Azure Databricks Migration Services
White-label delivery for AU, UK, and SG agencies. We architect the Lakehouse - your clients see your brand.
From data estate audit to production Delta Lake, Unity Catalog governance, and DLT pipeline builds. All delivered under your agency name.
Azure Databricks Migration: Building a Lakehouse, Not Moving Files
Moving data workloads to Azure Databricks isn’t a lift-and-shift. The platform is built around a Lakehouse architecture — Delta Lake as the unified storage layer, Unity Catalog for multi-workspace governance, and Apache Spark as the distributed compute engine. Migrate your existing ETL logic without rethinking it for this environment and you’ll get poorly-tuned clusters, unchecked schema drift, and compute costs running well above your legacy warehouse within ninety days. We’ve seen that pattern enough times to treat it as predictable.
The decisions made in the first two weeks of a Databricks migration — the medallion layer design, the cluster policy configuration, the Unity Catalog hierarchy — determine the cost and performance characteristics of the environment for the next two years. That’s where we spend disproportionate time before a single notebook gets written. You can see how this approach translates into real-world client work across our case studies.
Azure Databricks Migration Services
Six specialist capabilities. One engineering team handling your client delivery end to end.
Databricks Workspace Setup and Configuration
We provision Databricks workspaces via Terraform or Bicep, configure VNet injection for network isolation, set up cluster policies and instance pools, and map Azure AD groups to workspace roles and entitlements. Configuration choices made during setup — auto-termination thresholds, instance pool warm counts, cluster access control tiers — have a direct impact on monthly DBU spend. See the full scope of what we build on the Microsoft Azure Databricks documentation.
Delta Lake Migration and Table Conversion
We convert existing Parquet, Avro, ORC, and Hive tables to Delta format, define partitioning strategies, and implement Z-ordering on high-cardinality filter columns. Delta Lake’s ACID transactions, time-travel history, and schema enforcement replace the fragile state management your legacy pipelines handled manually. We validate row counts and aggregate metrics against your source environment before signing off on each table group.
Unity Catalog Data Governance
Unity Catalog introduces a three-level namespace — catalog, schema, table — and replaces the legacy table ACL model in Databricks. We design the catalog hierarchy to match your data domain structure, configure fine-grained access controls including row filters and column masking for PII fields, and enable audit logging to Azure Monitor. Most Azure Databricks migrations skip this step and spend months retrofitting governance onto a live production environment under time pressure.
Spark Workload Migration and Refactoring
We refactor legacy SQL Server SSIS packages, Azure Data Factory pipelines, and ad-hoc Jupyter notebooks into clean PySpark or Spark SQL jobs optimised for Databricks compute. Broadcast join thresholds, adaptive query execution, and dynamic partition pruning get tuned per workload rather than set to defaults. We don’t port broken transformation logic into Spark — we rewrite it with the right distributed compute primitives for the environment it’s actually running in.
Delta Live Tables Pipeline Build
DLT replaces brittle orchestration with @dlt.table and @dlt.expect decorators that define pipeline logic and quality expectations in the same codebase. We design Bronze-to-Silver-to-Gold DLT graphs, configure CDC ingestion from Azure Event Hubs and Kafka, and set continuous versus triggered pipeline modes based on your SLA requirements. Data quality expectations become part of the pipeline definition — not a separate monitoring layer bolted on after the fact.
MLflow and Model Registry Migration
If your client has existing ML workloads in AWS SageMaker, on-premise MLflow installations, or fragmented notebook experiments, we migrate experiment tracking and registered models to Databricks MLflow backed by Unity Catalog’s model registry. Feature engineering pipelines, model serving endpoints, and A/B deployment configurations migrate with full experiment lineage and version history retained.
Our Assessment-First Migration Framework
We don’t start with cluster configurations or notebook rewrites. Every engagement begins with a data estate audit — cataloguing source systems, transformation layers, and downstream consumers before a single line of PySpark gets written. From that inventory, we design the target Lakehouse architecture: Bronze, Silver, and Gold Delta layer boundaries, the Unity Catalog namespace hierarchy, the cluster policy matrix, and network topology within your Azure VNet.
Migration then proceeds in defined phases against a sign-off checklist — not as a big-bang cutover. Your legacy systems keep running until the Databricks environment passes row-count reconciliation and aggregate metric validation at every downstream consumption point. We’ve seen enough rushed cutovers to know a parallel-run period isn’t optional. To scope your migration or ask about a specific source environment, book a discovery call with our team — we can typically return a scope document within a week.
Technical Capabilities We Bring to Every Databricks Migration
Architecture and governance decisions that hold up at production scale from day one.
Medallion Architecture Design
We define clear semantic boundaries between your raw ingestion layer (Bronze), cleansed and conformed domain tables (Silver), and aggregated consumption layer (Gold). Compaction schedules, VACUUM policies, and Z-ordering strategies are designed per layer and per table — not applied uniformly across the estate with a single setting.
Unity Catalog Governance
Three-level namespace design, workspace federation across multiple Databricks environments, column-level masking for PII compliance, row-filter policies for multi-tenant data isolation, and audit log routing to Azure Monitor. Governance designed from the start — not retrofitted under deadline pressure six months into production.
Compute and Cost Optimisation
Autoscaling cluster policies calibrated to actual query patterns, spot instance job clusters for batch workloads versus interactive clusters for analyst queries, Photon engine eligibility assessment, and DBU consumption dashboards in Azure Cost Management. We size for your real usage — not a generic template that leaves compute budget on the table.
Pipeline Observability
DLT expectation metric dashboards, Spark UI profiling for long-running stages, Ganglia cluster metrics, and Azure Monitor alerts triggered on data quality SLA breaches. Your team sees exactly what’s happening across every pipeline stage — not just a green or red job status from an orchestration layer with no diagnostic detail.
Azure Databricks Migration Delivered Under Your Agency Brand
We work as an invisible extension of your agency. Our Databricks engineers operate in your Slack, write documentation in your naming conventions, and produce migration reports and architecture diagrams your account managers can share directly with clients. Your clients see your project team. We’re the engineering layer behind it — and that invisibility is deliberate.
Our white-label development model is built for agencies managing data engineering clients at scale. You scope confidently; we handle technical delivery. For agencies bringing Azure Databricks migration to multiple clients, our agency partner programme provides priority access to our Databricks team, preferred project rates, and a dedicated account contact across all your active engagements. Both delivery structures operate invisibly under your brand.
Why Azure Databricks Migrations Fail — And How We Avoid It
The most common failure pattern: teams port existing pipeline logic without rethinking it for a distributed Spark environment. SSIS packages that ran sequentially on a single SQL Server node get converted to PySpark notebooks on a default 8-node cluster — no broadcast join tuning, no adaptive query execution, no partition strategy. Compute costs run 40 to 60 percent over forecast. Schema drift causes downstream failures weekly. The data team spends its time debugging job timeouts instead of building new capability.
The second pattern is skipping Unity Catalog at the start. Teams launch on the legacy table ACL model, discover it doesn’t scale across multiple workspaces, and spend three months retrofitting governance onto a live production environment under deadline pressure. We treat both as non-negotiable prerequisites — architecture design and governance setup are phase one. Our Azure development services practice means Databricks is always positioned within a properly architected Azure environment, not treated as an isolated tool.
Engagement Models for Azure Databricks Migration
Structured for agency delivery workflows. Scalable across your full client portfolio.
Migration Sprint — Fixed Scope
A defined 4-to-8-week sprint covering assessment, architecture design, environment provisioning, pipeline migration, and validation with handover documentation. Best for agencies that need a firm deliverable date and a clearly bounded scope they can communicate to clients without ambiguity.
Dedicated Databricks Engineer
A senior Databricks-certified engineer embedded in your client project — joining standups, owning the migration backlog, participating in client demos, and writing documentation your account team reviews before it ships. Available full-time or part-time depending on project stage and workload.
Lakehouse Architecture Retainer
An ongoing monthly retainer for agencies managing multiple Databricks clients simultaneously. Covers architecture reviews, new pipeline builds, Unity Catalog governance audits, and performance optimisation across your client portfolio — without spinning up a separate project for each request. Predictable cost, flexible scope.
Staff Augmentation — Databricks Pod
Two to four Databricks engineers augmenting an existing client data team. Right for clients who have in-house data analysts or BI developers but need engineering capacity to execute the migration. The pod integrates into the existing team structure without replacing it. Reach us via our contact page to discuss pod sizing for your client.
Our Azure Databricks Migration Process
Six phases from data estate audit to production handover, with sign-off gates at each stage.
Phase 1 — Data Estate Audit
We catalogue every source system, warehouse schema, transformation layer, and downstream consumer. Each workload is rated for migration complexity: straightforward table conversion, moderate refactoring needed, or significant rewrite required. The output is a migration scope document your agency can use to set accurate client expectations before a line of code gets written.
Phase 2 — Lakehouse Architecture Design
We define the Bronze-Silver-Gold Delta layer boundaries, design the Unity Catalog namespace hierarchy to match your data domain structure, blueprint the cluster policy matrix, and plan the network topology within your Azure VNet. Architecture decisions are documented and reviewed before environment provisioning begins — not discovered during build.
Phase 3 — Environment Provisioning
Databricks workspace deployment via Terraform or Bicep, Unity Catalog metastore setup and workspace attachment, cluster policy creation, VNet injection configuration, and Azure AD group-to-workspace role mapping. The environment is provisioned to specification and validated before any data or pipeline migration activity starts.
Phase 4 — Pipeline Migration and Refactoring
Legacy SSIS packages, ADF pipelines, and raw notebooks are refactored into PySpark or Spark SQL jobs tuned for Databricks. DLT pipelines are built with @dlt.table decorators and @dlt.expect quality expectations. CDC ingestion from Azure Event Hubs and Kafka is configured. Adaptive query execution and partition pruning are tuned per workload — not left at default settings.
Phase 5 — Data Quality Validation
DLT expectation suites run against Bronze-to-Silver and Silver-to-Gold transitions. Row counts and aggregate metrics from the Databricks environment are reconciled against the legacy source system. No downstream consumer is cut over until validation passes at every consumption point. A parallel-run period is standard — it’s what separates a clean handover from a production incident.
Phase 6 — Handover and Knowledge Transfer
We deliver workspace administration runbooks, cluster policy documentation, Unity Catalog governance guides, DLT pipeline references, and DBU cost budgeting dashboards in Azure Cost Management. Your client’s data team receives a structured knowledge transfer session covering Databricks administration and pipeline operation — not just a repository of code they’ve never seen before. Learn more about how we structure all our engineering delivery on the NextEnvision Digital homepage.
Azure Databricks Migration — Frequently Asked Questions
Honest answers to the questions agencies ask us most before engaging.
What security requirements should Android Kotlin development address?
It depends on the source environment. A data warehouse with under 50 tables and no custom ETL logic can typically be migrated in four to six weeks. Environments with hundreds of legacy SSIS packages, complex CDC pipelines, ML workloads, and compliance requirements tend to run ten to sixteen weeks. We don’t quote timelines without a completed scope document — we size every engagement during the audit phase first.
How is WCAG 2.1 accessibility implemented in Android Kotlin development?
Yes, and the sequencing matters. Power BI DirectQuery against a Databricks SQL Warehouse performs very differently from DirectQuery against SQL Server or Azure Synapse Analytics. We test report query patterns against the Databricks endpoint during the validation phase and tune SQL Warehouse cluster sizing and concurrency settings to match your report SLAs before any cutover happens.
How does Hilt dependency injection work in Android Kotlin development?
Synapse fits better if your team is heavily SQL-oriented and already embedded in the Azure native tooling ecosystem. Databricks is the right choice when you have significant machine learning workloads, need tight MLflow integration, or are running large-scale Spark jobs with complex transformation logic. They’re not interchangeable — and we’ll tell you honestly if Synapse is the better fit for a specific client before you commit to a Databricks migration scope.
What is ProGuard/R8 and why does Android Kotlin development need it?
You can keep Parquet, but you’ll lose ACID transactions, time travel, schema enforcement, and the Delta change data feed. That means your pipelines need more defensive logic to handle schema drift and partial writes. For anything above a prototype or a single-user analytics workload, we migrate to Delta. The compaction and Z-ordering overhead is marginal compared to the operational reliability and governance capabilities you gain.
How does Android Kotlin development handle errors and offline states?
Unity Catalog replaces the legacy table ACL model. If you’re currently using workspace-level GRANT statements, those need to be re-modelled as Unity Catalog privileges on the three-level namespace — catalog, schema, table. The governance model is more powerful, particularly for multi-workspace environments, but it requires deliberate planning. We document the privilege migration as part of the architecture design phase so there are no surprises during provisioning.
How do you evaluate and select third-party libraries for Android Kotlin development?
That’s our primary delivery model. We operate as an invisible extension of your agency — in your project management tools, writing documentation in your naming conventions, producing architecture diagrams and migration reports your account managers can share directly. Your clients interact with your team; we’re the engineering layer they don’t see. Our agency partner programme is specifically structured for this kind of ongoing engagement across multiple client projects.