DatriseAI-first ETL

Twitter/X Ads Amazon S3 Data Lake

AI-first ETL from Twitter/X Ads into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Twitter/X Ads into Amazon S3 Data Lake

Datrise syncs Twitter/X Ads's campaign metrics, spend, engagement, and ad conversion performance into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Twitter/X Ads: Ad performance source for X campaign analytics.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Twitter/X Ads entities map to Amazon S3 Data Lake

Twitter/X Ads entityAmazon S3 Data Lake objectNotes
campaign metricstwitter_x_ads_campaign_metricsid PK · custom fields → nested struct/map fields in Parquet
spendtwitter_x_ads_spendid PK · linked to twitter_x_ads_campaign_metrics
engagementtwitter_x_ads_engagementid PK · linked to twitter_x_ads_campaign_metrics
ad conversion performancetwitter_x_ads_ad_conversion_performanceid PK · linked to twitter_x_ads_campaign_metrics

FAQ

How does Datrise handle Twitter/X Ads's custom fields in Amazon S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the Twitter/X Ads to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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