Data Streams

At the core of contemporary data solutions, the journey begins with raw data streams. Providers, operating robust RPC servers, embark on the meticulous collection of this invaluable raw data. They are tasked not only with its efficient storage but also with ensuring it remains accessible. The goal is to facilitate the creation of datasets in a manner that is both cost-effective and swift.

Data streams, such as transactions, are preserved in their original form, retaining all interactions with programs or smart contracts in their executed state, typically encapsulated within serialized binary structures. In certain instances, like with the Ethereum Virtual Machine (EVM), data streams are augmented with comprehensive traces of smart contract calls. This enrichment process incorporates data that could otherwise not be recreated from the default raw outputs.

A recurrent challenge observed amongst numerous data-centric startups is their approach towards data preparation. These entities endeavor to process and offer foundational datasets based on currently available information. However, this information is subject to rapid changes due to updates in ABI or IDL interface descriptions, new releases, or standards. Adapting to these alterations often proves to be not only costly but resource-intensive. It entails extensive data rescans and the reconstruction of datasets. Moreover, for startups leveraging cloud services, this necessity translates into significant expenses and delayed progression.

DH3 stands at the forefront of addressing and overcoming these challenges. Our innovative approach ensures unparalleled efficiency in rebuilding and modifying datasets. We have developed a system that permits the continual querying of raw data, incorporating the most current parsing methodologies. This enables developers to focus solely on the strategic aspects of data management, such as the selection and indexing of fields, thereby streamlining the entire process.

Through our efforts, we are setting a new standard in data management, one that promises agility, cost-effectiveness, and technological resilience in the ever-evolving landscape of data utilization and analysis.