DatriseAI-first ETL

Twitter/X Ads Azure Data Lake Storage

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

How Datrise loads Twitter/X Ads into Azure Data Lake Storage

Datrise syncs Twitter/X Ads's campaign metrics, spend, engagement, and ad conversion performance into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 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 to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

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

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How Twitter/X Ads entities map to Azure Data Lake Storage

Twitter/X Ads entityAzure Data Lake Storage 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 Azure Data Lake Storage?

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 Azure Data Lake Storage types.

How does the Twitter/X Ads to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

Related pipelines

Early access

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