DatriseAI-first ETL

Dixa Amazon Athena

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

How Datrise loads Dixa into Amazon Athena

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics 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

Dixa: Customer service platform for conversations across channels.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Dixa entities map to Amazon Athena

Dixa entityAmazon Athena objectNotes
conversationsdixa_conversationsid PK · custom fields → struct/map columns in Parquet
agentsdixa_agentsid PK · linked to dixa_conversations
customersdixa_customersid PK · linked to dixa_conversations
tagsdixa_tagsid PK · linked to dixa_conversations

FAQ

How does Datrise handle Dixa'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 Dixa 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

Early access

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