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Google Cloud Platform for Architects

Google Cloud Platform for Architects

By : Vitthal Srinivasan, Loonycorn Ravi, Judy Raj
3.1 (12)
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Google Cloud Platform for Architects

Google Cloud Platform for Architects

3.1 (12)
By: Vitthal Srinivasan, Loonycorn Ravi, Judy Raj

Overview of this book

Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago. Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence. You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect. You will also learn how individual cloud services are configured and used, so that you are never intimidated at having to build it yourself. You will also learn the right way and the right situation in which to use the important GCP services. By the end of this book, you will be able to make the most out of Google Cloud Platform design.
Table of Contents (19 chapters)
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13
Logging and Monitoring

Google Cloud Dataproc

As mentioned earlier, Google Cloud Dataproc is a managed Spark and Hadoop solution from Google. Its nature of being managed and of being on the cloud gives users the ability to turn the clusters off when they are not required, which saves a lot of cost. So, Dataproc is not only simple and time saving, but it is also cost effective.

Just like other managed services from Google, we can use GCP APIs to interact with Dataproc. We will get into the details later in this chapter. While the initial vision of Dataproc was to provide managed Hadoop and Spark, the current state boasts managed support for open source Apache Hive, Pig, Hadoop, and Spark, and integration with Cloud Storage and BigQuery through connectors, on top of being monitored by Stackdriver. Just like Hadoop, Dataproc also has Master, Client and Worker nodes configurations where Master nodes manage...

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