Engineering Enterprise Data for the AI Era

Palni brings data engineering, migration, governance and AI expertise together to help enterprises move from complex data environments to trusted, AI ready platforms.

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What Databricks Can Unlock for Your Enterprise

A modern data foundation makes enterprise information easier to trust, easier to use and more useful for timely decisions.

Faster Modernisation

Move beyond legacy data platforms with a planned path to a modern environment.

Trusted Data

Build governed, secure and reliable data that teams can use with confidence.

Real Time Intelligence

Turn data into timely information that supports faster decisions.

AI at Scale

Move suitable AI use cases from pilots towards production with the right data foundations.

Lower Data Complexity

Simplify fragmented data environments and reduce unnecessary platform friction.

Data Engineering

Build scalable lakehouse foundations with engineering practices that make enterprise data reliable, accessible and ready for downstream use.

Lakehouse Architecture

Design scalable data foundations that support data engineering, analytics and AI workloads.

Spark and Delta

Use the right Spark and Delta Lake capabilities to build and manage dependable data processing.

Lakeflow Pipelines

Develop and manage data pipelines with repeatable processing, monitoring and quality checks.

Data Ingestion and Transformation

Bring data from source systems into reusable pipelines with clear transformation and validation rules.

Data Products

Turn complex enterprise data into trusted, business ready products that support consistent reporting, analytics and informed decisions.

Business Ready Data Sets

Prepare reusable data sets around defined business needs and consistent definitions.

Semantic Modelling

Create a consistent meaning for key business terms, metrics and relationships.

Governed Data Products

Build data products with clear ownership, quality expectations and access controls.

Analytics and AI Ready Data

Prepare data so reporting, analytics and suitable AI use cases can use it consistently.

AI and Machine Learning

Prepare enterprise data and platforms for machine learning and agentic AI applications grounded in trusted business context.

Machine Learning

Prepare data pipelines and platform foundations that support model development and operational use.

Generative AI

Build GenAI experiences grounded in approved enterprise information and defined business tasks.

Agentic AI

Explore agents that can retrieve context, coordinate steps and assist with defined workflows.

AI Assisted Workflows

Connect AI to the workflows and decisions where it can support people with useful information and actions.

Governance and Security

Establish governed data environments with security, lineage and quality controls designed for enterprise scale analytics and AI.

Fine Grained Data Security

Apply access controls that reflect data sensitivity and the responsibilities of different users.

Data Lineage

Improve visibility into where data comes from, how it changes and where it is used.

Data Sharing and Collaboration

Make data available to the right users and teams with appropriate permissions and controls.

Data Quality and Compliance

Define quality checks and governance practices that support trusted and accountable use.

Migration and Modernisation

Modernise legacy data platforms through structured discovery, automated conversion, migration validation and alignment with a modern lakehouse architecture.

Platform Assessment and Discovery

Understand the source environment, dependencies, data volumes and migration risks.

SQL and ETL Transformation

Review and convert SQL, ETL and transformation logic for the target environment.

Data Model and Architecture Redesign

Redesign data models and architecture to support maintainability, scale and governance.

Migration Assurance and Validation

Use reconciliation, quality checks and validation to increase confidence in migrated data.

Agentic Data and AI

Turn enterprise data into actionable business outcomes through natural language access, conversational analytics and AI supported workflows.

Business Driven Data Querying

Help users ask questions in business language and find answers from trusted, governed data.

Conversational Analytics

Enable natural language exploration of approved metrics and information.

Agentic Workflow Orchestration

Connect data retrieval and defined workflow actions under approved controls.

AI Powered Decision Intelligence

Bring context and relevant information together to support faster and better informed decisions.

Frequently Asked Questions

Short answers to what people ask us most. Still unsure? Ask us directly

What Databricks capabilities does Palni cover?

Data engineering, data products, AI and machine learning, governance and security, migration and modernisation, and agentic data and AI.

What is a data product?

A trusted, business ready data set with clear ownership, consistent definitions, quality expectations and access controls, built for reporting, analytics and AI.

How does Palni validate a data migration?

With reconciliation, quality checks and validation of data, logic and outputs, so teams can trust the migrated results.

How does Palni approach data governance?

Through fine grained access controls, lineage, controlled sharing and quality checks, designed for enterprise scale analytics and AI.

What is agentic data and AI?

Systems that let people ask questions in business language, explore approved metrics conversationally and trigger defined workflow actions under approved controls.

Does Palni build pipelines with Lakeflow?

Yes. Palni develops and manages data pipelines with repeatable processing, monitoring and quality checks.

Put Our Databricks Expertise to Work

Discuss your data architecture, migration or AI priorities with Palni. We will help you find a practical starting point.

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Share your priorityTell us the challenge, platform or data priority you are working on.
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sales@palni.com214 501 06908 offices across the USA and India