The Bitcoin (BTC) and Ethereum (ETH) blockchain datasets are now integrated into Google’s serverless and big data warehouse for analytics, BigQuery. This allows you to analyze and visualize data in the Ethereum and Bitcoin blockchains.
The company, through its Cloud Team, announced this in its Google Cloud Blog on August 29th, 2018. Additionally, Google announced on February 8, 2018, that it is making Bitcoin blockchain data available for exploration using BigQuery since increasing transparency of cryptocurrency products…
“makes the contained data becomes more accessible and useful”.
BigQuery Makes Blockchain Data Available
According to Google, all the historical data in the Bitcoin blockchain dataset, via BigQuery, updates every 10 minutes. Furthermore, the sole purpose is to assist users to better understand how digital currencies work by making the data in Bitcoin blockchain transparent.
Ethereum Developer APIs offer different random-access functions for obtaining the Ethereum balance for a single address. However, they do not offer simple access to all the data stored on the blockchain. Also, APIs do not allow you to see the blockchain data in total, according to Google Cloud Blog.
As Google explains, BigQuery will make the Ethereum dataset accessible for exploration. Also, all historical data in the Ethereum dataset updates on a daily basis. This tool is actually created to assist users to make business decisions, by prioritizing how fast a wallet can balance again (to balance sheet changes), and also prioritize updates to the Ethereum architecture.
You might want to view a chart that shows the aggregate Ethereum transferred, and average transaction cost aggregated within one day. Furthermore, this is the kind of visualization Google wants you to see using BigQuery.
Final Word….
Additionally, Google showcases three instances of the kinds of interesting queries you might want to handle in the Ethereum dataset. They include:
- Clean contract function calls
- Clean contract function analytics
- On-chain transaction networks and transaction time-series
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