Teradata to Amazon Athena Integration

How Datrise loads Teradata into Amazon Athena

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Teradata: Teradata analytical database for enterprise workloads.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Teradata entities map to Amazon Athena

Teradata entity	Amazon Athena object	Notes
tables	`teradata_d_tables`	id PK · custom fields → struct/map columns in Parquet
rows	`teradata_d_rows`	id PK · linked to teradata_d_tables
schemas	`teradata_d_schemas`	id PK · linked to teradata_d_tables
indexes	`teradata_d_indexes`	id PK · linked to teradata_d_tables

FAQ

How does Datrise handle Teradata's custom fields in Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Teradata to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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