Databricks Consulting Services
Optimize, migrate, and scale your Databricks Lakehouse—from foundations to production AI.
25+
Years of data expertise
100K+
Workloads migrated or managed
45+
Technology specializations
Pythian turns lakehouse complexity into measurable business outcomes
Production-grade Databricks solutions—from optimization to AI.
Cut costs, boost speed
Performance optimization and FinOps
We optimize cluster sizing, autoscaling, and DBU consumption to cut your total cost of ownership. Our engineers tune Spark jobs and implement Delta Lake best practices so your lakehouse delivers at the speed your business demands.
Govern your entire data estate
Unity Catalog governance implementation
We implement Unity Catalog across your organization—access control, data classification, lineage tracking, and quality monitoring. For multi-cloud environments, we standardize governance across every workspace.
Migrate legacy warehouses
Legacy-to-Databricks migration
We convert proprietary SQL and stored procedures into Spark SQL and PySpark—using automated tooling where possible and deep manual refactoring where it matters. Legacy ETL pipelines get replaced with Databricks-native architectures.
Scale and mature the lakehouse
Platform maturity and architecture modernization
We transform ad hoc Databricks deployments into production-grade enterprise platforms—governed medallion architectures, serverless compute adoption, and multi-cloud standardization.
Deliver real-time analytics
Production analytics and self-service BI
We connect your lakehouse to Power BI, Tableau, and Looker, design semantic layers for self-service analytics, and enable AI/BI Genie for natural-language exploration. We also manage transitions from legacy BI tools.
Operationalize AI at scale
Production AI, ML, and GenAI deployment
We deploy production ML models using MLflow with full experiment tracking and automated retraining. We build GenAI applications on your governed data—RAG pipelines, vector search, and AI agents—backed by MLOps infrastructure.
Unifying a tier-1 financial institution's data estate on the Databricks Lakehouse
Pythian consolidated legacy warehouses, governed petabytes of regulated data, and deployed production AI.
A global financial services firm with $50B+ in assets under management ran a fragmented data environment: A 12-year-old Teradata warehouse, siloed Oracle databases, and an ungoverned Databricks deployment. Pythian unified the estate onto a production-grade Lakehouse, implemented enterprise-wide Unity Catalog governance, and delivered three production ML models—all within 11 months.
A phased, outcomes-driven approach to Databricks
Full-stack environment profiling
We analyze your Databricks environment—cluster utilization, cost attribution, governance posture—and assess legacy source platforms for migration readiness. You get a clear picture of where you are and a prioritized roadmap forward.
Production-grade, goal-oriented blueprints
We design a production-grade architecture tailored to your business—medallion layer structure, compute strategy, and multi-cloud topology. For migrations, we map legacy code to Spark equivalents and sequence for early wins on high-value workloads.
Hands-on, dual-validation engineering
We build what we designed—optimizing jobs, deploying pipelines, and converting legacy code. For migrations, we run dual-validation to ensure nothing is lost in transit. Every pipeline is production-grade from day one.
Unified governance and cost control
We deploy Unity Catalog enterprise-wide and implement FinOps discipline—cost governance, DBU budgets, and consumption dashboards. For regulated industries, we align frameworks to compliance requirements across every workspace.
AI knowledge transfer
We connect your lakehouse to BI tools, deploy production ML models, and build GenAI applications. We also provide knowledge transfer so your team can operate independently—with 24/7 managed services available for ongoing support.
Ready to transform your Databricks environment?
Pythian's related Databricks services
Our expertise ensures your Databricks investment delivers lasting business outcomes.
Optimize and stabilize at the platform level
Database consulting
Deep-tier legacy and cloud-native database expertise, including Databricks environment optimization, Spark job tuning, and source platform assessment for migration readiness.
Migrate legacy platforms end to end
Data migration consulting
End-to-end migration from Teradata, Oracle, SQL Server, Netezza, SAS, and Hadoop to the Databricks Lakehouse—including SQL conversion, ETL replacement, and data validation.
Govern and align your data estate
Data strategy and governance consulting
Unity Catalog implementation, FinOps strategy, data governance frameworks, and regulatory alignment—so your Databricks investment is secure, compliant, and cost-controlled.
Databricks consulting services frequently asked questions (FAQ)
We implement Unity Catalog as the unified governance layer across your entire Databricks estate—including fine-grained role-based access control, automated sensitive data discovery, data lineage tracking, and quality monitoring. For regulated industries (healthcare, financial services, government), we align governance frameworks to HIPAA, GDPR, CCPA, and SOC 2 requirements. Every workspace gets consistent security posture, audit logging, and data classification. For organizations running Databricks across multiple clouds, we standardize governance across AWS, Azure, and GCP so compliance doesn't break at the cloud boundary.
ROI comes from multiple sources. For existing Databricks customers, cost optimization alone typically delivers significant reductions in total cost of ownership—driven by eliminating idle clusters, right-sizing compute, optimizing Spark jobs, and implementing FinOps discipline around DBU consumption. Performance improvements of 3–8x on critical workloads are common when we enable Photon strategically, implement liquid clustering and Z-ordering, and refactor inefficient PySpark code. For migration customers, the ROI compounds: you eliminate legacy licensing and hardware costs, reduce operational complexity, and gain capabilities—self-service analytics, production AI—that weren't possible on the old platform. The phased approach means you start seeing returns on high-value workloads early, not just at project completion.
Tools like BladeBridge (now part of Databricks) can automate 60–80 percent of standard SQL conversion from Teradata, Oracle, SQL Server, and Netezza. However, the remaining 20–40 percent—complex stored procedures, deeply nested business logic, custom functions, and proprietary syntax—requires manual refactoring by engineers who understand both the source platform and the Databricks target. Legacy ETL pipelines (DataStage, SSIS, Informatica, SAS DI) have no direct automated conversion path; they must be redesigned as Delta Live Tables, Lakeflow, or Airflow-based pipelines. This is exactly where Pythian's dual fluency matters. We know Teradata's BTEQ, Oracle's PL/SQL, and Netezza's NZSQL as deeply as we know Spark SQL and PySpark—so the conversion is accurate, performant, and production-ready.
Databricks' consumption-based pricing (DBU model) can spiral without disciplined FinOps practices. We start with a cost attribution analysis—identifying which teams, workloads, and clusters are driving spend. Common culprits include idle clusters, over-provisioned compute, inefficient Spark jobs that consume excessive DBUs, and poor storage practices. We implement cluster policies and autoscaling guardrails, evaluate serverless compute for eligible workloads, optimize Photon enablement to balance performance with cost, and establish ongoing cost governance dashboards so your team can maintain discipline after we leave. Organizations typically see 30–40 percent TCO reduction through this work alone.
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