TERADATA → BIGQUERY
Retire Teradata onto BigQuery with cost designed in from the start.
Datachecks assesses large Teradata estates, maps analytical structures to partitioned and clustered BigQuery tables, translates Teradata SQL into GoogleSQL, and reconciles results at warehouse scale.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
Teradata → BigQuery changes what performance costs.
Teradata performance is bought once through hardware and physical design. BigQuery charges per query, so distribution strategy and workload patterns translate into a running cost model rather than a capacity plan.
1
Human-owned
Capacity becomes cost per query
Teradata performance is bought once as hardware. BigQuery charges per query, so scan behaviour that was invisible on-premise becomes a monthly bill and has to be designed against.
2
Architect-owned
Physical design has no target
Primary indexes, distribution and join indexes do not exist in BigQuery. Their intent has to be re-expressed through partitioning and clustering, or deliberately dropped.
3
Accelerated translation
Teradata SQL dialect
QUALIFY, volatile tables, interval handling and Teradata-specific analytic patterns need GoogleSQL-aware conversion.
4
Expert review
BTEQ, macros and utilities
FastLoad, MultiLoad and BTEQ orchestration have no BigQuery equivalent and become ingestion jobs and scheduled queries — a rebuild rather than a translation.
5
Automated discovery
Estate scale and dead weight
Large Teradata environments hold thousands of objects, many unused. Deciding what actually justifies migration is what keeps the project bounded.
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 and BTEQ scripts, profile volumes and query patterns, and identify the workloads whose scan behaviour will drive BigQuery cost.
Teradata estate assessed for BigQuery
02 · MAP
Source → Target Mapping
Design partitioning and clustering, not indexes.
Map Teradata schemas into BigQuery tables with partitioning, clustering and column choices driven by real query patterns, with ambiguity routed to architect review.
Reviewed BigQuery target model
03 · TRANSLATE
SQL + Procedural Translation
Convert Teradata SQL into GoogleSQL.
Translate supported Teradata SQL, QUALIFY patterns and analytical constructs into GoogleSQL, and flag volatile tables, utilities and macros that need workflow redesign.
GoogleSQL 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 BigQuery, and produce reconciliation evidence for sign-off.
Reconciled BigQuery outputs
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CUSTOMER_ID DECIMAL(18,0) → customer_id NUMERIC
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
Teradata SQL → GoogleSQL
TRANSLATED · VALIDATED
EXCEPTION
Workload tuned around physical distribution
ARCHITECT REVIEW REQUIRED
DELIVERY TIME
Compress months of Teradata conversion into weeks.
Automate estate discovery, table design, repetitive SQL conversion, test generation and reconciliation across large analytical estates while experts own cost modelling and partitioning strategy.
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 BigQuery before retiring Teradata.
Compare critical datasets, transformations and business measures across Teradata and BigQuery, surface discrepancies early, and build evidence before 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
FAQ
Frequently asked questions
How do Teradata primary indexes translate to BigQuery?
What happens to BTEQ scripts and TPT loads?
Is Teradata SQL close to BigQuery standard SQL?
How does the Teradata view layer become a BigQuery model?
How is BigQuery cost managed compared to fixed Teradata capacity?
How do we prove BigQuery matches Teradata?
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