Financial institutions have a natural desire to predict the volume, volatility, value or other parameters of financial instruments or their derivatives, to manage positions and mitigate risk more effectively. They also have a rich set of business problems (and correspondingly large datasets) to which it’s practical to apply machine learning techniques.

In this blog post, we’ll share more on how we built the BigQuery Ethereum Public Dataset that contains the Ethereum blockchain data. This includes the primary data structures—blocks, transactions—as well as high-value data derivatives—token transfers, smart contract method descriptions.

We all know we are at the peak of the hype cycle for…wait for it – Blockchain! We are also already aware of some of the benefits of blockchain - but can blockchain be applicable to traditional data management? Though real-world blockchain implementations in the enterprise are minimal so far, I do believe there is a ton of potential to solve some of the problems that businesses face.

Blockchain technology has been all over the news recently. With the explosion of bitcoin usage for digital transactions, blockchain is required to track and verify every transaction made.