DatriseAI-first ETL

Parquet File to Amazon Athena Integration

AI-first ETL from Parquet File into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads Parquet File into Amazon Athena

Datrise syncs Parquet File's row groups, columns, schemas, partitions, file batches, and column statistics 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

Parquet File: Columnar Parquet files from lakes and exports.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Parquet File entities map to Amazon Athena

Parquet File entityAmazon Athena objectNotes
row groupsparquet_file_row_groupsid PK · custom fields → struct/map columns in Parquet
columnsparquet_file_columnsid PK · linked to parquet_file_row_groups
schemasparquet_file_schemasid PK · linked to parquet_file_row_groups
partitionsparquet_file_partitionsid PK · linked to parquet_file_row_groups

FAQ

How does Datrise handle Parquet File'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 Parquet File 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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