Workflow:Dagster io Dagster Bluesky Analytics
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Analytics, Social_Media |
| Last Updated | 2026-02-10 12:00 GMT |
Overview
End-to-end process for building a social media analytics pipeline that ingests Bluesky data, transforms it with dbt, and presents results in a Power BI dashboard, orchestrated by Dagster.
Description
This workflow demonstrates how to build a multi-layer analytics pipeline with Dagster that integrates a social media API, SQL-based data modeling, and business intelligence visualization. It implements a custom Dagster resource for the Bluesky API with built-in rate limiting, loads social media data into a database, applies dbt transformations to create analytical models, and configures a Power BI dashboard for business user consumption. The pipeline showcases the integration of custom API resources with the dbt component system and BI tool connectors.
Usage
Execute this workflow when you need to build an analytics pipeline that ingests data from a rate-limited API, applies dimensional modeling with dbt, and presents results in a BI tool. This pattern applies broadly to social media analytics, marketing data pipelines, or any scenario combining API ingestion with SQL transformation and dashboard presentation. Requires Bluesky API access, dbt, and Power BI.
Execution Steps
Step 1: API Resource and Data Ingestion
Build a custom Dagster resource that wraps the Bluesky API with authentication, pagination, and rate limit handling. The resource provides a clean interface for fetching social media posts, user profiles, and engagement metrics. Data is loaded into the database as raw ingestion assets.
Key considerations:
- Custom resource encapsulates API authentication and session management
- Rate limiting prevents API throttling during bulk data collection
- Pagination support handles APIs that return results in pages
- Raw ingestion assets provide a clean boundary between extraction and transformation
Step 2: Rate Limiting Implementation
Implement intelligent rate limiting that respects the Bluesky API's usage constraints. The rate limiter tracks request counts within sliding time windows and automatically delays requests when approaching limits. This ensures reliable data collection without triggering API bans.
Key considerations:
- Rate limiting is built into the custom resource, transparent to asset code
- Sliding window tracking adapts to varying API rate limit policies
- Automatic retry with backoff handles transient rate limit responses
- Rate limit metrics can be recorded as asset metadata for monitoring
Step 3: dbt Data Modeling
Transform raw social media data into analytical models using dbt orchestrated through Dagster's component system. Staging models clean and normalize raw data. Intermediate models join and enrich datasets. Mart models produce business-ready aggregations (engagement metrics, trending topics, user activity).
Key considerations:
- dbt models automatically become Dagster assets via DbtProjectComponent
- The transformation layer depends on upstream ingestion assets for lineage
- Incremental models enable efficient processing of time-series social media data
- dbt tests validate data quality at each transformation stage
Step 4: Dashboard Configuration
Connect the transformed data to a Power BI dashboard for business user visualization. The dashboard presents key social media metrics including engagement trends, content performance, and audience demographics. Dagster tracks the dashboard as a downstream asset for full pipeline lineage.
Key considerations:
- Power BI connects to the same database populated by dbt models
- Dashboard refresh can be triggered by Dagster after dbt model materialization
- Full lineage from API ingestion through transformation to visualization is maintained
- Dashboard configuration is managed alongside the pipeline for version control