Google BigQuery Analytics by Jordan Tigani, Siddartha Naidu

By Jordan Tigani, Siddartha Naidu

Easy methods to successfully use BigQuery, steer clear of universal error, and execute subtle queries opposed to huge datasets Google BigQuery Analytics is the fitting advisor for company and information analysts who wish the most recent pointers on operating advanced queries and writing code to speak with the BigQuery API. The booklet makes use of real-world examples to illustrate present top practices and methods, and in addition explains and demonstrates streaming ingestion, transformation through Hadoop in Google Compute engine, AppEngine datastore integration, and utilizing GViz with Tableau to generate charts of question effects. as well as the mechanics of BigQuery, the e-book additionally covers the structure of the underlying Dremel question engine, delivering an intensive knowing that ends up in higher question effects. encompasses a significant other site that comes with all code and knowledge units from the ebook makes use of real-world examples to provide an explanation for every thing analysts want to know to successfully use BigQuery contains internet program examples coded in Python.

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Additional info for Google BigQuery Analytics

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It is likely that in the coming months and years there will be additional entries, so just because a tool or service isn’t mentioned here doesn’t mean that it doesn’t exist. Chapter 2, “BigQuery Fundamentals,” goes into more detail about the individual components, but this is a quick survey of the offerings. You can divide the cloud offerings into three portions: processing, storage, and analytics. Chapter 1 ■ The Story of Big Data at Google Cloud Processing The cloud processing components enable you to run arbitrary computations over your data: ■ Google Compute Engine (GCE): The base of Google’s Cloud Platform, GCE is infrastructure-as-a-service, plain and simple.

Multitenancy and Parallel Execution When you run a query on MySQL that takes one second, you get to occupy a single processor core for one second. If you have eight processors, you can run eight queries at once. Amazon Redshift lets you run a single query in parallel, but on a fixed number of cores that are all yours for the entire time you are renting the Redshift instance. BigQuery operates on a fundamentally different model; your query will run on thousands of cores in parallel. If you have eight queries, those may all run on a thousand cores in parallel.

This type of failure still wouldn’t take BigQuery Chapter 2 ■ BigQuery Fundamentals down, however, since BigQuery runs in multiple geographically distributed datacenters and will fail over automatically. Of course, this is where we have to remind you that all software is fallible. Just because your data is replicated nine ways doesn’t mean that it is completely immune to loss. A buggy software release could cause data to be inadvertently deleted from all nine of those disks. If you have critical data, make sure to back it up.

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