Home Databricks

Give Your Data the Power to Drive Business Forward

Governed and AI-Ready Lakehouse Solutions

Indium combines data strategy, Lakehouse engineering, data governance, and quality engineering with the Databricks Data Intelligence Platform. Together, they help enterprises modernize data environments with business-aware agents. Backed by people who have done it before.

Databricks Expertise, Backed by Engineering

Our shared mission is to help customers innovate faster with governed, scalable Lakehouse solutions. We focus on improving data engineering velocity, platform reliability, and cost efficiency.

Partnership timeline

5+ Years

Tier

Bronze Tier

Together, We Turn Data Ambitions into Outcomes

Lakehouse migration and modernization

Data engineering and pipelines

Governance and data quality

Analytics and BI enablement

Agentic AI/GenAI and ML engineering

DataOps, MLOps, and managed operations

Industry-led vertical solutions

Databricks

Lakehouse Architecture

Brings Lakehouse architecture together in a unified data platform.

Data Governance

Supports governance across enterprise data environments.

AI Tooling

Provides tools to support enterprise AI initiatives.

Indium

Data Strategy and Architecture

Defines data strategy and architecture for enterprise needs.

Data and AI Engineering

Covers data engineering, AI engineering, and migration for production outcomes.

Quality and Managed Operations

Combines quality assurance with managed operations, backed by reusable reference architectures and proprietary accelerators.

Delivery Experience

Years of Databricks experience

0 +

Enterprise deployments

0 +

Industries served

0 +

Databricks Expertise

Certified and experienced professionals

0 +

Databricks badges

0 +

Take Your Data Initiatives Further

When data gets complex, solutions need to be specific. Each solution is designed around a distinct use case, with a focus on making enterprise data more useful and ready for business applications.

Databricks-Based Gaming Data Platform

Unifies gaming data through scalable ETL pipelines and reconciliation services. Centralized governance and auto-scaling compute enable low-latency reporting and AI-driven personalization.

Data Lifter + Databricks (Industry Agnostic)

Uses AI agents, AST parsing, Graphify-generated graphs, and context graphs to migrate legacy databases and code to PySpark. Includes deterministic verification and human approval.

Data Lifter + Databricks (Retail)

Applies AI agents and custom MCP tools to assess retail data estates. Context graphs inform AI-assisted PySpark conversion, followed by deterministic verification and human approval.

Ontology Workbench (Industry Agnostic)

Converts Unity Catalog tables, data dictionaries, and documents into governed business ontologies through AI-assisted drafting. Domain experts refine the ontology before metric views are deployed for Genie and custom agents.

Ontology Workbench for Databricks (Retail)

Creates a governed retail ontology from Unity Catalog sales, customer, product, store, and returns data. AI drafts the ontology, which merchandising and data experts refine before metric views are deployed for Genie and custom agents.

ibriX – Databricks Implementation Accelerator

Supports Databricks implementation from data processing to model deployment. AI-enhanced implementation incorporates security, governance, and scalability.

Your Data Platform, Built for the Long Run

Faster Databricks Adoption

Reusable frameworks and proprietary accelerators shorten Lakehouse assessment, migration, modernization, and release cycles. This brings data and AI investments closer to production and reduces the time required to realize business value.

Reduced Migration Risk

Better visibility into legacy dependencies and lower manual reverse-engineering effort reduce the chances of rework during migration. Enterprises can move to Databricks with governed data they can trust and a clearer understanding of their existing environments.

Scalable Performance and Reliability

Databricks performance supports growing enterprise data workloads. Reliable managed operations help maintain platform stability from initial assessment through production.

Cloud Cost Optimization

Greater visibility into Databricks compute consumption reveals opportunities to optimize resource usage. It helps control the operational costs associated with data modernization.

Real Stories, Real Impact

Modernizing Legacy Python Workloads to PySpark

Client Challenge

Undocumented Python code and scattered business logic increased manual analysis effort. Legacy dependencies made modernization risky, while late compliance checks increased the risk of non-compliant SEMS code.

The Indium Approach

Indium used its Data Lifter modernization accelerator to convert Python workloads to PySpark on Databricks. AST analysis and dependency graphs supported AI-assisted conversion, followed by code validation, remediation, and developer review.

Reduction in manual modernization effort

0 %

The Outcome

2x faster modernization cycles and 60–70% less legacy code analysis effort.
More than 4 code-quality compliance checks embedded, with 4,000+ lines of independent Python code addressed per migration.

Partner Certifications & Recognition

Associate and Professional Certifications
Data, generative AI, and context engineers certified on the Databricks platform.

0 +

Databricks Delivery Experts

0 +

Databricks Partner Champions in the Making

Something Big on Your Roadmap? Let's Talk.

Build or modernize your next Databricks Lakehouse, data engineering, or AI solution with confidence.