Helping Enterprises Modernize Data, Accelerate AI, and Maximize Business Value.

Data Engineering
& Databricks Development
Services

We design, build, and manage Lakehouse data platforms on Databricks turning fragmented data into governed, AI-ready pipelines that power analytics, ML, and real-time decision-making.

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What We Do

End-to-End Databricks & Data Engineering Services

From raw, siloed data to a governed Lakehouse we handle architecture, migration, pipelines, and the AI layer on top.

Lakehouse Architecture & Setup

Design and deploy Databricks Lakehouse environments on AWS, Azure, or GCP, built around Delta Lake and Unity Catalog for unified governance.

Data Pipeline Engineering (ETL/ELT)

Build scalable batch and streaming pipelines with Apache Spark and Lakeflow to ingest, clean, and transform data from any source.

Legacy & Cloud Data Migration

Move workloads off legacy warehouses (Hadoop, Redshift, on-prem SQL) onto Databricks with minimal downtime and validated data parity.

Data Governance & Security

Implement fine-grained access control, lineage tracking, and compliance-ready governance using Unity Catalog.

MLOps & AI Model Enablement

Operationalize machine learning with MLflow on Databricks from feature engineering to model deployment and monitoring.

BI & Analytics Integration

Connect Databricks SQL to Power BI, Tableau, or Looker for real-time dashboards and self-serve analytics.

Genie Agents & Conversational Analytics

Build and configure Databricks Genie Agents so business teams can ask data questions in plain English and get governed, Unity Catalog-grounded answers no SQL required.

What We Do

End-to-End Databricks & Data Engineering Services

From raw, siloed data to a governed Lakehouse we handle architecture, migration, pipelines, and the AI layer on top.

Lakehouse Architecture & Setup

Design and deploy Databricks Lakehouse environments on AWS, Azure, or GCP, built around Delta Lake and Unity Catalog for unified governance.

Data Pipeline Engineering (ETL/ELT)

Build scalable batch and streaming pipelines with Apache Spark and Lakeflow to ingest, clean, and transform data from any source.

Legacy & Cloud Data Migration

Move workloads off legacy warehouses (Hadoop, Redshift, on-prem SQL) onto Databricks with minimal downtime and validated data parity.

Data Governance & Security

Implement fine-grained access control, lineage tracking, and compliance-ready governance using Unity Catalog.

MLOps & AI Model Enablement

Operationalize machine learning with MLflow on Databricks from feature engineering to model deployment and monitoring.

BI & Analytics Integration

Connect Databricks SQL to Power BI, Tableau, or Looker for real-time dashboards and self-serve analytics.

Genie Agents & Conversational Analytics

Build and configure Databricks Genie Agents so business teams can ask data questions in plain English and get governed, Unity Catalog-grounded answers no SQL required.

Featured Capability

Genie Agents: Self-Serve Analytics for Business Teams

Instead of routing every ad-hoc business question through an analyst, Genie Agents let non-technical users ask questions in plain English and get answers generated from your governed Unity Catalog data with the SQL, chart, and reasoning shown alongside the answer. Each agent is scoped to a domain (e.g. a “Revenue Agent” or “Supply Chain Agent”), with shared memory, curated instructions, and Unity Catalog permissions baked in, so answers stay within what that team is allowed to see.

Domain-scoped agents (Revenue, Supply Chain, Support, etc.) built around a tight, curated set of tables

Trusted example queries to anchor accuracy before wider rollout

Genie One chat interface unifying Genie Agents, dashboards, and saved queries in one place

Plain-English curator instructions defining metric definitions and correct join logic

Unity Catalog permissions enforced automatically on every answer

Pay-as-you-go usage billed in DBUs, with admin-level budget controls

Platform Capabilities

Why Databricks as Your Data Foundation

A unified platform that replaces disconnected data warehouses, lakes, and ML tooling with one governed system.

Unified batch + streaming data processing on Apache Spark

Unity Catalog for centralized data governance & lineage

Auto-scaling compute to control infrastructure cost

Delta Lake for ACID transactions on top of cloud storage

Native MLflow support for the full ML lifecycle

Works across AWS, Azure, and Google Cloud

Our Process

How We Deliver a Databricks Engagement

01.
Data & Workload Assessment

Audit existing data sources, pipelines, and pain points to define scope, cost, and target architecture.

02.
Lakehouse Architecture Design

Blueprint the Databricks environment workspace setup, catalog structure, storage layout, and security model.

03.
Pipeline & Migration Build

Develop ingestion and transformation pipelines, migrate historical data, and validate against source systems.

04.
Testing & Governance Rollout

Apply access controls, run data quality checks, and validate performance under real workloads.

05.
Deployment & Ongoing Support

Go live with monitoring, cost optimization, and continued support as data volumes and use cases grow.

Industries We Serve

Data Engineering Across Sectors

Finance & Banking

Fraud detection pipelines & real-time risk analytics.

Healthcare

Unified patient data for predictive diagnostics.

Retail & eCommerce

Customer 360 pipelines for personalization at scale.

Manufacturing

IoT sensor data pipelines for predictive maintenance.

Logistics

Streaming data for demand forecasting & routing.

Automotive

Telemetry data pipelines feeding ML models.

Tech Stack

Tools We Pair With Databricks

Databricks

Apache Spark

Delta Lake

MLflow

PostgreSQL

MySQL

MongoDB

AWS

Google Cloud

Microsoft Azure

Contact us

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    FAQ

    Data Engineering & Databricks Services – FAQ

    Do you migrate existing data warehouses to Databricks?

    Yes we handle migrations from legacy warehouses and Hadoop clusters onto Databricks Lakehouse, with data validated against the original source before cutover.

    Can Databricks work alongside our existing databases?

    Yes. Databricks integrates with PostgreSQL, MySQL, MongoDB, and other operational databases as data sources feeding into the Lakehouse.

    How long does a typical Databricks implementation take?

    Timelines vary by data volume and complexity — a focused pipeline project can take a few weeks, while a full platform migration typically spans a few months.

    Do you support ongoing management after go-live?

    Yes, we offer ongoing pipeline monitoring, cost optimization, and platform support as part of our dedicated development team engagements.

    What is Databricks Genie, and is it different from Genie Spaces?

    Genie is Databricks' conversational analytics layer it lets users ask data questions in plain English and get answers grounded in your Unity Catalog data. "Genie Spaces" was renamed to Genie Agents in July 2026 as Databricks expanded the feature into a broader agent platform (Genie One, Genie Code, Genie App Builder, and others). The core idea is unchanged: scoped, governed, natural-language access to your data.

    How accurate are Genie Agent answers, and how do you make sure they're correct?

    Accuracy depends heavily on setup, not just turning the feature on. We scope each agent to a tight set of tables, write clear curator instructions defining your actual metric definitions and join logic, and validate answers against known-good results during a pilot period before rolling it out more broadly.

    Can Genie Agents only see data that specific teams are allowed to access?

    Yes. Genie Agents run on top of Unity Catalog, so the same row- and column-level permissions you've already defined are enforced automatically on every question the agent answers no separate access layer to maintain.

    How is Genie usage billed?

    Genie runs on a pay-as-you-go model: each user gets a monthly free allowance of usage, and anything beyond that is billed in Databricks Units (DBUs). Admins can set budgets to monitor and cap spend at the account level.

    Do we need a full Lakehouse migration before we can use Genie Agents?

    You need the relevant data available in Unity Catalog, but that doesn't require migrating everything at once. We often start with a narrow, high-value dataset enough for one working Genie Agent and expand the Lakehouse footprint alongside it as the pilot proves out.