We map Teradata's existing security model, including database-level permissions, role-based access, and row-level security, to the target platform's identity and access management (IAM) framework. For regulated industries, we align the migration with HIPAA, SOC 2, PCI DSS, and GDPR requirements using each cloud provider's compliance controls. Data is encrypted in transit and at rest throughout the migration. Dual-run validation confirms that access policies and data masking rules carry over with zero gaps.

Teradata environments carry significant fixed costs: Hardware refreshes, per-node licensing, and specialized DBA talent that's increasingly hard to find. Optimization alone typically reduces infrastructure spend by 20 to 45 percent by eliminating dead tables, refactoring expensive queries, and right-sizing workload management. Migration to a cloud-native platform shifts you from fixed CAPEX to elastic OPEX, and most customers see payback within the first year. In one engagement, Pythian helped a customer cut annual infrastructure costs by $2.1 million after migrating select Teradata workloads to the cloud.

Complexity depends on code volume, business logic depth, and how heavily your environment relies on Teradata-specific features like TASM, MultiLoad, and FastExport. We use a combination of automated SQL translation tools, including SnowConvert, Google's batch translation, and the AWS Schema Conversion Tool, alongside manual engineering for logic that automation can't handle. Complex multi-statement BTEQ scripts and deeply nested stored procedures get refactored by engineers who know both Teradata and the target platform. Both environments run in parallel during validation so nothing breaks at cutover.

Yes. Many customers need to stabilize and optimize before they're ready to move. Pythian provides 24/7 managed services, including monitoring, alerting, workload management tuning, and proactive optimization, to keep your Teradata environment running at peak performance while you plan the next step. We call this "fix before you fly." You get senior Teradata expertise without hiring full-time specialists, and the assessment work feeds directly into a migration roadmap when you're ready.

A Teradata migration moves your data, business logic, ETL pipelines, and reporting layers from Teradata's on-prem warehouse to a cloud-native platform like Snowflake, BigQuery, Databricks, or Redshift. The process goes beyond moving data files. Proprietary Teradata SQL, BTEQ scripts, stored procedures, and workload management rules all need to be translated to the target platform's native syntax. The hardest part isn't the data itself—it's preserving the decades of business logic embedded in custom code while validating that every query returns identical results on the new platform.

Teradata isn't disappearing tomorrow, but the economics are shifting. Hardware refresh cycles, per-node licensing, and a shrinking talent pool make it increasingly expensive to maintain. Cloud-native platforms now match or exceed Teradata's query performance at a fraction of the fixed cost, with elastic scaling that Teradata's on-prem model can't replicate. Most enterprises we work with aren't asking "if" they'll move—they're asking "when" and "to what." The smart play is to stabilize and optimize now so you're migration-ready when the business case tips.

There are three core approaches. A lift-and-shift moves your data and logic to the cloud with minimal changes—it's fast but doesn't take advantage of cloud-native features. A re-platform translates your SQL and workloads to the target platform's native syntax while preserving core business logic—this is the most common approach for Teradata migrations. A re-architect redesigns the data model and pipelines from scratch to fully exploit the target platform's architecture (for example, converting Teradata's normalized star schemas into Databricks' lakehouse format). Most Teradata migrations use a re-platform strategy because it balances speed with long-term value.

Both are SQL-first analytics platforms, but they're built on fundamentally different economics. Teradata uses fixed-capacity, on-prem hardware with per-node licensing—you pay the same whether you're running one query or a thousand. Snowflake separates storage from compute and scales elastically, so you only pay for what you use. Snowflake also supports multi-cloud deployment (AWS, Azure, GCP), near-zero administration, and instant concurrency scaling. The trade-off is that Teradata's mature workload management and mixed-workload handling can be more predictable for very large, steady-state environments. Most customers migrate because the cost savings and operational flexibility outweigh the switching effort.

Teradata's SQL dialect includes proprietary extensions that don't exist on other platforms. Features like QUALIFY clauses, NORMALIZE/PERIOD temporal logic, hash-based primary index distribution, and SET vs. MULTISET table semantics all need to be translated or re-engineered. On top of that, BTEQ (Teradata's batch scripting tool), FastExport, MultiLoad, and TPT utilities have no direct equivalents on cloud platforms. The SQL itself may look similar, but the execution model, data distribution strategy, and utility ecosystem underneath are different enough that line-by-line translation doesn't work—you need engineers who understand both sides.