Google expands its Blockchain search tools, adds six new cryptocurrencies in BigQuery Public Datasets

Google’s BigQuery Public Datasets program, has added six new cryptocurrencies to expand it’s blockchain search tools. Including Bitcoin and Ethereum which were added last year, the total count is now eight. The six new cryptocurrency blockchain datasets are Bitcoin Cash, Dash, Dogecoin, Ethereum Classic, Litecoin, and Zcash.

BigQuery Public dataset is stored in BigQuery and made available to the general public through the Google Cloud Public Dataset Program. The blockchain related datasets consist of the blockchain’s transaction history to help developers better understand cryptocurrency.

Apart from adding new datasets, Google has released a set of queries and views that map all blockchain datasets to a double-entry book data structure that enables multi-chain meta-analyses, as well as integration with conventional financial record processing systems.

A Blockchain ETL ingestion framework helps to update all datasets every 24 hours via a common codebase. This results in a higher latency for loading Bitcoin blocks into BigQuery. It also leads to ingesting additional BigQuery datasets with less effort. It also means that a low-latency loading solution can be implemented once and can be used to enable real-time streaming transactions for all blockchains.

With this release, the blockchain data sets have been standardized into a "unified schema," meaning the data is structured in a uniform, easy-to-access way. They’ve also included more data, such as script op-codes. Having these scripts available for Bitcoin-like datasets enables more advanced analyses. They have also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. This helps to further interoperate with Ethereum and ERC-20 token transactions.

Allen Day, Cloud Developer Advocate, Google Cloud Health AI, writes in a blog post, “ We hope these new public datasets encourage you to try out BigQuery and BigQuery ML for yourself. Or, if you run your own enterprise-focused blockchain, these datasets and sample queries can guide you as you form your own blockchain analytics.”