Motivation
It all starts with The Graph. The Graph is an open-source decentralized indexing protocol used by data teams to query, process, store and load data from various networks and chains i.e Ethereum, Fanthom, IPFS etc
In other words, The Graph provides data processing layers that makes it easy for data-driven applications or environments to query blockchain data that is generally difficult to query directly.
The data processing layers are known as Subgraphs. Subgraphs allow users to define data models and storage methods to index and execute. Using Subgraphs as the data processing layer, users can build data-driven applications that query the protocol for indexed blockchain data and receive relevant responses.
While The Graph makes it easier to index, query and understand data across blockchains there are a couple of difficulties to using it effectively:
It requires specialized technical knowledge. Data Teams and general users need to know a specialized programming language, GraphQL, and understand the process of writing subgraphs to query indexed data. Writing subgraphs is a skill in and of itself that takes time and effort to become proficient in. As a result, poorly written subgraphs can lead to incomplete or inaccurate data.
Data queried from Subgraphs is not always human friendly: Data queried from the subgraph is often not transformed to a state that is ready for everyday data analytics environment or easily human-readable. As a result, there is generally the task of unpacking or reprocessing the subgraph data into a more familiar or approachable format.
Playgrounds seek to extend and augment the data analytics capabilities made possible by The Graph by creating open-source tools that make interfacing with The Graph and the vast libraries of subgraphs easier and accessible. Subgrounds extends and augments the data analytics capabilities of The Graph by providing a pythonic environment that is familiar, easy to use, and open-source. Using Subgrounds, anyone can leverage their existing python data analytics environment and workflows for blockchain data.
There are four main motivations behind Subgrounds:
Leverage The Graph network and its vast and rapidly expanding library of modeled data
Leverage Python for its immense data science and analytics ecosystem
Recover the Web2 data science stack in Web3
Empower data scientists, analysts, engineers, hobbyists and teams with an advanced, yet accessible, set of tools for on-chain data analytics
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