Data Model - Overview

What are Data Models?

  • What's Opensea's cross chain sales volume?
  • What's the average size of a DEX trade on Ethereum vs Polygon vs Avalanche?
  • How many transactions per second are processed on Arbitrum vs Optimism?
  • What tokens are the most profitable traders buying and selling?

Taking the traditional approach of writing SQL, these questions would require multiple data providers, a deep experience in the complexities of blockchain data, advanced skills in SQL and 1000s of lines of code.

On Increment, data models encode business logic - Reach, Retention, and Revenue - into an SQL compiler that write valid SQL for you. The tool takes every possible chart creatable with SQL-based tools and bakes them into a standardized, open-sourced set of dimensions and measures - known as a model.

Build Analytics In 3 Clicks.

  1. Pick a model: Data models are used to automatically write SQL for you based on the analytics you want to see. Choose from our robust collection of pre-built models, including JPEG Analysis, DEXscovery, Chain GDP, and more, engineered to answer any question you might have at any level of granularity.
  2. Pick a dimension: Dimensions allow you to group data together based on a specific attribute. For instance, if you want to calculate the daily count of transactions on Avalanche, you would use "day" as your dimension, as the count is performed on a daily basis. Dimensions function similarly to GROUP BY arguments in SQL.
  3. Pick a measure: Measures aggregate data by performing mathematical operations such as counting, summing, averaging, and more. For example, if you want to calculate the daily count of transactions on Avalanche, your measure would be "count transactions." Measures function similarly to aggregate SELECT statements in SQL.