From Data Source to Intelligent Analysis: The Evolution of Web3 Data Indexing Technology

1 Introduction

The rapid development of blockchain applications has raised a key question: how do these decentralized applications acquire and process data? From the early dApps of 2017 to the flourishing financial, gaming, and social applications in today's multi-chain ecosystem, data acquisition and processing have always been key challenges.

In the field of artificial intelligence, data is the key to driving system learning and evolution. Without a massive amount of high-quality data, even the most advanced AI algorithms cannot realize their potential. This article will delve into the evolution of data indexing technology from the perspective of blockchain data accessibility, and will compare and analyze the similarities and differences of several representative data service protocols in terms of technical routes and product features.

2 The Complexity and Simplicity of Data Indexing: From Blockchain Nodes to Full Chain Database

2.1 Data Source: Blockchain Node

Blockchain, as a decentralized distributed ledger, stores a complete copy of transaction data at each node. However, building and maintaining a node is costly and technically challenging for ordinary users. To address this issue, RPC node providers have emerged, offering a lower barrier data access method for users through API interfaces.

2.2 Data Parsing: From Raw Data to Usable Data

Raw data obtained from blockchain nodes is often encrypted and encoded, and needs to be parsed to be effectively utilized. Data parsing is the process of converting complex raw data into a more understandable and operable format, and it is a key link in the entire data indexing process.

Evolution of Data Indexers 2.3

As the volume of blockchain data surges, the demand for data indexers is becoming increasingly urgent. Indexers greatly simplify the process for developers to access and use blockchain data by organizing on-chain data and providing a unified query interface. Currently, mainstream indexers support multi-chain indexing and have customized specialized data parsing frameworks for different application scenarios.

2.4 Full-chain database: Aligning to flow priority

As application demands become increasingly complex, traditional indexers and standardized API interfaces are gradually unable to meet the diverse query requirements. The industry is moving towards building blockchain data streams to achieve real-time data processing and analysis. This shift provides a new perspective for more efficient data management and application development.

3 AI + Database? A Deep Comparison of The Graph, Chainbase, Space and Time

3.1 The Graph

The Graph provides multi-chain data indexing and querying services through a decentralized network of nodes. Its core products include a data query execution market and a data indexing cache market, defining data extraction and transformation rules through subgraph (Subgraphs). The network is maintained collectively by indexers, curators, delegators, and developers, forming a complete incentive ecosystem.

Recently, Semiotic Labs in The Graph ecosystem developed a series of AI-assisted tools, such as AutoAgora, Allocation Optimizer, and AgentC, further enhancing the intelligence level of the system and user experience.

3.2 Chainbase

Chainbase, as a full-chain data network, integrates multi-chain data and provides real-time data lakes, dual-chain architecture, and innovative data format standards. Its uniqueness lies in combining AI technology to create a crypto world model, such as Theia developed based on the NVIDIA DORA model, which can deeply explore the potential value of on-chain data.

3.3 Space and Time

Space and Time (SxT) is committed to building a verifiable computation layer, achieving reliable data processing on decentralized data warehouses through Proof of SQL technology. This innovative zero-knowledge proof technology provides an efficient solution for data verification and is expected to offer new possibilities for traditional industries to build products using blockchain data.

SxT has also collaborated with Microsoft's AI Lab to develop generative AI tools that simplify the process for users to query blockchain data using natural language.

Conclusion and Outlook

Blockchain data indexing technology has gradually evolved from the initial node data sources, through the development of data parsing and indexing, to AI-enabled full-chain data services. This process has not only improved the efficiency and accuracy of data access but also provided users with a more intelligent experience.

With the continuous advancement of new technologies such as AI and zero-knowledge proofs, blockchain data services will become further intelligent and secure. As the industry infrastructure, these innovative technologies will continue to drive the development and innovation of the Web3 ecosystem.