Databricks migration services: Avoid rework, silent errors and big-bang cutovers
Our Databricks migration services move your data to a governed lakehouse with both platforms live and rollback-ready. We cover assessment, architecture, data and pipeline migration, and validation, backed by 200 data engineers and 24 years of enterprise delivery.
Head of Data and Analytics Practice
A staged move to Databricks, led by one team from start to finish
Enterprise data teams move to Databricks because it fits their main cloud and stores data in the open Delta Lake format. It also governs data and AI in one place and includes generative AI tools. The risk comes from the migration itself. Copied SQL keeps the old technical debt. Access rules designed first apply to new tables on arrival, and rules added later need rework on each table.
As a Databricks partner since 2023, N-iX has led migrations of SQL Server, Azure Synapse, SAP BW, and on-premises systems for enterprises in energy, telecom, manufacturing, and finance. Our Databricks team reduces risk by moving in stages, with both platforms in parallel and your current environment ready as a rollback until you approve the switch. Automation handles routine SQL, while our engineers handle advanced code and business logic. The result is a governed lakehouse that analysts trust and AI use cases can build on.
30+
Databricks-certified experts
200+
AI, data, and ML specialists
Up to 50%
fewer errors in one client’s data pipelines after migration
5x
growth in data volume on one client’s Databricks platform
Our Databricks migration services
Data migration and synchronization
N-iX moves historical data in off-peak batches and keeps active datasets in sync through change data capture until cutover. Migrations run within AWS as part of a broader data platform modernization on AWS, or across clouds where a dedicated private network link limits egress costs. Source-to-target row counts confirm that every table arrives complete.
SQL, dbt, and ETL code conversion
Lakebridge enables conversion of routine SQL, dbt models, and stored procedures. Our engineers rebuild the complex code and business logic the tool only partially converts, and review every converted model against the business rules. dbt keeps documentation next to the models. N-iX also moves orchestration to Lakeflow Jobs and builds CI/CD pipelines for ongoing development.
Migration assessment and roadmap
N-iX profiles the current platform to find unused tables, duplicate models, and orphaned views, and maps their dependencies on reports and applications. Each workload gets a conversion effort rating. Databricks migration services deliver a phased roadmap with an effort estimate within 1-2 weeks.
Lakehouse design and consolidation
Our team designs the target data lakehouse on Databricks and brings data from several source systems into it. N-iX stores data from several source systems in one platform with one set of access rules and removes duplicate sources and pipelines. The old warehouse keeps serving reports until the switch.
Databricks cost optimization and enablement
Our specialists tune compute and storage costs, set autoscaling policies, and track team spend through dedicated compute. Analysts and engineers get hands-on training on the new platform, with documentation written for the client’s own environment.
Databricks migration for AI
N-iX designs the target platform for analytics and AI from the start, with Unity Catalog governing both data and models. Consolidated, governed Delta tables become the base for Machine Learning with MLflow and for large language model applications with vector search.
Work with N-iX to avoid common Databricks migration mistakes
1. Start on a clean platform without the old technical debt
2. Pay to migrate only the data and models that teams use
3. Define governance and access rules once, before the data arrives
4. Find data differences during testing, before users see them
5. Use converted code that engineers have checked against business logic
6. Switch in stages, with the old platform available as a fallback
Over 160 businesses rely on N-iX expertise: Success stories in Databricks adoption Case studies
Types of Databricks migrations we support
Snowflake tables, views, dbt models, and SQL logic move to Delta tables on Databricks. We move historical partitions in off-peak batches, and active datasets stay in sync through change data capture until cutover. Our engineers map differing data types, such as numeric precision and time zones.
SQL Server tables, views, and models move to Delta tables on Databricks, with Azure Data Factory handling the loads. Historical data moves in batches. New data moves through incremental loads. Our team compares results with the source to keep reports consistent during the move.
Synapse tables, dbt models, and pipelines move to Delta tables on Databricks, with Azure Data Factory orchestrating the loads. Compute is separated by team or requester, which allows cost tracking for each group and scaling without a full system shutdown.
SAP BW data and logic move to Delta tables on Databricks, with ingestion pipelines built for each source. Databricks migration services enable the removal of duplicate data sources and pipelines during the move, consolidating reports in Power BI on top of one governed warehouse.
On-premises databases and file-based data flows move to Databricks in phases. Reports and applications keep working during each phase. Legacy pipelines are analyzed and documented, duplicate logic is removed, and the remaining flows run in one standard framework.
Databricks platform capabilities N-iX delivers
Lakehouse design with bronze, silver, and gold data layers
Large language model and vector search support for AI applications
Management of the machine learning lifecycle, from experiments to deployment
Real-time data ingestion and processing
Open storage format with reliable transactions, time travel, and change data feed
One place for data and AI governance, access rules, and lineage
Version control and shared development for data teams
SQL analytics and dashboards
Secure exchange of data and models with partners and teams
Declarative ETL pipelines for batch and streaming data, with built-in history tracking
Our Databricks migration process in five steps
Goal
Know what exists, what teams use, and what needs to move. N-iX profiles the current platform before any plan is fixed.
What we do
- Run automated analysis of workloads, tables, models, and code complexity
- Find unused tables and duplicate models
- Rate every workload by conversion effort and map dependencies on reports and applications
Output
- Inventory of data, code, and dependencies
- Workload ratings by conversion effort
- Scoped migration plan with an effort estimate
Goal
Agree on the target platform and the access rules before any data moves. Our architects design both with the client’s data and security teams.
What we do
- Design the bronze, silver, and gold layers and the Unity Catalog structure
- Map existing roles and identity rules to the new access model
- Plan the cutover path, with parallel run, rollback, and a private network link for moves between clouds
Output
- Target architecture
- Access model reviewed with the client’s security team
- Phased migration roadmap
Goal
Move historical and active data to Delta tables within agreed cost and downtime limits.
What we do
- Move historical data in off-peak batches
- Keep active datasets in near-sync through change data capture
- Limit egress cost on cloud-to-cloud moves with a dedicated private network link
Output
- Data loaded into Delta tables
- Source-to-target row counts
- Source and target in near-sync until cutover
Goal
Move the SQL and model logic with the business rules intact. Lakebridge automation covers the routine code, and engineers cover the rest.
What we do
- Convert routine SQL, dbt models, and stored procedures with automation
- Rebuild advanced constructs and business logic by hand, with engineer review
- Move orchestration to Lakeflow Jobs and build CI/CD pipelines
Output
- Converted and reviewed pipelines and models
- Orchestration and CI/CD pipelines
- Documentation of the converted logic
Goal
Switch to Databricks after the results match. The client approves the cutover.
What we do
- Compare row, column, sum, and distinct-count results and resolve every difference
- Run both platforms in parallel during a defined validation window
- Repoint reports and applications, with a short cutover freeze on active data and the old environment kept as a rollback until sign-off
Output
- Reconciliation report with every difference resolved
- Signed-off cutover
- Plan to retire the old platform
What can teams do on Databricks after go-live?
Databricks Genie for business users
- Ask questions about governed data in plain language
- Get answers without writing SQL
- Keep answers within Unity Catalog permissions and existing access rules
- Shorten the time from question to answer for analysts and stakeholders
- Build dashboards and custom data apps on the same governed data
Databricks Genie Code and Claude Code with Databricks MCP for engineers
- Build pipelines on the migrated platform with AI assistance
- Test and document pipelines with AI assistance
- Deploy tested pipelines through automated CI/CD
- Connect Claude Code to Databricks managed MCP servers
- Give AI tools access only to the data each user can see in Unity Catalog
Keep data, access, and uptime safe during Databricks migration services
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Validating data against the source
The N-iX team compares row, column, sum, and distinct-count results between the old and new platforms. We also run data-quality checks after the reconciliation and resolve every discrepancy before sign-off.
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Securing access and identity
We map existing roles to the target identity provider, such as Snowflake roles and AWS IAM to Azure Entra ID, with single sign-on and identity federation. Unity Catalog then enforces table, column, and row-level access through service principals. The client’s security team reviews the access model with our team before go-live.
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Limiting downtime at cutover
Source and target run in parallel for a defined validation window. Providing Databricks migration services, we limit the cutover to a short, scheduled freeze on active data, and the old environment stays available as a rollback until sign-off.
What sets N-iX apart as the Databricks migration partner
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Deep expertise in Databricks migration
N-iX brings 24 years of engineering delivery and has been a Databricks partner since 2023, with more than 30 Databricks-certified experts. We profile the current platform before any plan is fixed; duplicate models stay out of scope. Lakebridge, the Databricks migration tool, converts routine SQL and models. Our engineers handle the complex code and the business rules.
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Data and AI on one governed platform
Drawing on 100 delivered data and AI projects, our architects design the target platform for analytics and AI from the start, with Unity Catalog governing both data and models. Genie gives business users plain-language access to governed data, and AI-assisted tools help our engineers build and test pipelines inside CI/CD. Under Pragmatic AI Software Engineering, N-iX measures what those tools deliver before scaling them. The migrated platform is ready for new AI use cases.
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Assurance built into every migration
Every migration includes reconciliation, a parallel run, and a rollback option. The N-iX team compares row, column, sum, and distinct-count results with the source in a defined validation window, and data-quality gates run on top of the reconciliation. Every discrepancy is resolved before sign-off, and the client’s security team reviews the access model before go-live. The old environment stays available until the client approves the switch.
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Full-cycle partnership after go-live
As your Databricks migration provider, N-iX stays available for monitoring, cost optimization, and troubleshooting. Performance tuning covers Spark jobs, SQL queries, and cluster settings, and regular optimization reviews cut unnecessary compute spend. N-iX’s optional managed services cover the day-to-day operation of the Databricks platform. Training sessions and custom documentation prepare analysts and engineers to run it themselves.
Head of Data and Analytics Practice
Head of Data and Analytics Practice
FAQ
A Databricks migration takes 4-6 weeks for a basic scope and 3-6 months for projects with extensive data migration and custom integrations. The timeline depends on the complexity of the data environment and the number of integrations, and N-iX confirms it after a 1-2 week assessment.
The cost of Databricks migration consulting & services depends on data volume, the number of pipelines, and the share of complex code, and N-iX scopes it after an assessment. Our Databricks readiness assessment takes 1-2 weeks at an indicative $10,000, and a rapid discovery with a working proof of concept takes 3-4 weeks at an indicative $40,000. End-to-end cost differs from project to project.
Yes, N-iX migrates SQL Server, Azure Synapse, SAP BW, and on-premises systems to Databricks, and applies the same method to Snowflake. For other sources, an assessment shows what the migration needs.
Routine SQL, dbt models, and stored procedures convert with automation, while advanced constructs and business logic need engineer review and often a rebuild. Within Databricks migration consulting, we profile the pipelines first and decide which to convert, rebuild, or retire.
A Databricks migration needs only a short, scheduled freeze on active data at cutover, and both platforms run in parallel before that. As professional Databricks migration consulting partners, N-iX keeps the old environment available as a rollback until the client approves the switch.
A Databricks migration roadmap is a phased plan that shows which workloads move, which are retired, and in what order, with an effort estimate. N-iX builds it after profiling the current platform and rating every workload by conversion effort. The roadmap also covers the target architecture, the validation window, and the cutover path for each phase.
Contact us
Briefly outline your project or challenge, and our team will respond within one business day with relevant experience and initial technical insights.
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