Google’s BigQuery Demystifies Cryptocurrency Blockchains

With JP Morgan and others offering their own digital currenciesOpens a new window as cryptocurrenciesOpens a new window find their way into mainstream finance, this question looms: How should developers handle the secretive nature of blockchain exchanges, based on two unknown entities interconnecting through a secret code?

Google’s managed data service, BigQuery, has now loaded the entire datasets of the leading cryptocurrencies, such as Bitcoin or Ethereum, to give the new financial technology credibility and transparency. As a result, Google has stripped some of the black-box anonymity from blockchain, a record of cryptocurrency transactions maintained across several computers linked in a peer-to-peer network.

“In the future, moving more economic activity on chain won’t just require a consensus level of trust,” says Allen DayOpens a new window , a senior data scientist for Google Cloud who has worked on BigQuery. “It will require having some trust in knowing about who it is you’re actually interacting with.”

Google has loaded the data from Bitcoin’s and Ethereum’s blockchains into BigQuery, and in early February, Google added six more cryptocurrency datasetsOpens a new window : Bitcoin Cash, Dash, Dogecoin, Ethereum Classic, Litecoin and Zcash.

Each dataset is updated every 24 hours, according to Google, Opens a new window and developers designed the system to add additional cryptocurrency datasets in the future. Google also is working on providing real-time streaming transaction data for all blockchains.

Operators can extract data from BigQuery via queries, using the SQL database management language. The queries can provide evidence that a given currency is storing its value or can show how mining of a specific currency is progressing.

BigQuery’s Deciphering Power

Using BigQuery to decipher how cryptocurrencies are used has proven to be popular. Its data has been used for more than 500 new analytical tools to provide insights into how the blockchains function. One such toolOpens a new window delivers visual data on who — as identified by the unique blockchain key — has the most bitcoins and notes the highest-value transactions completed last year.

The data can also provide intelligence on broader trends – for example, the information that Day has been able to discover through analyzing BitcoinOpens a new window trends that bitcoin cash is not used primarily for small transactions, as once had been envisioned, but is instead being hoarded by a small group of investors.

As a data service, Google’s BigQuery faces limited competition from Amazon and Microsoft. In 2018, Amazon Web Services partnered with a company called Kaleido and offered a simplified blockchain cloud platforms for its clients.

“Kaleido eliminates 80% of the custom code needed to build a given blockchain project by providing an array of tools and protocols that are ‘plug-and-play’, spanning needs from back-end development to front-end app user interfaces,” says Stephen O’NealOpens a new window , writing for Cointelegraph.com.

Last May, Microsoft began offering a similar product called the Azure Blockchain Workbench designed to make the creation of blockchain apps easier. It is configured to use Ethereum as its consensus protocol.

BigQuery’s Difference

Still, experts say that BigQuery has differentiated itself from the Microsoft and Amazon data analytical tools in its ability to allow users to interact with the blockchain data itself, rather than merely building blockchain-related tools, offering understanding of how the cryptocurrencies are used.

Independent developers are also taking advantage of the opportunity to load their own datasets onto BigQuery.

But what really differentiates BigQuery from the other offerings is its tremendous speed. The lightning-quick results of queries are the result of its design, which is based on Google’s Dremel. Dremel was designed to run SQL-like queries without requiring an intermediary server.

“This is where the service’s server-less architecture can demonstrate its raw, unadulterated processing power,” Alex Woodie saysOpens a new window in Datanami, a data science publication. “Billion-row queries that would bring modest on-premise clusters to their knees are processed in a handful of seconds, making BigQuery an ideal solution for ad hoc queries.”