MIGRATE TO DATABRICKS
Move to the Databricks lakehouse from the platform you’re on today.
Datachecks combines specialized AI agents with migration experts to move Oracle, Teradata, Redshift, and SQL Server estates onto Databricks — automating assessment, Delta table mapping, SQL translation, validation, and reconciliation.
Supported objects and automation depth vary by source, target, and migration scope.
THE TARGET SIDE
Landing on a lakehouse is its own set of decisions.
These apply whichever platform you are migrating from. They are decided once, for the target architecture, rather than per source object.
STORAGE MODEL
Delta tables, not warehouse tables
Data lands as files under a transaction log rather than in an opaque warehouse. Partitioning, file sizing, and compaction become design decisions that determine whether queries perform.
GOVERNANCE
Unity Catalog is a modelling decision
Catalogs, schemas, and grants replace database-level permissions. The namespace design affects lineage, sharing, and access control, and is difficult to restructure once workloads depend on it.
EXECUTION MODEL
Spark SQL, PySpark, or a workflow
Translated logic can land as SQL, as PySpark, or as an orchestrated job. The right answer differs per object, and picking one uniformly produces either unreadable SQL or unnecessary notebooks.
LAYERING
Medallion structure is not automatic
Bronze, silver, and gold are a convention, not a feature. Deciding which migrated objects belong in which layer is what stops a lakehouse becoming a lake of legacy tables.
CUTOVER SAFETY
Delta time travel bounds the risk
Table history gives a recovery window if a load or transformation is wrong during cutover, which changes how aggressively a migration sequence can be run.
AFTER CUTOVER
Compute policy from day one
Cluster policies, job sizing, and serverless choices decide the running cost of the migrated estate. These belong in the migration, not in a later optimization project.
SOURCE PLATFORMS
Where are you migrating from?
Oracle
Rebuild Oracle warehouse logic as Databricks SQL and notebooks with dependency lineage preserved.
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Teradata
Convert Teradata analytical workloads and macros into Databricks SQL with reconciliation evidence.
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Redshift
Translate Redshift DDL, distribution and sort keys, and SQL into Databricks-native equivalents.
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Microsoft SQL Server
Move T-SQL transformation logic and warehouse models onto the Databricks lakehouse.
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THE MIGRATION LIFECYCLE
One controlled workflow across every migration.
UNDERSTAND → MAP → TRANSLATE → VALIDATE
Understand
Inventory the estate, profile data, identify dependencies, and assess complexity.
10,428 objects
Map
Generate source-to-target mappings, transformations, confidence, and exceptions.
97% mapping confidence
Translate
Convert SQL, procedures, and transformation logic for the target platform.
PL/SQL → target SQL
Validate
Test migrated outputs, reconcile source and target, and surface exceptions before cutover.
source = target ✓
PLANNING A DATABRICKS MIGRATION?
Tell us what you’re moving off.
Share your source platform, estate size, target lakehouse architecture, and migration goals. We’ll help assess complexity, define scope, and identify where automation removes manual delivery work.