Ethereum and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Earlier this year, we made the Bitcoin dataset publicly available for analysis in Google BigQuery. Today we’re making the Ethereum dataset available.
This quote from Niccolò Machiavelli – an Italian politician, historian, and philosopher from the Renaissance period, has profound relevance to emerging technologies.
Having detailed response time percentiles and impact analysis for transactions is great. But often it's not the php application that is causing unwanted spikes in response times rather than services used by your application, such as MySQL, redis, HTTP APIs and so on.
In this article, I’ll walk you through the process of building a machine learning model using BigQuery ML. As a bonus, we’ll have the chance to use BigQuery’s support for spatial functions. We’ll use the New York City taxicab dataset, with the goal of predicting taxi fare, given both pick-up and drop-off locations for each ride — imagine that we are designing a trip planner.
July was a huge month for merger and acquisition (M&A) activity in the BI space. Thoughtspot raised $145 million, Salesforce acquired Datorama, Workday acquired Stories.bi and Tableau acquired Empirical Systems.
