Context
GoodDollar's onchain data was scattered across subgraphs, Google Apps Script pipelines, block explorers, and loose Dune dashboards -- no centralized, reliable view of protocol activity existed. The idea emerged in early 2026 to build a proper analytics system.
Journey so far:
- Hypersync discovery -- Hadar pointed to Hypersync as an indexing solution; studied it, built the first ingestion pipeline
- BigQuery as the warehouse -- researched storage options; chose BigQuery for cost, public dataset capability, and SQL familiarity
- MVP: XDC invites pipeline -- first end-to-end pipeline (Hypersync to BigQuery to Looker Studio dashboard), validating the architecture
- dbt migration -- 10 models across staging/semantic/marts, 57 tests, full documentation and governance framework
- Production deployment -- all models live in BigQuery, dbt docs site operational
Goal
A production-grade onchain analytics system: centralized, automated, documented, and expandable -- so any team member can query protocol activity without asking the data team.
Scope
Current Status
Production-deployed with 10 models and 57 passing tests. Data freshness stale since Jun 7 -- pipeline hardening is the critical path to fix this. Hadar greenlit the approach after a demo (Jul 6).
Value
Moves GoodDollar from "ask Thales to pull the data" to "query it yourself." Foundation for every dashboard, metric, and reporting cadence downstream.
References
- Project folder:
projects/onchain-analytics/
- dbt docs: generated locally via
dbt docs generate
- dbt models:
projects/onchain-analytics/gd_dbt/
Context
GoodDollar's onchain data was scattered across subgraphs, Google Apps Script pipelines, block explorers, and loose Dune dashboards -- no centralized, reliable view of protocol activity existed. The idea emerged in early 2026 to build a proper analytics system.
Journey so far:
Goal
A production-grade onchain analytics system: centralized, automated, documented, and expandable -- so any team member can query protocol activity without asking the data team.
Scope
Current Status
Production-deployed with 10 models and 57 passing tests. Data freshness stale since Jun 7 -- pipeline hardening is the critical path to fix this. Hadar greenlit the approach after a demo (Jul 6).
Value
Moves GoodDollar from "ask Thales to pull the data" to "query it yourself." Foundation for every dashboard, metric, and reporting cadence downstream.
References
projects/onchain-analytics/dbt docs generateprojects/onchain-analytics/gd_dbt/