Databricks Migration
Move from legacy on-prem systems or fragmented cloud warehouses to a unified Databricks Lakehouse. We take care of the migration complexity so you can focus on innovation – reducing costs and accelerating AI adoption.
Why Migrate to Databricks? Key Business Benefits Explained
How can we transition away from legacy systems while maintaining stability and continuity of critical reporting?
Legacy warehouses, Hadoop clusters, and bespoke ETL frameworks often run mission-critical nightly loads that downstream systems depend on. Replatforming them introduces risk because report accuracy is tied to legacy SQL dialects, custom user-defined functions, and undocumented business logic.
Addepto mitigates this risk by:
- running source and target systems in parallel,
- automatically validating row-level and column-level parity (data, schema, metrics),
- translating warehouse-specific SQL (e.g., Teradata BTEQ, Netezza, Oracle PL/SQL) into Delta Lake equivalents,
- recreating orchestration paths in Databricks Workflows or existing schedulers.
This ensures your BI dashboards, financial reports, and regulatory outputs remain consistent during the transition, with no “big-bang” cutover risk.
How do we establish consistent governance when data is distributed across multiple platforms, teams, and formats?
Enterprises typically maintain separate data marts, sandbox environments, and ML datasets legacy warehouses and local notebooks. This leads to duplicated logic, inconsistent metric definitions, and unclear data ownership.
Addepto addresses this by implementing:
- Unity Catalog-based governance, with workspace-level, table-level, and column-level access policies,
- centralized lineage tracking for tables, jobs, and ML models,
- automated schema evolution rules to prevent breaking changes,
- fine-grained permissioning aligned with enterprise RBAC requirements,
- standardized data contracts used by engineering and analytics teams.
This transforms fragmented environments into a single governed ecosystem, reducing compliance noise and simplifying data lifecycle management.
How can we ensure our data foundation is suitable for machine learning and AI without rebuilding pipelines from scratch?
Most enterprises maintain separate paths for BI, data science, and ML operations. This causes inconsistencies and makes it difficult to establish reproducible training pipelines.
Addepto restructures this by:
- redesigning ingestion layers into Delta Live Tables with built-in data quality rules (expectations),
- standardizing bronze – silver – gold layers that serve both BI and ML needs,
- creating feature pipelines aligned with MLflow Feature Store,
- converting legacy Spark jobs or ETLs into clean, incremental Delta workflows,
- enabling experiment tracking and versioned datasets for reproducibility.
This provides a shared, high-quality data foundation suitable for analytics, ML, and generative AI workloads, without replatforming the business logic from zero.
How do we handle variable or unpredictable workloads without manually managing capacity?
Legacy systems often require fixed cluster sizing or manual resource management. Spikes – such as month-end closing, promotional campaigns, or model retraining—can overwhelm these environments.
Addepto optimizes Databricks for variability by:
- configuring job and interactive clusters with auto-scaling and auto-termination,
- separating workloads into cost-efficient cluster policies (SQL, ETL, ML, batch),
- implementing photon execution for SQL performance improvements,
- using Delta Lake’s Z-ordering and file compaction to reduce read times,
- integrating cost dashboards to track spend by team, project, or workload.
This ensures that workloads scale automatically when needed and contract when idle – preventing both performance bottlenecks and uncontrolled cloud spend.
How can we accelerate time-to-value for AI initiatives that drive revenue growth and customer retention?
Most AI and machine learning projects stall not because of algorithm limitations, but because data is inaccessible, inconsistent, or unsuitable for modeling. Data science teams spend 80% of their time on data preparation instead of building models that optimize pricing, predict churn, personalize recommendations, or detect fraud. This delays revenue-generating initiatives and frustrates executives who approved AI investments.
Addepto accelerates AI outcomes by:
- creating feature stores with pre-computed, versioned datasets that data scientists can immediately use for modeling,
- standardizing data quality rules so models train on reliable, consistent inputs,
- establishing MLOps workflows that reduce model deployment time from months to weeks,
- implementing monitoring that detects model performance degradation before it affects business KPIs,
- building reusable pipelines that allow quick experimentation and iteration on new use cases.
This transforms AI from a long-term research project into a practical revenue driver—enabling faster launches of personalized marketing, dynamic pricing, predictive maintenance, and other high-value applications.
Clients that trusted us
What our clients say
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
The collaborative culture during the project was unique and significantly facilitated seamless implementation. Addepto has completely satisfied our requirements.
Dawid Majcherek – Nexteer Advanced Technology Engineer – Nexteer Automotive
Addepto’s flexible team reacts to tasks rapidly.
Head of IT – SMEO S.A.
Databricks Migration Process
From Assessment to Production: A Structured Approach
Discovery & Audit
Your current data estate is mapped end-to-end, revealing dependencies, legacy bottlenecks, and data-quality risks—giving you a clear, accurate migration roadmap from day one.
Architecture Design
You receive a future-ready Lakehouse architecture built on Medallion layers (Bronze/Silver/Gold), designed for seamless scalability, robust security with Unity Catalog, and long-term cost efficiency.
MVP & Parallel Run
A fully validated MVP demonstrates how your data behaves in the new environment, while a parallel run ensures zero downtime and guarantees one-to-one data parity before full cutover.
Full-Scale Migration
Your schemas, code, and pipelines are automatically converted and migrated at scale using proprietary accelerators—bringing the bulk of your workloads onto Databricks quickly and reliably.
Enablement & Handover
Your team gains hands-on expertise with the new stack, supported by complete documentation and FinOps guardrails that keep cloud spending optimized after go-live.
End-to-End Databricks Expertise for Every Stage of Your Data Journey
At Addepto, we deliver enterprise-grade Databricks solutions that process millions of data points daily and generate measurable ROI.
Our expertise covers data integration, ML pipelines, and cloud-native architecture, helping organizations cut infrastructure costs, speed up innovation, and achieve real business impact.
read more:
Databricks Services
A comprehensive assessment of your Databricks setup, covering architecture, compute usage, governance, security, and workflow efficiency. We uncover bottlenecks, hidden risks, and unnecessary costs, then deliver a clear, actionable roadmap for improvement. This gives you full visibility and control over the reliability and performance of your platform. Read more: https://addepto.com/databricks-audit/
A tailored enhancement initiative designed to elevate performance, reduce costs, and strengthen reliability across your Databricks ecosystem. We refine compute usage, streamline pipelines, remove inefficiencies, and introduce automation where it drives measurable value. Your organization benefits from a faster, smoother, and more cost-efficient platform built for continuous growth. Read more: https://addepto.com/databricks-optimization/
Databricks Consulting and Deployment
We translate your Databricks architecture into a fully operational, production-ready environment. This includes workspace setup, governance configuration, CI/CD pipelines, job orchestration, and integration with your broader data ecosystem. The result is a secure, scalable deployment that accelerates time-to-value and supports continuous delivery of analytics and ML solutions. Read more: https://addepto.com/services/databricks-deployment-services/
Why work with us
AI and Data Experts on board
Databricks certified Experts
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
Aviation: From Flight Safety to Operational Excellence
Airlines operate under intense pressure to reduce costs while upholding strict safety standards. Yet unplanned maintenance, inefficient route planning, and airport delays erode margins and harm the passenger experience.
What Databricks enables:
- Predictive Maintenance: Use aircraft sensor data to anticipate component failures, cutting unplanned downtime and maintenance costs.
- Flight Operations Optimization: Process real-time flight data to improve routing, fuel efficiency, and crew scheduling.
- Turnaround Time Reduction: Stream operational data to forecast gate availability and coordinate ground teams, reducing delays.
- Safety & Compliance: Centralize incident reports, maintenance logs, and regulatory data for deeper safety insights and audit readiness.
Automotive: Driving Innovation Through Data
Automotive manufacturers navigate supply chain disruptions, high warranty costs, and the massive computational load of autonomous vehicle development—while generating data from millions of connected cars worldwide.
What Databricks enables:
- Connected Vehicle Analytics: Process large-scale telematics data to improve vehicle performance, predict maintenance, and enhance driver experience.
- Supply Chain Optimization: Track global parts movement in real time to identify bottlenecks and anticipate disruptions.
- Quality Control: Analyze manufacturing sensor and defect data to reduce warranty claims and improve product quality.
- Autonomous Driving Development: Process petabytes of sensor data to train and validate self-driving models at scale.
Manufacturing: Optimizing Every Stage of Production
Manufacturers lose revenue to equipment downtime, unnecessary inventory, production waste, and rising energy costs—all intensified by limited real-time visibility into factory operations.
What Databricks enables:
- Production Line Optimization: Monitor equipment performance in real time to maximize throughput and reduce scrap.
- Demand Forecasting: Combine sales history, market trends, and external factors to plan production and inventory more accurately.
- Energy Management: Analyze energy usage patterns across facilities to uncover savings opportunities.
- Digital Twins: Build virtual models of production processes for testing, simulation, and ongoing performance improvement.
Engineering: Building Smarter, Safer Infrastructure
Engineering organizations face the risks of equipment failures, inaccurate project estimates, long development cycles, and complex regulatory requirements.
What Databricks enables:
- Asset Performance Management: Monitor equipment health across sites to prevent failures and extend asset life.
- Project Risk Analysis: Analyze historical project data to improve cost estimates, timelines, and risk mitigation.
- Design Optimization: Process simulation and test data to accelerate product development and reduce time-to-market.
- Regulatory Compliance: Centralize documentation, change logs, and compliance records to streamline audits and ensure traceability.
Aviation: From Flight Safety to Operational Excellence
Airlines operate under intense pressure to reduce costs while upholding strict safety standards. Yet unplanned maintenance, inefficient route planning, and airport delays erode margins and harm the passenger experience.
What Databricks enables:
- Predictive Maintenance: Use aircraft sensor data to anticipate component failures, cutting unplanned downtime and maintenance costs.
- Flight Operations Optimization: Process real-time flight data to improve routing, fuel efficiency, and crew scheduling.
- Turnaround Time Reduction: Stream operational data to forecast gate availability and coordinate ground teams, reducing delays.
- Safety & Compliance: Centralize incident reports, maintenance logs, and regulatory data for deeper safety insights and audit readiness.
Automotive: Driving Innovation Through Data
Automotive manufacturers navigate supply chain disruptions, high warranty costs, and the massive computational load of autonomous vehicle development—while generating data from millions of connected cars worldwide.
What Databricks enables:
- Connected Vehicle Analytics: Process large-scale telematics data to improve vehicle performance, predict maintenance, and enhance driver experience.
- Supply Chain Optimization: Track global parts movement in real time to identify bottlenecks and anticipate disruptions.
- Quality Control: Analyze manufacturing sensor and defect data to reduce warranty claims and improve product quality.
- Autonomous Driving Development: Process petabytes of sensor data to train and validate self-driving models at scale.
Manufacturing: Optimizing Every Stage of Production
Manufacturers lose revenue to equipment downtime, unnecessary inventory, production waste, and rising energy costs—all intensified by limited real-time visibility into factory operations.
What Databricks enables:
- Production Line Optimization: Monitor equipment performance in real time to maximize throughput and reduce scrap.
- Demand Forecasting: Combine sales history, market trends, and external factors to plan production and inventory more accurately.
- Energy Management: Analyze energy usage patterns across facilities to uncover savings opportunities.
- Digital Twins: Build virtual models of production processes for testing, simulation, and ongoing performance improvement.
Engineering: Building Smarter, Safer Infrastructure
Engineering organizations face the risks of equipment failures, inaccurate project estimates, long development cycles, and complex regulatory requirements.
What Databricks enables:
- Asset Performance Management: Monitor equipment health across sites to prevent failures and extend asset life.
- Project Risk Analysis: Analyze historical project data to improve cost estimates, timelines, and risk mitigation.
- Design Optimization: Process simulation and test data to accelerate product development and reduce time-to-market.
- Regulatory Compliance: Centralize documentation, change logs, and compliance records to streamline audits and ensure traceability.
Three Business-Critical Benefits of Databricks Migration
Budget optimization
Legacy infrastructure consumes disproportionate IT budgets through expensive licenses, over-provisioned capacity, and maintenance overhead. Modern data platforms eliminate these inefficiencies by replacing proprietary licensing with flexible consumption models, implementing auto-scaling that matches spend to actual usage, consolidating fragmented tools into unified infrastructure, and optimizing storage and compute independently.
Faster innovation & competitive response
Traditional environments slow down revenue-generating work—teams wait for infrastructure, fight for data access, and lose time prepping instead of analyzing. Modern platforms remove these bottlenecks with self-service tools, collaborative workflows, and automated deployment. The result: faster product launches, quicker data science iteration, and greater agility against competitive pressure.
Enhanced Governance & Risk Management
Fragmented data environments create compliance exposure, audit burden, and regulatory risk. Inconsistent access controls, unclear data lineage, and manual governance processes leave organizations vulnerable to violations and findings. Unified governance eliminates these gaps through centralized policy enforcement, automated audit trails, complete lineage tracking, and consistent security controls across all data assets.
What our clients say
I am impressed with their openness to new solutions and dedication to making the tool effective and easy to maintain.
Narayana Pappu CEO – Zendata
Addepto delivered a platform with AI models that resulted in time savings. The team addressed the client's core problem and went above and beyond to understand the client's tech stack. Their technological expertise and state-of-the-art technologies complemented their business-oriented approach.
Michelle Medeiros Sr Director of Data & ML – Western Governors University
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
What does your company offer?
You get a team of certified Databricks Architects and Data Engineers who specialize exclusively in the Databricks ecosystem. Your migration is accelerated with proprietary tools that automatically convert legacy SQL (Oracle, Teradata, Hive) into Spark SQL, reducing manual effort and eliminating errors. Instead of a simple lift-and-shift, your pipelines are refactored to improve performance, lower TCO, and prepare your data estate for AI and advanced analytics. The result is a faster, safer transition to the Lakehouse—and a modern, cost-efficient, business-ready data platform that delivers real value from day one.
Will Databricks reduce our overall data and AI infrastructure costs?
Yes. Most organizations see a significant drop in compute costs due to Photon engine acceleration, Delta Lake optimizations, and auto-scaling clusters, combined with better storage efficiency and fewer redundant pipelines. Plus, consolidating ETL, BI, and ML onto one platform eliminates tool sprawl. With our FinOps practices, you maintain ongoing cost governance, not just savings at migration time.
How does Databricks improve the performance and reliability of our analytics and AI workloads?
Databricks unifies streaming, ETL, analytics, and ML on a single Lakehouse platform. You gain:
- Faster model training thanks to distributed compute and optimized runtimes.
- More reliable datasets via Delta Lake’s ACID transactions and versioning.
- Better ML consistency with Feature Store and centralized lineage.
- Fewer pipeline failures due to automated scaling and robust orchestration.
Overall, your teams spend less time fixing data issues and more time delivering insights.
Can Databricks support our roadmap for GenAI, RAG, or LLM fine-tuning?
Yes, Databricks is designed for next-generation AI. You can:
- Build vector databases using Delta Lake for large-scale embedding storage.
- Run scalable RAG architectures integrating LLMs and enterprise data.
- Fine-tune and deploy models using MosaicML, MLflow, and Unity Catalog.
- Use distributed compute to handle large training datasets efficiently.
This gives your organization a secure, governed, and scalable foundation for GenAI initiatives—from prototypes to production.
How do you ensure governance, security, and compliance during and after migration?
The migration is built around Unity Catalog, ensuring consistent governance across all Workspaces, users, clusters, and clouds. You receive:
- Centralized access control (table-, column-, and row-level).
- Automated lineage and audit logs across pipelines and models.
- Secure data sharing internally and externally via Delta Sharing.
- Built-in compliance alignment (GDPR, HIPAA, PCI, ISO workflows).
Your governance shifts from fragmented to simple, scalable, and auditable.
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