Polymarket Market Data Integration with LiveFetch Functions – Blog Overview

Polymarket Data Gets a Boost from Dune’s LiveFetch – A Case Study in On‑Chain / Off‑Chain Integration

By [Your Name] – June 2024

Overview

A recent Dune blog post demonstrates how the platform’s new LiveFetch SQL functions can pull real‑time off‑chain data into on‑chain analytics. Using Polymarket—a decentralized prediction‑market protocol built on Polygon—the author showcases a dashboard that merges live market metadata from Polymarket’s Gamma Markets API with blockchain events captured on Dune. The result is a richer, more granular view of prediction‑market activity that would be impossible with on‑chain data alone.

The Problem: Off‑Chain Gaps in On‑Chain Dashboards

Traditional Dune dashboards are fed exclusively by data already stored in the platform’s on‑chain tables. While this works well for raw transaction metrics (e.g., volume of OrderFilled events), it leaves a blind spot for any information kept off‑chain: market descriptions, categorisation, outcome identifiers, and other metadata that Polymarket stores outside the Polygon ledger.

Analysts who attempted to bridge the gap previously resorted to static CSV uploads or external scripts that manually refreshed queries. Both methods faltered when data needed to be refreshed continuously, as prediction‑market conditions can change minute by minute.

LiveFetch: Real‑Time HTTP Calls from SQL

Dune’s LiveFetch family of functions (e.g., http_get) lets a query issue HTTP requests to any public or private endpoint and parse the JSON response directly inside the SQL engine. By converting the response into an array of JSON objects and unnesting it, analysts can treat external data as if it were a native table.

Key capabilities highlighted in the blog:

• Dynamic data retrieval – each query fetches the latest API payload, eliminating stale CSV imports.
• Full JSON parsing – functions such as json_parse, json_extract_scalar, and try_cast let users flatten complex structures into relational columns.
• Scalability controls – parameters like limit, offset, and minimum volume thresholds keep API usage within Dune’s execution quotas.

Polymarket’s Hybrid Architecture

Polymarket runs its core trading logic on Polygon, ensuring low‑cost, fast settlement. However, market‑level details—questions, answer sets, categorisation, and resolution timestamps—are stored off‑chain and accessed via the Gamma Markets API. The API also maps each market to its CLOB token IDs, which correspond to the makerAssetId and takerAssetId fields recorded in the on‑chain events.

Building the Integrated Dashboard

The author, data engineer Fernando Molina, outlines a step‑by‑step workflow:

1. Fetch off‑chain market metadata – http_get('https://api.gamma.polymarket.com/v1/markets?limit=100&offset=…').
2. Parse the JSON payload – use UNNEST(TRY_CAST(json_parse(json_data) AS ARRAY(JSON))) to expand each market into individual rows.
3. Extract fields – JSON_EXTRACT_SCALAR(market, '$.question') for the market title, JSON_EXTRACT(market, '$.outcomes') for possible answers, and token IDs for linking.
4. Filter noise – apply a minimum USD volume filter (volume_num_min = 50000) so low‑activity markets do not blow up API call limits.
5. Join to on‑chain tables – match clob_token_id from the API with makerAssetId/takerAssetId from Polygon OrderFilled events.
6. Aggregate metrics – calculate daily and monthly USD volume per market and per outcome, then visualise the results in a Dune dashboard.

The final product, published at dune.com/fergmolina/polymarket-markets-data, displays volume split by market category (election‑related vs. non‑election), outcome‑level breakdowns, and temporal spikes for real‑world events.

Results and Insights

One striking observation from the integrated view is the surge in betting volume on September 18 2024, the day the U.S. Federal Reserve cut rates. The dashboard shows that the Fed‑cut‑related markets outperformed even the US‑Election markets in daily volume, a nuance that the on‑chain‑only version missed entirely.

Other takeaways include:

• Improved market attribution – analysts can now trace exactly which market and which outcome contributed to a given on‑chain trade.
• Dynamic category analysis – the ability to pull the latest market tags from Gamma enables quick segmentation (e.g., political vs. sports vs. crypto).
• Resilience through materialised views – by persisting the API response in a materialised view, query costs drop dramatically and the dashboard remains functional if the external API experiences brief outages.

Industry Implications

LiveFetch opens the door for Dune users to enrich on‑chain analytics with any publicly accessible data source: price feeds, oracle snapshots, DeFi protocol parameters, or even social‑media sentiment APIs. For prediction‑market protocols like Polymarket, the technique bridges the “metadata gap” that has long limited deep behavioural analysis.

The success story also signals a broader trend: hybrid data pipelines that blend trusted blockchain records with auxiliary off‑chain intelligence. As more DeFi projects expose RESTful APIs for governance, market design, or user‑profile data, tools like LiveFetch will become essential for delivering fully contextual insights.

Key Takeaways

About the Author

Fernando Molina is a data engineer with extensive experience in telecom, finance, and crypto analytics. Former Lead Data Engineer at The Defiant, he runs the Spanish‑language blog Bloque X and contributes a variety of Dune dashboards covering Ethereum, DAOs, micro‑payment apps, and meme‑coins.

The analysis presented here reflects the author’s perspective and does not constitute an official statement from Dune.

Source: https://dune.com/blog/polymarket-markets-data-a-success-story-for-livefetch-functions