DatriseAI-first ETL

Microsoft Teams to Amazon Athena Integration

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

How Datrise loads Microsoft Teams into Amazon Athena

Datrise syncs Microsoft Teams's teams, channels, messages, users, meetings, and call records 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

Microsoft Teams: Team chat, channels, and meeting activity.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Microsoft Teams entities map to Amazon Athena

Microsoft Teams entityAmazon Athena objectNotes
teamsmicrosoft_teams_teamsid PK · custom fields → struct/map columns in Parquet
channelsmicrosoft_teams_channelsid PK · linked to microsoft_teams_teams
messagesmicrosoft_teams_messagesid PK · linked to microsoft_teams_teams
usersmicrosoft_teams_usersid PK · linked to microsoft_teams_teams

FAQ

How does Datrise handle Microsoft Teams'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 Microsoft Teams 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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