data-pipeline

To build a GDELT ETL pipeline, you orchestrate scheduled polling and raw ingestion of data exports, then normalize and filter records for curated outlets while preserving data lineage. This establishes a structured foundation for downstream analytics.

A Railway-oriented data pipeline architecture enforces sequential, track-based data flow from ingestion to serving. It uses Mentions as the participation spine, enriching them with GKG and Events, and routing processed data into historical BigQuery tables.

Structuring BigQuery serving tables for media analytics involves designing derived analytics layers and materialized tables. This separates heavy historical processing from application access, enabling efficient queries for analyst dashboards.

Yes, you can use Convex for app state and lightweight payloads in a data pipeline. While BigQuery maintains the heavy historical data, Convex manages the application state and serves optimized, lightweight payloads to frontend interfaces.

Filtering and normalizing GDELT mentions for regional outlets requires applying early filtering to curated source lists during ingestion. This isolates relevant regional data, such as Romanian media, while preserving the original data lineage.

You should use Mentions as the participation spine when building media intelligence platforms that require linking multiple data dimensions. This approach anchors the pipeline by attaching GKG and Events enrichments directly to mention records.