Data engineering
Build scalable data foundations that make enterprise information reliable, accessible and ready for analytics and AI.
- Data pipelines and transformation
- Lakehouse architecture
- Cloud data platforms
Databricks transformation, end to end
Turn Databricks intobusiness infrastructure.
Palni helps enterprises assess, architect, migrate and operate Databricks environments across data engineering, governance, analytics and AI—connecting platform decisions to the systems and business processes the data must support.
Certified platform depth, connected to business outcomes.
80+Databricks-certified resources
100+Data engineers and AI practitioners
Full lifecycleStrategy · migration · governance · AI
Start with the constraint
Where should Databricks create value first?
Palni’s Databricks services span platform strategy and architecture, migration and modernization, lakehouse engineering, Unity Catalog and governance, data products, analytics, machine learning, generative AI and agentic workflows—from assessment through implementation, adoption and optimization.
Migration and modernization
Move critical workloads without losing business logic.
Assess the estate, prioritize workloads and rebuild pipelines on a governed Databricks foundation—with validation designed into every migration wave.
Start withEstate and workload assessment
Palni bringsArchitecture, migration and validation
Aim forFaster modernization with less disruption
Full-lifecycle Databricks delivery
From modern data to enterprise intelligence.
Palni engineers Databricks as one connected system for trusted data, analytics and AI. These six capabilities work together—from modernizing the estate to deploying governed agents—so value is built across the lifecycle, not in isolated projects.
Data products & semantic intelligence
Turn governed data into reusable, business-ready products and consistent metrics that improve decisions across the enterprise.
- Domain data products
- Semantic models and metrics
- BI and self-service analytics
AI & machine learning
Engineer predictive and generative AI solutions on trusted enterprise data, with the controls required to move beyond experimentation.
- Machine learning
- Generative AI
- MLOps and model monitoring
Governance & security
Establish the ownership model, catalog structure, access policies, lineage, quality controls and operating standards needed to govern Databricks across business domains.
- Unity Catalog foundations
- Access, privacy and security
- Lineage, quality and compliance
Migration & modernization
Move legacy platforms, pipelines and workloads to Databricks while preserving quality, business logic and operational continuity.
- Estate and workload assessment
- Data platform migration
- Reconciliation and validation
Agentic data & AI
Build governed agents that retrieve enterprise context, analyze trusted data, recommend actions and execute controlled workflows with permissions, evaluation, observability and human oversight.
- Enterprise AI agents
- Agentic data engineering
- Evaluation and observability
Published Palni case outcome
A fragmented data estate, rebuilt for real-time decisions.
The situation: Data was spread across ERP, CRM and legacy systems, creating inconsistent reporting, delayed decisions and low confidence in key metrics.
The intervention
Palni migrated more than 10TB of data into Databricks, rebuilt automated pipelines and introduced centralized governance and reporting.
40%increase in near real-time data availability
70%improvement in data trust
30%improvement in process efficiency
$100Kannual savings from ELT optimization
Published Palni outcome: 40% more near-real-time data availability, 70% greater data trust, 30% better process efficiency and $100K in annual ELT savings.
A path built around value
Start focused. Build confidence. Scale what works.
Palni combines senior guidance with hands-on engineering, proving the architecture and operating model before expanding the platform footprint.
Assess.
Map priorities, workloads, dependencies and risk. Define what success should change for the business.
Architect.
Design the target platform, governance foundation and delivery roadmap around the highest-value starting point.
Build.
Deliver working pipelines, data products, analytics or AI use cases with quality and validation embedded.
Scale.
Expand adoption, strengthen operations and enable teams to own the platform with confidence.
Why Palni for Databricks
Platform depth. Enterprise context. Senior execution.
Certified Databricks depth
Validated capability across data engineering, migration, governance, analytics and AI.
Connected enterprise thinking
Databricks expertise connected to Salesforce, ServiceNow and the workflows where data creates value.
Senior-led, lean teams
Experienced practitioners stay close to the work, decisions and business outcome.
Adoption beyond go-live
Architecture, governance and enablement designed to help teams use and own the platform.
High-value enterprise workloads
Data + AI that lands inside the business.
Trusted risk and finance intelligence
- Regulatory and finance data
- Fraud and risk analytics
- Customer intelligence
Connected operational intelligence
- Quality and yield analytics
- Predictive operations
- Supply chain visibility
Governed data for research and operations
- Clinical and operational data
- Research analytics
- Secure AI workloads
Decisions built around demand
- Customer 360
- Demand and inventory
- Merchandising analytics
Databricks questions
What enterprise buyers ask.
Direct answers about Palni’s Databricks services, migration validation, Unity Catalog, platform optimization and production AI.
What Databricks services does Palni provide?
Palni provides strategy and architecture, migration and modernization, lakehouse engineering, governance and security, analytics and data products, and AI and machine learning delivery.
Can Palni migrate our legacy data platform to Databricks?
Yes. Palni assesses the estate, defines the target architecture, migrates prioritized workloads and validates data quality and business logic before each release is scaled.
How does Palni validate data during migration?
Palni builds validation into each migration wave by reconciling source and target data, testing transformed business logic, monitoring data quality and confirming priority reports and downstream processes before release.
Can Palni implement Unity Catalog and data governance?
Yes. Palni helps establish the ownership model, catalog structure, access policies, lineage, data-quality controls, privacy requirements and operating standards needed to govern Databricks across business domains.
Can Palni help move GenAI or agents into production?
Yes. Palni prepares governed enterprise data and engineers generative AI, conversational analytics and agentic workflows with appropriate permissions, evaluation, observability and human oversight.
Can Palni improve an existing Databricks environment?
Yes. Palni can assess an existing environment for workload design, pipeline reliability, governance gaps, operating practices, performance and platform economics, then prioritize improvements around business value and risk.
What does a Databricks engagement deliver?
Depending on scope, deliverables can include a current-state assessment, target architecture, prioritized roadmap, governance design, working pipelines or data products, migration validation, AI production plan and operating documentation.
How does a Databricks engagement begin?
It begins with a focused discussion about the business priority, current data estate, workloads, dependencies and operating constraints. Palni then identifies the most valuable starting point and defines a practical roadmap with measurable outcomes.
Start with the hardest data problem
Build the Databricks platform your business can trust and use.
Bring Palni the priority, workload or platform constraint. We’ll help you define the clearest path from today’s estate to measurable value.
Talk to a Databricks expert