TERADATA → DATABRICKS
Move Teradata analytical workloads onto the Databricks lakehouse.
Datachecks assesses large Teradata estates, maps analytical structures to Delta tables, translates Teradata SQL and workload logic into Spark, and reconciles results at warehouse scale with migration experts reviewing architectural decisions.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Teradata → Databricks changes both the engine and the model.
Teradata's tightly coupled physical design and set-based analytical SQL meet a decoupled storage and compute model. Distribution strategy, workload orchestration, and analytical semantics all need rethinking rather than replicating.
1
Architect-owned
Physical design does not carry over
Teradata primary indexes, distribution, and join indexes are engine-specific tuning. On Delta they are replaced by partitioning, file sizing, and clustering — a redesign, not a translation.
2
Accelerated translation
Teradata SQL dialect
QUALIFY, volatile tables, Teradata date and interval handling, and platform-specific analytic patterns need Spark-aware conversion.
3
Expert review
BTEQ scripts and utilities
FastLoad, MultiLoad, and BTEQ orchestration have no lakehouse equivalent. These become workflow tasks and ingestion jobs, which is a rebuild rather than a port.
4
Expert review
Macros and stored procedures
Teradata macros and procedures often encode reporting logic used by many downstream consumers. Each needs a decision on whether it becomes Spark SQL, a workflow, or is retired.
5
Automated discovery
Estate scale
Large Teradata environments hold thousands of objects, many unused. Classifying what actually justifies migration is the difference between a bounded project and an open-ended one.
6
Automated
Warehouse-scale reconciliation
Analytical estates need comparison across counts, aggregates, historical periods, and business measures rather than sampled spot checks.
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
Classify the Teradata estate before rebuilding it.
Inventory tables, views, macros, BTEQ scripts, and analytical workloads; profile volumes and identify which workloads justify migration to the lakehouse at all.
Teradata estate assessed for lakehouse fit
02 · MAP
Source → Target Mapping
Map analytical structures to Delta.
Translate Teradata schemas and analytical structures into Delta table definitions with partitioning, file sizing, and medallion layer decisions surfaced for architect review.
Reviewed Delta target model
03 · TRANSLATE
SQL + Procedural Translation
Convert Teradata SQL into Spark SQL.
Translate supported Teradata SQL, QUALIFY patterns, and macro logic into Spark SQL or PySpark, and flag volatile-table and utility patterns that need workflow redesign.
Spark SQL with redesign exceptions flagged
04 · VALIDATE
Testing + Reconciliation
Reconcile at warehouse scale.
Generate tests across critical datasets, compare aggregates and business measures between Teradata and Delta, and produce reconciliation evidence for sign-off.
Reconciled lakehouse outputs
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID DECIMAL(18,0) → customer_id DECIMAL(18,0)
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Teradata SQL → Spark SQL
TRANSLATED · VALIDATED
EXCEPTION
BTEQ utility flow with no direct workflow equivalent
ARCHITECT REVIEW REQUIRED
DELIVERY TIME
Compress months of Teradata-to-lakehouse conversion into weeks.
Automate estate discovery, Delta mapping, repetitive SQL conversion, test generation, and reconciliation across large analytical estates while experts own physical-design and orchestration decisions.
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 Databricks before retiring Teradata.
Compare critical datasets, transformations, and business measures across Teradata and Delta, surface discrepancies early, and build evidence before production cutover.
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
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