Blockchain’s immutable ledger is what makes digital currencies tick. Ethereum, created by Vitalik Buterin, augmented their ledger with smart contracts and additional features that differentiate it from cousins like Bitcoin, providing a potential treasure trove of data – if only it was accessible for analysis.
This made Google’s August announcement that their “fast, highly scalable, cost-effective, and fully managed cloud data warehouse for analytics, with built-in machine learning”, called BigQuery, had made the entire Ethereum dataset available particularly exciting – and opened up a series of new, exciting possibilities.
The announcement was music to data analysts’ ears for a number of reasons, outlined in a blog post by Allen Day, a Cloud Developer Advocate at Google Cloud Health AI and Evgeny Medvedev, a Data Engineer with CoinFi. While the Ethereum blockchain software has an existing API for “commonly used random-access functions”, including frequent activities like monitoring transaction statuses and checking wallet balances, it doesn’t offer API endpoints “for easy access to all of the data stored on-chain”.
BigQuery’s features allow users to glean insights from the Ethereum blockchain that were previously unobtainable. Users can now view blockchain data in aggregate – a useful decision-making tool for determining and improving the Ethereum blockchain’s structural efficiency. Day and Medvedev also outlined additional features built into BigQuery for data analysis: the ability to synchronize the Ethereum blockchain to computers running Parity (who provide “blockchain infrastructure for the decentralized web”) in Google Cloud; a daily data extraction “including the results of smart contract transactions, such as token transfers” from Ethereum’s ledger; and organizing data by date “for easy and cost-effective exploration”.
The ability to analyze smart contract data is particularly exciting – BigQuery can query contract tables and dataset transactions to determine the most-used smart contracts by the number of transactions. Users can also measure the most popular tokens distributed on the Ethereum blockchain within a user-specified time frame, then use the resulting data to create interesting, informative visual representations of that information – Day and Medvedev’s blog post uses visualization software Gephi to map “the first 50,000 transactions that had at least two trading partners” for the OmiseGO token.
BigQuery opens Ethereum blockchain data up to a world of possibilities. The Day- and Medvedev-outlined use cases are the tip of the iceberg, and creative analyses with fresh insights are sure to follow. The world of smart contract analytics is now open for study – with reams of data, backed by powerful tools.
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