BigQuery Data Warehouse

Modernizing a Legacy Teradata Estate for Enterprise Analytics and AI

3 min read

Mar 5, 2026 1:47:16 PM

Pythian migrated a decade-old Teradata warehouse to BigQuery without disrupting 24/7 operations

This organization built its business on Teradata's reliability. But as customers demanded real-time analytics and AI-driven insights, the legacy environment couldn't keep pace. Pythian delivered a full-stack modernization—cutting infrastructure costs by 45 percent and unlocking capabilities Teradata alone couldn't support.

45%

Reduction in infrastructure costs

$2.1M

Annual savings

99.99%

Peak-season availability

Account profile

Industry: Information Technology & Services

Organization scale: Global enterprise, $750M+ revenue

Tech stack:

Teradata (on-premises)
Google BigQuery
Apache Airflow and Fivetran
Apache NiFi and Google Cloud Dataproc
dbt, Looker, Vertex AI

When "Big Iron" becomes a ceiling

For over a decade, this IT services provider ran its core warehouse on an on-premises Teradata appliance—serving reporting, customer analytics, and SLA monitoring for hundreds of enterprise customers. The platform was stable, but it was approaching end-of-support, hemorrhaging budget, and blocking every modern analytics initiative.

End-of-support and vanishing talent

Teradata hardware and software were approaching end-of-support. Licensing renewals carried 20 percent annual increases. Experienced Teradata DBAs became nearly impossible to recruit.

3,000 scripts with no path to cloud

The environment relied on over 3,000 BTEQ scripts, hundreds of stored procedures, and a tangle of FastLoad, MultiLoad, and TPT batch jobs. Embedded business logic in Teradata-specific SQL extensions made lift-and-shift impossible. Data silos across the estate and three satellite systems meant no single source of truth.

Day-old dashboards in a real-time market

Batch reporting took 14 hours overnight—dashboards were always a day behind. The data team spent 70 percent of its time on maintenance. Customers wanted real-time SLA monitoring and predictive insights. The platform couldn't deliver.

From proprietary lock-in to cloud-native advantage

Pythian executed a phased modernization that preserved critical business logic while rebuilding the data stack for cloud-native performance. The full transformation took under 12 months from initial assessment to production analytics.

Discovery phase

We audited the full Teradata estate—scripts, stored procedures, ETL jobs, data models, and TASM configuration. We mapped dependencies across 3,000+ BTEQ scripts, three satellite systems, and 40+ upstream feeds. We stabilized mission-critical workloads to buy time for strategic planning.

Strategic architecture

Workload analysis and ROI modeling pointed to BigQuery. Three factors drove the decision: serverless scaling for unpredictable query volumes, native Vertex AI integration, and consumption-based pricing that eliminated Teradata's fixed-cost trap. The design consolidated the Teradata estate and three satellite systems into a single BigQuery environment—eliminating data silos.

Data platform

BigQuery serves as the core analytical warehouse. dbt handles transformation, testing, and documentation across the consolidated environment.

Data Integration

Airflow and Fivetran replace Teradata's proprietary ETL utilities. NiFi and Dataproc manage large-scale data integration across the consolidated source systems.

Analytics & AI

Looker delivers self-service BI and customer-facing dashboards. Vertex AI powers production ML workflows integrated directly into the data platform.

Implementation roadmap

Phase 1: Assessment and stabilization

We audited the full Teradata estate, stabilized workloads approaching end-of-support, mapped all dependencies, and delivered a vendor-neutral target platform recommendation.

Phase 2: Migration and execution

We refactored 3,000+ BTEQ scripts and stored procedures for BigQuery, re-engineered FastLoad, MultiLoad, and TPT jobs into Airflow and Fivetran pipelines, and ran parallel dual-run validation to ensure zero disruption.

Phase 3: Analytics and managed services

We deployed Looker dashboards to replace 14-hour batch reports, integrated Vertex AI ML workflows into the production platform, and transitioned the environment to 24/7 managed services.

From maintenance to production AI in under 12 months

The modernization transformed a maintenance-heavy legacy environment into a platform that drives revenue and competitive differentiation.

45% reduction in infrastructure costs

Eliminating Teradata licensing, hardware maintenance, and scarce DBA talent costs saved $2.1M per year.

60% faster query performance

Dashboards now refresh in near real time, and the data team reclaimed 70 percent of maintenance hours for new analytics and ML products.

99.95% system availability

Up from 99.5 percent on aging Teradata hardware, with governance and compliance controls built into BigQuery from day one.

Self-service BI and production AI

Looker eliminated the data team bottleneck, and Vertex AI now powers real-time SLA monitoring and predictive analytics for enterprise customers.

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