This project aimed at analysing bitcoin graph data and providing sophisticated information on bitcoin transaction data and aggregate on-chain statistics derived from graph data. The outcome statistics in the form of timeseries were later used in predictive machine learning models.
The goal of this project was to build an ETL system processing live online bitcoin transactional data and loading it to Neo4J graph database on the fly. The data included not only block chains but all details about transactions and interactions between addresses.
The goal of this project was to model blockchain historical data as a graph and to build an ETL system loading the entire data (blocks, transactions, inputs, outputs, addresses) into a Neo4j graph database. The solution I designed performed 10x faster than the best reported system.