Get the most out of your Teradata SQL database with AI

Combining frontier AI models with Teradata, you can move faster and more safely while getting more value from your analytical data platform.

Chat with your Teradata database

SQLAI uses frontier AI models to turn natural-language questions into Teradata SQL. In a long-running conversation, follow-up questions can refine filters and calculations or explore a question in more detail without starting over. This ensures that you always get exactly what you want and helps you better understand what the generated query does.

SQLAI does not execute generated queries against Teradata today. Review the SQL in the conversation, then run it in Teradata Studio, BTEQ, SQL Assistant or your application. Result rows are never added to the conversation automatically, but you can include a sample or summary in a follow-up if you want the model to consider it.

Try the Text-to-SQL demo to see a full conversation.

Import a Teradata schema

A Teradata schema can be pasted or uploaded as DDL, JSON or CSV, or described to the AI parser as documentation or another schema export. Direct connection is not available for Teradata. You can also select Teradata without attaching a schema. The public API engine name is teradata_sql; see all supported databases and versions.

The imported schema and optional database rules give the AI context about your tables and relationships, helping it generate accurate, relevant SQL. Rules are a useful place to record the database and user conventions, primary-index strategy, partitioning and spool-space expectations. SQLAI is built for large Teradata schemas: you don't have to trim your schema or paste it into every prompt, and the AI stays focused on the tables and columns that matter for your question as the schema grows. Learn more about how SQLAI keeps your database context in every chat.

See importing a Teradata schema in action.

Optimize Teradata queries

The SQL optimizer reviews slow Teradata queries and suggests rewrites, indexes or statistics changes intended to reduce execution time while preserving results. Teradata performance is closely tied to primary-index choice, secondary and join indexes, partitioning, join strategies and statistics. Spool space, skew and unnecessary redistribution often explain why a query that looks reasonable is slow.

Optional deep analysis can compare a baseline and proposed rewrite using advanced evaluation matrices. This provides even more accurate optimization suggestions. The rewrite and its performance should still be verified against the production workload.

EXPLAIN shows the access and join plan, while COLLECT STATISTICS gives the optimizer the demographics it needs. QUALIFY, TOP and SAMPLE can simplify queries that would otherwise need extra subqueries. See deep analysis in the SQL optimizer on a sample query.

Validate Teradata queries

The SQL validator performs an in-depth review of potential syntax and semantic issues, including invalid identifiers, type mismatches, ambiguous joins and Teradata-specific constructs. It explains findings and can suggest corrected SQL. Schema context can help with checking table names and relationships, but validation does not guarantee that a query matches the intended result.

Try the SQL validator on a query of your own.

Explain Teradata queries

The SQL explainer breaks down an existing query, including its joins, filters, aggregations, QUALIFY clauses and returned columns. Follow-up questions can focus on a particular clause, a primary-index decision or potential edge case.

Try the SQL explainer on a query you want to understand.

An imported Teradata database can be shared with teammates, who can use its schema and rules in their own chats. Only the owner can edit the database configuration. There is no live Teradata connection to share today, so teammates work from the shared schema rather than a live system.

Chat threads can be shared by invitation or link as read-only transcripts. Recipients can review the conversation, but only the owner can continue it. Query result rows are not saved in the thread.

Frequently asked questions

Do I need a live Teradata connection to use SQLAI?

No, and direct connections are not available for Teradata today. Import the schema by pasting or uploading DDL, JSON or CSV, or let AI parse a description of your schema. You can also select Teradata without attaching a schema.

Does SQLAI run queries against my database automatically?

No. SQLAI does not execute generated queries against Teradata today. Generated SQL is for you to review and run in Teradata Studio, BTEQ or your application. Result rows are not added to a chat thread automatically.

Can SQLAI handle large Teradata schemas?

Yes. SQLAI is designed for large schemas with many tables and relationships. You can keep your whole schema imported, and responses stay focused on the tables and columns that matter as it grows. Learn more about how SQLAI keeps your database context in every chat.

Which Teradata versions are supported?

There is no version picker for Teradata, so the supported databases page lists the engine as covering all versions. Add the release and platform details to the database rules if you rely on version-specific syntax or features.

What Teradata details improve generated SQL?

Record the primary-index and partitioning conventions, whether tables are MULTISET or SET, and any spool-space or workload constraints. Those details help the AI recommend realistic indexes and rewrites instead of generic SQL advice.

Review before production

AI-generated SQL queries can contain errors. Queries should be reviewed and their results and performance tested in the target Teradata environment before production use. Check the EXPLAIN plan, statistics, primary-index choice and spool usage on representative data.