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Footprint Analytics lets anybody, regardless of experience with analytics, explore and understand the data of the blockchain. The blockchain goes beyond crypto tokens, and Footprint allows analysts to gain a fuller picture about almost any GameFi title, NFT collection, marketplace, or DeFi protocol.

What are some things you can do with Footprint's suite of products?

  • Investigate where FTX investors moved their crypto after the exchange's collapse
  • Use wallet analysis to find out whether OG BAYC holders are still interested in Yuga Labs' new projects like Otherdeed for Otherside
  • Find out which chains are the least volatile in light of adverse market conditions

We built Footprint Analytics to make blockchain data accessible so that a retail investor, crypto reporter, or VC researcher with years of SQL experience can look into these questions equally.

Let's take EVM chains as an example. At the base level, blockchain data consists of smart contracts—blocks of transactions. These transactions have logs and traces, and smart contracts can also emit critical info.

Using SQL or Python, a technically-trained data analyst could use this data to answer questions like the ones above. However, beginners cannot.

At Footprint, we've developed a model that aggregates this raw data and indexes it to be meaningful.

The info about these millions of transactions is broken up by domain—our data engine determines whether it can be classified as GameFi, NFT, DEX, or other. We decode this data so analysts can search for the information they need, like block time, TVL, token price, etc., and immediately display that data on a chart.

Instead of strings of numbers and letters that are, to most, indecipherable, you have wallet addresses, chains, NFT collections, and other meaningful categories.


On the other hand, experienced analysts who want more flexibility can also work with raw data using SQL or Python.

Building a data engine that is the most comprehensive in the industry (we currently cover 22 chains) while retaining best-in-class performance was no easy feat of engineering.

The following article explains our data design in-depth.

How Footprint Analytics balances flexibility and simplicity

Footprint web application is built on Metabase open source technology. Read more about Metabase:

We use Metabase because it is open—the technology allows users to contribute to the code base, developing and improving it over time.

For example, in the latest update of Metabase the models are introduced. This functionality is allowing users to curate data from another table or tables from the same database to anticipate the kinds of questions people will ask of the data.

Analysts can create charts on the Footprint Analytics platform with a convenient drag-and-drop query builder. This capability significantly lowers the barrier to entry, allowing any user without technical knowledge to use the product and extract business value.

You may find the relevant guides in the following chapter:

It is important to note that, architecturally, Metabase is an abstraction over SQL code; that is, any request made by drag and drop can be represented as SQL. Thus, users who want to build more complex queries or who prefer to work with data using code have the opportunity to use SQL straight away.

Other platforms

Many alternative analytics solutions allow the user to analyze different networks according to various levels of requirements. However, for the most part, alternative solutions tend to go to extremes, implementing either a very flexible product that requires knowledge of query languages ​​or even programming languages ​​a very simple interface with prepared scripts and, accordingly, low flexibility.


We have one of the widest coverages in the entire market. We describe the current coverage in detail, referring to the organization of the data (levels, domains), within the following section: