Relevant Software
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Databricks migration services

Databricks migration services

Q: How do you decide which workloads to migrate and which to retire?

Each workload gets three questions: does something downstream still read its output? does the logic inside it exist anywhere else?would you build it this way today? The answers put it in one of three buckets, and your team signs the list before any migration work begins.Pick your ten heaviest jobs by runtime and try to name a person who reads each result. Any job where the name does not come easily is a candidate to retire rather than rebuild. Where usage telemetry is missing, our engineers instrument the estate to gather it before the scoring pass.

Q: Should we migrate everything to Databricks or modernize what we have?

Both options are legitimate, and the estate decides. Migration suits a platform that is the constraint itself, through a cluster contract ending, scaling limits, or an operations burden your team carries. Re-architecting in place suits an estate where the platform still serves and the gaps sit in modelling, freshness, or governance, and our data warehouse modernization team scores each workload for that call.A quick check: list your three biggest complaints about the current platform. If they read as licence cost, cluster operations, or capacity, the move addresses them. If they read as disputed metrics or stale data, those travel with you unless the modelling changes too. When your list mixes both, our architects split it and price each side.

Q: Vendors quote 30% to 60% effort savings from migration accelerators. How much is real?

Tooling genuinely helps with the mechanical share: schema conversion, standard SQL translation, and job scaffolding all move faster with automation, and our engineers use it. The savings shrink on the parts that decide the timeline, which are stored procedures, vendor-specific functions, ETL mappings holding undocumented rules, and anything with a hand-tuned performance profile.A check worth running before you compare quotes: count your stored procedures and ETL mappings, then ask each vendor what their percentage assumes about that count. A figure quoted before anyone has seen your inventory describes their average project rather than your estate. If the count is hard to pull, our engineers produce it during the assessment, and you can hold every quote against the same number.

Q: Do our teams lose access to reporting during the migration?

No, your reporting stays available throughout. The legacy platform keeps serving while the lakehouse fills through rebuilt pipelines, and both answer the same questions for an agreed parallel window. Consumer groups then move in stages, each with a checkpoint and a rollback, so exposure stays small.

Q: Can you take over a Databricks migration another team or vendor started?

Yes, we can, and part-finished migrations are a common starting point. The first job is establishing where the migration actually stands, which is seldom where the last status report left it. Our architects trace which workloads moved, which of them reconcile against the legacy platform, and what in production still reads from the old system, then rebuild the dependency map connecting the two.Already-migrated workloads then get verified before anything new moves, so the remaining stages build on output your team has confirmed. From there our engineers complete the outstanding scope in stages, each priced and dated, with the parallel run and rollback discipline applied from the point we pick the work up.You receive the state assessment as a written document with the completion plan attached, and it stands on its own with any partner you choose. To have your migration reviewed on those terms, contact us and we will scope the assessment against what has already moved.

How many jobs on your Hadoop cluster still have someone reading their output? Relevant Software inventories every job, table, and report, scores each one to retire, rebuild, or rehost, then migrates only the workloads that earn a place on the lakehouse.

Get your estate scored

10+

Years of data engineering expertisedelivering warehouses, pipelines, and lakehouses into production

246

Projects deliveredacross data platforms, analytics, and product engineering

98%

Client satisfactionfrom customers who come back with their next project

99%

On-time deliveryso the cutover date you approve is the one that holds

Up to 20%

Fewer bugsdue to AI-assisted delivery with senior review on every release

Our Databricks migration services

Migration assessment and workload scoring
Hadoop and Cloudera migration to Databricks
Warehouse migration onto the lakehouse
Legacy ETL migration to Delta Live Tables
Pipeline rebuild and orchestration on Databricks
Unity Catalog and governance during migration
Validation, parallel run, and staged cutover
Post-migration cost tuning and decommissioning

Migration assessment and workload scoring
Hadoop and Cloudera migration to Databricks
Warehouse migration onto the lakehouse
Legacy ETL migration to Delta Live Tables
Pipeline rebuild and orchestration on Databricks
Unity Catalog and governance during migration
Validation, parallel run, and staged cutover
Post-migration cost tuning and decommissioning

Migration assessment and workload scoring

Assessment counts what exists and what still earns its keep: jobs, tables, reports, and the consumers behind each one. Every workload leaves the pass marked to retire, rebuild, or rehost, with the reasoning attached. If your estate is staying where it is and Databricks is a new build alongside it, our Databricks implementation services cover that work.

An inventory of jobs, tables, and downstream consumers
A retire, rebuild, or rehost decision per workload
A phased plan priced on what survives the scoring pass

Hadoop and Cloudera migration to Databricks

Hadoop estates carry HDFS layouts, Hive metastores, and years of MapReduce and Spark jobs, so each one moves onto Delta tables and lakehouse compute under a plan that keeps current output available.

HDFS and Hive tables converted to Delta with schemas validated
MapReduce and legacy Spark jobs rebuilt on current runtimes
Cluster contracts retired once nothing reads from them

Warehouse migration onto the lakehouse

Teradata, Oracle, and Synapse estates bring stored procedures and SQL that no converter fully reads, so our engineers rebuild that logic and test it against the numbers the warehouse produced.

Schemas and SQL converted, then reviewed object by object
Stored procedures rebuilt in Spark SQL or Python
Report totals compared across both platforms per table

Legacy ETL migration to Delta Live Tables

Informatica, SSIS, and Talend mappings hold business rules that exist nowhere else, so each one becomes readable pipeline code before the licence lapses.

Mappings documented, then rebuilt as declarative pipelines
Business rules preserved and covered by tests
Orchestration moved off the legacy scheduler

Pipeline rebuild and orchestration on Databricks

Feeds pointed at the old platform need a new destination, so data engineering team rebuilds ingestion and transformation on the lakehouse and runs them beside the originals until the outputs agree.

Batch and streaming ingestion repointed onto Auto Loader
Transformation logic as version-controlled code with tests
Workflows with monitoring, alerting, and retry handling

Unity Catalog and governance during migration

Access rules that lived in cluster configuration and Ranger policies need an equivalent on the lakehouse, so the catalog and permissions get built as workloads arrive.

Existing access policies mapped to catalog permissions
Masking and row-level rules applied to sensitive tables
Lineage and classification aligned with GDPR, HIPAA, and ISO 27001

Validation, parallel run, and staged cutover

Both platforms stay live while your teams compare outputs, then consumer groups move onto the lakehouse in stages with a rollback available at each one.

Row-level and aggregate comparison per table and per report
A parallel window where the teams who own reports verify them
Cutover by consumer group, each with a checkpoint

Post-migration cost tuning and decommissioning

Databricks bills DBUs plus cloud infrastructure, so cluster policies, job compute, and query tuning follow the move, and the legacy platform retires once nothing depends on it.

Cluster policies and job compute set per workload type
DBU tagging so spend attributes to a team
A decommissioning plan with legacy history archived

Types of Databricks migrations we support

Hadoop and Cloudera to Databricks
Hive metastores, HDFS layouts, and MapReduce jobs each need a lakehouse equivalent chosen deliberately, and the scoring pass usually retires a meaningful share of the job inventory before anything moves.
Teradata and Oracle to Databricks
Decades of stored procedures and vendor-specific SQL get rebuilt by hand, then checked against the output the warehouse produced.
Legacy ETL to Databricks
Informatica, SSIS, and Talend mappings become declarative pipeline code your team can read, which retires the licence alongside the tool.
Spark, EMR, and HDInsight to Databricks
Self-managed Spark moves onto managed runtimes, where the work concentrates on cluster policy, Photon economics, and the version upgrades the old estate skipped.
Azure Synapse to Databricks

Every workload earns its place on the lakehouse before anyone pays to move it.

How a Databricks migration engagement works

An Inventory With Owners Named
Every job, table, and report gets counted alongside the consumer that reads it, so the plan starts from evidence.
A Scoring Pass You Can Challenge
Each workload arrives marked to retire, rebuild, or rehost with the reasoning written down, and the scope shrinks to what earns the move.
A Sequenced Plan With A Rollback Path
Stages come priced and dated, each carrying its own way back, and your sign-off opens the next one.
Converted Workloads Proven Against Legacy Output
Tables, jobs, and rebuilt pipelines run on the lakehouse, then get compared to what the old platform produced.
A Parallel Run Your Teams Verify
Both platforms answer the same questions for an agreed window, and the report owners confirm the figures match.
A Staged Cutover And A Retired Cluster
Consumer groups move across in stages, the legacy platform retires on your word, and history lands in an archive you keep.

How a <b>Databricks migration</b> engagement works

Why choose Relevant for Databricks migration

A scored estate before a priced plan

Every job and table arrives marked to retire, rebuild, or rehost, so the quote covers the workloads that earn the move.

Outputs verified against the source

Converted workloads run against what the legacy platform produced, and the report owners confirm the match before cutover.

AI-assisted delivery with senior review

The Relevant AI Delivery Framework™ cuts delivery time by up to 50% through AI-assisted development, with every release still passing senior code review.

A senior engineering bench

92% of our engineers are senior, so the judgement calls on undocumented jobs and vendor SQL land right the first time.

The same team from inventory to cutover

96% employee retention keeps the architects who scored your estate on it through the final stage.

Certified security and compliance

ISO 27001-certified processes, with GDPR and HIPAA controls carried into the Unity Catalog access model.

What clients are saying about Relevant

4.9 is our Clutch
average

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How do you decide which workloads to migrate and which to retire?

Should we migrate everything to Databricks or modernize what we have?

Vendors quote 30% to 60% effort savings from migration accelerators. How much is real?

Do our teams lose access to reporting during the migration?

Can you take over a Databricks migration another team or vendor started?

Only what earns the move. Start with the estate scoring.

Our Databricks migration services

Migration assessment and workload scoring
Hadoop and Cloudera migration to Databricks
Warehouse migration onto the lakehouse
Legacy ETL migration to Delta Live Tables
Pipeline rebuild and orchestration on Databricks
Unity Catalog and governance during migration
Validation, parallel run, and staged cutover
Post-migration cost tuning and decommissioning

Migration assessment and workload scoring
Hadoop and Cloudera migration to Databricks
Warehouse migration onto the lakehouse
Legacy ETL migration to Delta Live Tables
Pipeline rebuild and orchestration on Databricks
Unity Catalog and governance during migration
Validation, parallel run, and staged cutover
Post-migration cost tuning and decommissioning

Why choose Relevant for Databricks migration

A scored estate before a priced plan

Every job and table arrives marked to retire, rebuild, or rehost, so the quote covers the workloads that earn the move.

Outputs verified against the source

Converted workloads run against what the legacy platform produced, and the report owners confirm the match before cutover.

AI-assisted delivery with senior review

The Relevant AI Delivery Framework™ cuts delivery time by up to 50% through AI-assisted development, with every release still passing senior code review.

A senior engineering bench

92% of our engineers are senior, so the judgement calls on undocumented jobs and vendor SQL land right the first time.

The same team from inventory to cutover

96% employee retention keeps the architects who scored your estate on it through the final stage.

Certified security and compliance

ISO 27001-certified processes, with GDPR and HIPAA controls carried into the Unity Catalog access model.

Only what earns the move. Start with the estate scoring.

Databricks migration services

Our Databricks migration services

Migration assessment and workload scoring

Hadoop and Cloudera migration to Databricks

Warehouse migration onto the lakehouse

Legacy ETL migration to Delta Live Tables

Pipeline rebuild and orchestration on Databricks

Unity Catalog and governance during migration

Validation, parallel run, and staged cutover

Post-migration cost tuning and decommissioning

Types of Databricks migrations we support

Every workload earns its place on the lakehouse before anyone pays to move it.

Why choose Relevant for Databricks migration

Only what earns the move. Start with the estate scoring.

Our Databricks migration services

Migration assessment and workload scoring

Hadoop and Cloudera migration to Databricks

Warehouse migration onto the lakehouse

Legacy ETL migration to Delta Live Tables

Pipeline rebuild and orchestration on Databricks

Unity Catalog and governance during migration

Validation, parallel run, and staged cutover

Post-migration cost tuning and decommissioning

Types of Databricks migrations we support

Every workload earns its place on the lakehouse before anyone pays to move it.

Why choose Relevant for Databricks migration

Only what earns the move. Start with the estate scoring.

Built AI-powered data visualisation platform for healthcare startup

Built a daily data pipeline with AI modules for an EdTech operator running 8 childcare centers

Want a migration plan sized to the workloads people still use?

Insights from our experts