Understanding Dune’s Table Types: From Raw Blocks to Curated Spellbook Models
By [Your Name] – March 4 2026
The Dune analytics platform now hosts tens of millions of tables that span raw blockchain extracts, decoded records, materialised views and community‑curated datasets. A recent walkthrough video breaks down this hierarchy and offers practical guidance for analysts, developers and data‑hungry traders looking to tap the platform’s full potential.
The Table Landscape on Dune
| Layer | Purpose | How it’s built | Typical prefix |
|---|---|---|---|
| Raw tables | Direct dumps of blockchain RPC payloads (e.g., block headers, transaction receipts) | Ingested automatically from on‑chain nodes | eth_, bsc_, etc., reflecting the source chain |
| Decoded tables | Human‑readable versions of raw logs and calls, generated by applying contract ABIs | Decoding pipelines that translate hex‑encoded fields into named columns | decoded_ (often implicit in the table name) |
| Materialised views | Pre‑computed query results that can be referenced by other analyses | Result of scheduled SQL jobs that “materialise” common patterns such as token transfers | result_ |
| Upload tables | User‑supplied CSV/JSON files that supplement on‑chain data | Uploaded via Dune’s UI and stored under the user’s namespace | dataset_ |
| Curated/Spellbook tables | Community‑maintained models that package best‑practice logic for common DeFi constructs (e.g., DEX trades, ENS labels) | Developed in the open‑source Spellbook repo, then published as reusable tables | query_ for the underlying view, with friendly aliases in Spellbook |
The naming conventions act as visual cues, enabling users to instantly recognise a table’s origin and intended use.
From Blockchain to Insight: Data Lineage
- Ingestion – Raw RPC calls feed a massive “raw” layer that mirrors the exact on‑chain state.
- Interpretation – ABIs supplied by the community decode event logs and function calls, populating the decoded layer.
- Community Enrichment – Developers contribute SQL transformations that stitch together decoded data into cohesive, reusable datasets. These are published in the Spellbook repository, which now contains over 4,000 models ranging from token metadata to complex trade aggregations.
- User Augmentation – Analysts can upload bespoke datasets (e.g., off‑chain price feeds) that join the on‑chain tables, extending the analytical surface.
- Consumption – Materialised views (
result_tables) provide fast, ready‑to‑query snapshots for dashboards and downstream queries.
Understanding this pipeline is crucial for pinpointing the most efficient source for a given analytical task. For instance, a query that needs the latest ERC‑20 transfer events should start from the decoded table rather than the raw logs, while a high‑level DEX‑volume dashboard will most likely rely on a curated Spellbook model.
Practical Tips for Navigating the Ecosystem
- Leverage the prefixes – When exploring the data explorer, filter by
dataset_,result_orquery_to narrow the search to uploads, materialised views or Spellbook assets respectively. - Use the built‑in dashboards – Dune’s UI lets you drop a contract address or transaction hash into a helper widget, which then surfaces the relevant event and function tables automatically.
- Check the GitHub source – Every Spellbook model is linked to a GitHub file that contains the original SQL. Reviewing the code helps verify assumptions and adapt the logic for custom use‑cases.
- Request missing data – If a needed table is absent, the community encourages open issues on the Spellbook repo. Contributors frequently respond with new models or guidance on how to construct the required query.
Analysis: Why Table Transparency Matters for DeFi
The sheer volume of tables on Dune can be daunting, but the platform’s structured naming and open‑source curation mitigate the risk of data silos. By exposing the lineage from raw RPC calls to community‑curated models, Dune reduces the friction that traditionally forced analysts to roll their own extraction pipelines. This transparency also improves auditability—a critical factor when building on‑chain risk dashboards, compliance tools, or real‑time trading bots.
Moreover, the Spellbook repository acts as a living standard. As DeFi protocols evolve, contributors can update the underlying SQL, and all downstream dashboards instantly benefit from the refreshed logic. The ability to request new tables via GitHub further democratizes data availability, turning the platform into a collaborative data‑engineering hub rather than a static data lake.
Key Takeaways
- Identify table types quickly using the
dataset_,result_, andquery_prefixes; this saves time when building or debugging queries. - Prefer decoded or curated tables for most analytics, as they already apply ABI decoding and community‑vetted logic.
- Explore Spellbook for ready‑made models covering tokens, ENS, contracts, and trade data; each model’s source SQL is publicly available.
- Upload custom datasets when the on‑chain data set does not meet specific needs, and join them with existing tables for richer insights.
- Engage with the community on GitHub to request missing tables or contribute improvements, ensuring the ecosystem stays responsive to emerging DeFi trends.
By mastering Dune’s table taxonomy, analysts can navigate the platform’s massive data catalog with confidence, extract more accurate insights, and contribute to a shared knowledge base that underpins the next generation of DeFi analytics.
Source: https://dune.com/blog/understanding-tables-raw-decoded-curated-spellbook-views-uploads
