This article is a translation of Dune's official documentation: Getting Started
About Dune
Whether you're an experienced SQL developer, blockchain researcher, business analyst, or any of the above, you can start analyzing blockchain data right away with Dune.
Dune has created many useful guides and tools to help you become a mature Dune user.
The fastest way to get started with Dune is to watch our getting started video series here and check out our getting started dashboard here.
The next step of learning depends on:
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Already know SQL
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System learning or learning by using
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Systematic learning, first read and watch a lot of content to get a general understanding, and then start to practice.
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Learn by doing is for those who learn by doing. If you already know what you want to do with Dune and just need to unlock yourself by finding some specific cases, then the core functionality may contain the information you need. Tables and Spellbook are more advanced features worth checking out.
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If you don't know SQL, it is recommended to start by learning SQL
How to become an on-chain data analyst
SQL skills
To successfully query data on Dune, a basic understanding of SQL is required
SQL is widely used in the software development industry, you can find many learning documents about SQL and most of the answers can be easily found on the internet.
(You can add me on WeChat data_cola to get the SQL quick reference guide)
Dune runs on PostgreSQL 12.2.
Parsing Ethereum Virtual Machine data
Almost all data on the chain and in the Dune database comes from the Ethereum Virtual Machine environment.
Therefore, understanding how the Ethereum Virtual Machine and smart contracts work as a whole is an important foundation for understanding and using most of the data available in Dune.
If you're able to read most of your data in Etherscan, you're well on your way to creating insightful queries and visualizations with Dune.
However, because each smart contract has its own rules, there is currently no single tool that can integrate all resources.
Know what communities, protocols, and businesses care about
A key part of being a data analyst is knowing how to separate the "signal from the noise".
Some data is interesting and valuable, and some data is not. To become an excellent data analyst, how to make data clear and valuable is a crucial part.
You can ask yourself a question:
What data does my audience/community/project/company need to make better decisions?