Today we are announcing the BigQuery sandbox, a credit-card free path to enable new users and students to experiment with BigQuery at no cost—without having to enter credit card information. As organizations begin to collect more and more data, many find that a serverless data warehouse like BigQuery is the only platform that can scale to meet their needs.
Since they emerged in 2009, cryptocurrencies have experienced their share of volatility—and are a continual source of fascination. In the past year, as part of the BigQuery Public Datasets program, Google Cloud released datasets consisting of the blockchain transaction history for Bitcoin and Ethereum, to help you better understand cryptocurrency. Today, we're releasing an additional six cryptocurrency blockchains.
Financial institutions have a natural desire to predict the volume, volatility, value or other parameters of financial instruments or their derivatives, to manage positions and mitigate risk more effectively. They also have a rich set of business problems (and correspondingly large datasets) to which it’s practical to apply machine learning techniques.
As enterprises amass terabytes of complex data, they need tools to house and make better sense of their information. This is why we’ve built BigQuery, to help data analysts deal with large datasets. But not all of us are data wizards. Many of us use spreadsheets to perform ad-hoc analysis.
Since announcing our new interface back in July, our goal has been to make it easier for BigQuery users and their teams to uncover insights and share them with teammates and colleagues. Whether you’re a veteran or brand new to BigQuery, we wanted to highlight some of the major improvements we’ve made to the interface in the past five months. Some of this functionality was previously available in the classic UI, while other elements are totally new. Let’s take a closer look.
Google BigQuery is a serverless enterprise data warehouse tool that’s designed for scalability. We built BigQuery to be highly scalable and let you focus on data analysis without having to take care of the underlying infrastructure. We know BigQuery users like its capability to query petabyte-scale datasets without the need to provision anything. You just upload the data and start playing with it.
