October 2019
Intermediate to advanced
519 pages
13h 39m
English
Content preview from Google BigQuery: The Definitive Guide
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Chapter 1. What Is Google BigQuery?
Data Processing Architectures
Google BigQuery is a serverless, highly scalable data warehouse that comes with a built-in query engine. The query engine is capable of running SQL queries on terabytes of data in a matter of seconds, and petabytes in only minutes. You get this performance without having to manage any infrastructure and without having to create or rebuild indexes.
BigQuery has legions of fans. Paul Lamere, a Spotify engineer, was thrilled that he could finally talk about how his team uses BigQuery to quickly analyze large datasets: “Google’s BigQuery is *da bomb*,” he tweeted in February 2016. “I can start with 2.2Billion ‘things’ and compute/summarize down to 20K in < 1 min.” The scale and speed are just two notable features of BigQuery. What is more transformative is not having to manage infrastructure because the simplicity inherent in serverless, ad hoc querying can open up new ways of working.
Companies are increasingly embracing data-driven decision making and fostering an open culture where the data is not siloed within departments. BigQuery, by providing the technological means to enact a cultural shift toward agility and openness, plays a big part in increasing the pace of innovation. For example, Twitter recently reported in its blog that it was able to democratize data analysis with BigQuery by providing some of its most frequently used tables to Twitter employees from a variety of teams (Engineering, Finance, and Marketing ...
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