REDSHIFT → DATABRICKS
Move Redshift workloads to Databricks without carrying the cluster model across.
Datachecks inventories Redshift clusters, maps schemas and distribution logic to Delta tables, translates Redshift SQL into Spark, and reconciles migrated data while experts handle workload and cost-model decisions.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Redshift → Databricks trades one compute model for another.
Distribution keys, sort keys, and cluster sizing are Redshift-specific optimizations with no Delta equivalent. They have to be understood, then deliberately discarded or replaced rather than translated.
1
Architect-owned
Distribution and sort keys
DISTKEY and SORTKEY choices encode years of query tuning. Delta has no equivalent, so that intent must be understood and re-expressed through partitioning and clustering rather than copied.
2
Accelerated translation
Redshift SQL dialect
Redshift-specific functions, column encodings, and syntax quirks need Spark-aware translation, and some tuning-driven query shapes stop making sense on the target.
3
Human-owned
Compute and cost model
Redshift bills for a provisioned cluster; Databricks bills for compute you size per workload. Migration is the moment to re-scope jobs rather than replicate cluster assumptions.
4
Expert review
Stored procedures and materialized views
Redshift procedures and materialized views need a decision each: convert to Spark SQL, rebuild as a workflow, or replace with a Delta pattern.
5
Automated discovery
Downstream consumers
BI tools, extracts, and applications connect directly to Redshift. Those connections and their expectations need cataloguing before the cluster can be switched off.
6
Automated
Reconciliation before decommission
The Redshift cluster is the only reference for correctness. Comparison across counts, aggregates, and transformations has to happen while it is still running.
HOW DATACHECKS HELPS
Understand. Map. Translate. Validate.
The migration is executed through controlled agent workflows, with migration experts reviewing ambiguity, unsupported patterns, business rules, and critical exceptions.
01 · UNDERSTAND
Assessment + Discovery
Inventory the Redshift estate and its optimizations.
Catalogue schemas, tables, views, stored procedures, and materialized views, and record the distribution and sort strategies that current query performance depends on.
Redshift estate inventory with optimization context
02 · MAP
Source → Target Mapping
Map Redshift schemas to Delta.
Translate Redshift DDL and data types into Delta definitions, and replace distribution and sort strategies with partitioning, file sizing, and clustering decisions on the target.
Delta mappings with target optimization decided
03 · TRANSLATE
SQL + Procedural Translation
Convert Redshift SQL into Spark SQL.
Translate supported Redshift SQL, procedures, and Redshift-specific functions into Spark SQL, flagging constructs that depend on cluster-local behaviour.
Spark SQL with cluster-dependent patterns flagged
04 · VALIDATE
Testing + Reconciliation
Compare Redshift and Delta outputs directly.
Generate tests across migrated tables, compare counts and aggregates, validate transformations, and reconcile before the Redshift cluster is decommissioned.
Reconciled Delta outputs
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
customer_id BIGINT ENCODE az64 → customer_id BIGINT
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Redshift SQL → Spark SQL
TRANSLATED · VALIDATED
EXCEPTION
Query tuned around DISTKEY and SORTKEY behaviour
ARCHITECT REVIEW REQUIRED
DELIVERY TIME
Compress months of Redshift migration into weeks.
Automate cluster inventory, Delta mapping, repetitive SQL conversion, test generation, and reconciliation while experts decide the target optimization and cost model.
TRADITIONAL MIGRATION
MONTHS
Understand
Map
Translate
Test & validate
Reconcile
Cutover
WITH DATACHECKS
WEEKS
Understand
Map
Translate + test
Validate + reconcile
Cutover
Why the timeline shrinks: automated estate analysis · generated mappings · accelerated SQL translation · generated tests · automated reconciliation. Bar lengths are illustrative, not project commitments.
MIGRATION CONFIDENCE
Validate Delta before decommissioning the cluster.
Validate translated logic and migrated tables against Redshift, reconcile critical results, and resolve exceptions while the source cluster is still running.
TRANSLATE
TEST ✓
TARGET LOAD
VALIDATE ✓
RECONCILE ✓
EXCEPTIONS REVIEWED ✓
READY FOR CUTOVER
HUMAN IN THE LOOP
Automate the repeatable work. Keep experts on the decisions.
AGENT EXECUTION
Estate inventory · metadata analysis · profiling · standard mappings · common SQL translation · test generation · row-count checks · aggregate comparisons
EXPERT REVIEW
Ambiguous mappings · unsupported procedural patterns · complex logic · unusual transformation patterns · reconciliation discrepancies
HUMAN-OWNED
Business-rule interpretation · critical exception resolution · acceptance criteria · migration scope decisions · cutover approval
ENTERPRISE DEPLOYMENT
Run the migration engine where the data lives.
Deploy Datachecks within enterprise-controlled infrastructure, connect approved AI models, and keep migration data, metadata, execution, and evidence inside your security boundary.
Private Deployment
BYOM
SAML SSO
SOC 2
ISO 27001
RELATED RESOURCES
Go deeper on this migration.
PLANNING THIS MIGRATION?
Start with the estate you already have.
Share your source environment, target architecture, approximate object volumes, and migration goals. We’ll help identify complexity, scope, and where automation can remove manual delivery work.
Useful to bring: source schemas · object counts · procedural code volume · target architecture · timelines