DatriseAI-first ETL

ConfigCat to Amazon Athena Integration

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

How Datrise loads ConfigCat into Amazon Athena

Datrise syncs ConfigCat's feature flags, settings, environments, config values, evaluations, and targeting rules 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

ConfigCat: Feature flag and remote configuration service.

Amazon Athena: Serverless SQL over S3 data lake tables.

How ConfigCat entities map to Amazon Athena

ConfigCat entityAmazon Athena objectNotes
feature flagsconfigcat_feature_flagsid PK · custom fields → struct/map columns in Parquet
settingsconfigcat_settingsid PK · linked to configcat_feature_flags
environmentsconfigcat_environmentsid PK · linked to configcat_feature_flags
config valuesconfigcat_config_valuesid PK · linked to configcat_feature_flags

FAQ

How does Datrise handle ConfigCat'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 ConfigCat 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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