Japan, serving enterprises across global markets. We design, implement and scale Databricks Lakehouse platforms, unifying data engineering, real-time analytics, and AI/ML on a single, governed architecture. With a team of certified Databricks engineers specialising in Delta Lake, Unity Catalog, MLflow, Mosaic AI, and Databricks Workflows, Cymetrix is a trusted Databricks implementation partner for enterprises building AI-ready
data foundations.
We help enterprises migrate from legacy data warehouses to the Databricks Lakehouse, build production-grade data pipelines using Delta Live Tables and Apache Spark, and activate machine learning and generative AI on top of their unified data platform. Whether you are building your first lakehouse, migrating from Snowflake, Redshift, or an on-prem data warehouse, or scaling AI capabilities on an existing Databricks deployment, our Databricks consulting services are designed to accelerate your data transformation at every stage.
What sets Cymetrix apart is our connected data and AI architecture. We combine Databricks with Salesforce Data 360 for customer data unification, Informatica for enterprise-grade master data management, and TextQL Ana for conversational natural language analytics, connecting your lakehouse to your CRM, your marketing and service operations, and your business leaders, not just your data engineering team.
Our Databricks Consulting Services
End-to-end Databricks consulting: from lakehouse architecture and data engineering to ML deployment, governance, and AI activation, built for enterprise scale.
Define the right lakehouse architecture for your data volume, team structure, and AI roadmap before a single line of code is written.
Data estate assessment: inventory of existing schemas, pipelines, and data volumes to benchmark lakehouse readiness
Medallion architecture design: Bronze, Silver, and Gold layer definitions aligned to your business domains
Databricks workspace setup: multi-workspace strategies, networking (VPC/VNET), and enterprise security configuration
Storage architecture: Delta Lake on Azure Data Lake Storage Gen2, AWS S3, or Google Cloud Storage
Databricks Lakehouse Platform sizing: cluster types, node configurations, and auto-termination policies
Technology stack recommendation: Databricks + Fivetran for ingestion + Informatica for data quality + TextQL for analytics
Phased roadmap from raw data ingestion to production ML and AI activation
Build production-grade, scalable data pipelines that move, transform, and unify enterprise data on Databricks at any scale.
Delta Live Tables (DLT) pipeline development: declarative ETL with built-in data quality enforcement and lineage tracking
Apache Spark development: batch and streaming pipeline engineering using PySpark, Scala, and Spark SQL
Auto Loader configuration for continuous, incremental data ingestion from cloud storage with exactly-once guarantees
Structured Streaming pipelines for real-time events from Kafka, Azure Event Hub, and Amazon Kinesis
Fivetran connector setup for pre-built ingestion from Salesforce, SAP, Workday, and 300+ enterprise sources
dbt integration for SQL-based transformation layers on top of Databricks Delta Lake
Pipeline observability: data quality monitoring, SLA alerting, and lineage dashboards
ETL migration from legacy tools (Informatica PowerCenter, SSIS, Talend) to Databricks-native pipelines
Migrate from legacy data warehouses to the Databricks Lakehouse, zero disruption to production analytics, full data validation on delivery.
Migration assessment: full inventory of schemas, stored procedures, views, and query patterns with complexity scoring
Snowflake to Databricks migration: schema translation, Spark SQL rewriting, and performance benchmarking
Redshift to Databricks migration: cluster migration and Redshift Spectrum to Delta Lake conversion
On-premises data warehouse migration: Teradata, Oracle, and SQL Server to Databricks Lakehouse
Legacy ETL modernisation: replace Informatica PowerCenter, SSIS, and Talend with Delta Live Tables pipelines
Zero-downtime migration strategy: parallel running, incremental table cutover, and rollback checkpoints
Post-migration validation: row count reconciliation, query result comparison, and stakeholder sign-off reporting
Deploy ML models and generative AI workloads on the Databricks Lakehouse using MLflow, Mosaic AI, and Databricks Model Serving.
MLflow experiment tracking, model registry, and deployment pipeline setup on Databricks
Databricks Feature Store design: reusable, governed ML features shared across models
Mosaic AI model training: fine-tuning open-source LLMs on proprietary enterprise data
Databricks Vector Search setup for Retrieval-Augmented Generation (RAG) workloads
ML model serving via Databricks Model Serving endpoints with autoscaling
GCP Vertex AI and Azure ML integration with Databricks for hybrid ML environments
AI/ML pipeline orchestration using Databricks Workflows and Apache Airflow
TextQL Ana deployment: conversational natural language querying of lakehouse data, no SQL required
Implement enterprise data governance on Databricks: securing access, tracking lineage, and ensuring data quality across your entire lakehouse.
Unity Catalog setup: metastore configuration, catalog and schema hierarchy design
Row-level and column-level security: fine-grained access controls aligned to business roles
Data lineage tracking: end-to-end visibility from raw ingestion through transformations to ML model outputs
Data classification and tagging: PII identification, sensitivity labelling, and governance policy enforcement
Informatica Data 360 integration: connecting MDM and data quality rules to the Databricks Silver layer
Delta Sharing for secure, governed data sharing across business units and external partners
Audit log configuration and compliance reporting for data access and query history
Connect your Databricks Lakehouse to BI tools, activate SQL analytics for business teams, and surface insights across the organisation.
Databricks SQL warehouse configuration for high-concurrency analytics with serverless and pro compute options
Looker, Tableau, and Power BI connectivity via native connectors and JDBC/ODBC
TextQL Ana deployment: business users query billions of rows in plain English, no SQL or analyst dependency
Semantic layer definition: business-friendly table aliases, calculated metrics, and KPI definitions
Databricks AI/BI Genie setup: natural language data exploration for non-technical users on the Gold layer
Data mart creation within the Gold layer for Sales, Marketing, Finance, Operations, and Customer Success
Keep your Databricks Lakehouse running at peak performance with Cymetrix's ongoing managed services, so your engineers focus on building, not maintaining.
Cluster management, autoscaling configuration, and compute cost optimisation
Delta Lake maintenance, compaction scheduling, and data quality monitoring
Unity Catalog governance: access control updates, schema versioning, and lineage tracking
Databricks Workflows and pipeline job monitoring with incident response
MLflow model tracking and scheduled retraining for deployed ML models
Mosaic AI and Vector Search optimisation for GenAI and RAG workloads
Monthly platform health reports with performance benchmarks and cost analysis
How Cymetrix Powers
Brand Success?
The Cymetrix Connected Stack: Databricks. Salesforce. Informatica. TextQL.
Most Databricks partners deliver data engineering. Cymetrix delivers connected data intelligence: linking your lakehouse to your CRM,
your data quality layer, and your AI query tools in one unified architecture.
Databricks + Salesforce Data 360
We connect your Databricks lakehouse to Salesforce Data 360, enabling real-time customer data unification between your CRM, transaction systems, and product data. Built using Fivetran ingestion, Delta Lake, and bidirectional Salesforce sync. Sales, marketing, and service teams operate from a single AI-enriched customer record.
Databricks + Informatica MDM
Informatica: now part of the Salesforce stack (acquired November 2025), delivers enterprise master data management and data quality enforcement. Cymetrix connects Informatica MDM rules to your Databricks Silver layer so your analytics and ML models run on governed, trusted data, not raw warehouse output.
Databricks + TextQL + Mosaic AI
We deploy TextQL Ana as a conversational analytics layer on top of your Databricks Gold layer, business leaders ask complex questions in plain English and get governed answers without SQL dependency. Combined with Mosaic AI for model training and deployment, this turns your Databricks investment into an AI-active data platform.
Cymetrix Industry Solutions Powered by Databricks
Customer-facing data intelligence across every sector, connecting Databricks Lakehouse to your CRM,
marketing, and customer experience operations.
• Unified Customer 360: consolidating data from banking apps, advisor CRMs, and transaction systems into a single Databricks lakehouse
• Customer churn prediction: ML models on Databricks identifying at-risk retail banking and insurance customers before they lapse
• Next-best-offer recommendation: personalised product suggestions for retail banking, wealth, and insurance customers using Mosaic AI
• Salesforce Data 360 + Databricks pipeline: syncing CRM data with transaction history for unified sales team analytics
• Patient engagement analytics: unifying appointment, interaction, and care journey data for personalised outreach programmes
• Pharma commercial analytics: connecting Salesforce CRM rep activity, HCP interaction data, and product usage for territory performance
• Clinical trial recruitment analytics: identifying eligible patient cohorts from unified data to accelerate trial enrolment
• Provider network analytics: analysing referral patterns and patient flow to improve network performance and care coordination
• Product usage analytics: connecting product telemetry, CRM data, and support tickets for unified customer health scoring
• Customer expansion analytics: ML models predicting upsell and cross-sell based on product usage and CRM signals
• SaaS revenue analytics: ARR, NRR, churn, and cohort analysis pipelines on Databricks with Salesforce as the source of record
• Customer success operations: unified data foundation connecting Salesforce, product usage, and support systems
Dealer and distributor analytics: unifying dealer sales, order pipeline, and CRM data for territory performance and demand forecasting
Customer order intelligence: analysing repeat purchase patterns and order value trends to improve sales and service operations
Warranty and service analytics: connecting claims, service tickets, and customer feedback to identify issues and reduce churn
Salesforce + Databricks integration: syncing CRM account and opportunity data with production data for real-time sales pipeline intelligence
• Customer 360 analytics: unifying e-commerce, loyalty, mobile app, and in-store data in a single Databricks lakehouse
• Personalisation engine: ML-powered product recommendations on Mosaic AI connected to Salesforce Commerce Cloud
• Marketing attribution: multi-touch attribution models across paid, organic, email, and CRM channels using Delta Live Tables
• Customer lifetime value modelling: CLV predictions enabling targeted retention, upsell, and loyalty campaign activation
Why Choose Cymetrix's Databricks
Consulting Services?
Deep expertise. Proven delivery. Industry-specific outcomes.
Certified Databricks Expertise
Certified Databricks engineers and architects delivering lakehouse implementations across BFSI, Manufacturing, Retail, Healthcare, and High-Tech.
Connected Stack Delivery
The only team combining deep Databricks depth with Salesforce Data 360, Informatica MDM, and TextQL, one connected architecture, one partner.
Enterprise-Grade Governance
Every implementation includes Unity Catalog, data lineage, access controls, and Informatica data quality from day one, not retrofitted.
Global Delivery
Engineering centres in Mumbai and Delhi NCR. Client engagement in USA, UK, and Poland. We work to your timezone.
Power Your Data Projects with On-Demand Databricks Talent
Whether you need to scale a data engineering team rapidly, fill a Databricks architect gap on a live project, or build in-house data platform capability without permanent headcount, Cymetrix's on-demand model gives you access to certified Databricks engineers, data architects, and ML engineers on flexible terms, integrated directly into your team, tools, and delivery cadence.
Voices of Trust and Partnership
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FAQs
Ready to Build Your AI-Ready Data Lakehouse?
Whether you are starting from scratch, migrating from a legacy data warehouse, or activating ML and AI on an existing Databricks deployment, Cymetrix has the architecture expertise, delivery track record, and connected stack to take you there. Talk to our Databricks consulting team and tell us where you are in your data journey.
Allied For Success: Our Partnerships
We partner with global technology leaders across CRM, cloud data, AI and integration ecosystems to strengthen our enterprise delivery model.
