DatriseAI-first ETL

Mailerlite Amazon Athena

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

How Datrise loads Mailerlite into Amazon Athena

Datrise syncs Mailerlite's records, events, and configuration objects 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

Mailerlite: SaaS or API data source for analytics and warehouse sync.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Mailerlite entities map to Amazon Athena

Mailerlite entityAmazon Athena objectNotes
recordsmailerlite_recordsid PK · custom fields → struct/map columns in Parquet
eventsmailerlite_eventstimestamp events
configuration objectsmailerlite_configuration_objectsid PK · linked to mailerlite_records

FAQ

How does Datrise handle Mailerlite'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 Mailerlite 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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