The petabyte-scale data warehousing solution of Google Cloud, Google BigQuery will now offer Ethereum blockchain’s big data view. This will allow smart contract analysis and expand existing functionalities.
All the historical data of Ethereum can be explored with BigQuery. In fact, the GitHub’s Ethereum ETL project has made available all the source code. This means that you can directly extract these source codes and enter them to BigQuery.
Accessible Blockchain Data
The motive of Google behind making this Ethereum blockchain data available and accessible is to ensure that the blockchain’s data can be accessed without hassle.
The functions’ API of Ethereum software is randomly accessible. For instance, you can check wallet balance. However, API endpoints are not as simply accessible for the blockchain’s stored data. Although you can’t view aggregated blockchain data through API endpoints, it possible through OLAP feature of BigQuery.
Further, using Google Cloud, you can synchronize parity equipped computers with Ethereum blockchain. The information such as token transfer can be additionally extracted from Ethereum ledger on a daily basis.
You can also query Kernels’ data tables from BigQuery Python library. A kernel is an in-browser platform for coding on public data science.
Smart Contract Analysis
Smart contracts in Ethereum are transactions made in the form of tokens. Simply put, it applies relationships using functions and cryptographic code exactly as wanted by the creator. For example, using a smart contract, a user can send some specified Ether amount on a fixed data repetitively.
In order to show how BigQuery can help, Google found out most famous smart contracts on Ethereum. They found out that one of it was connected to Cryptokitties, a famous game. The transaction related to this happened 2.3 million times.
Expanding Available Functionalities
Although we would have been able to check the Ethereum wallet balance and transaction earlier too, Google’s offering is different. Google provides access to Ethereum blockchain’s full data. This clearly means that many data scientists can now easily get sources for blockchain evaluation, which they would have lacked before.
For instance, a business may be able to make a tool for supply chain management, which is fully automated. This tool would help information sharing in real-time.
Additionally, surges can be tracked to discover what is the reason behind abnormal usage.
Conclusion
There are various reasons behind giving Ethereum data access to users. For one, aggregate data API endpoints were made available. You can find out transaction frequency in various wallet addresses using just one query. Users can also gain access to a specific smart contract’s function without even having its source codes.
Using all this information, the data scientists and people in machine learning field can explore via Google’s tool and then create separate platforms to make blockchain data openly available. This would open many opportunities for people in a data science field.
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