Название: Building Cloud Data Architecture: Engineer Scalable, Efficient, and Cost-Effective Data Systems (Early Release) Автор: Sahil Jhangiani Издательство: O’Reilly Media, Inc. Год: 2021-06-23 Язык: английский Формат: epub Размер: 10.1 MB
As mentioned in the preface, there are three major players in the cloud provider space at the time of writing: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. According to a report from Gartner, AWS has the largest market share. This is mostly due to its first mover advantage and its ability to lock in customers once they start building infrastructure/services within the AWS platform. AWS is followed by Azure, and then GCP in overall market share.
Although there are many cloud computing companies out there, this book mostly focuses on the big three, with a slight emphasis on AWS since it holds far more market share than Azure and GCP combined. Among the large swath of cloud service offerings available, the big three mostly focus on what would fall under infrastructure as a service (IaaS) and platform as a service (PaaS) offerings. Although the big three do provide their own versions of software as a service or SaaS (technically you could easily argue that IaaS and PaaS are just forms of software as a service), there is a solid distinction for what the big three provide compared to companies like Salesforce.
It's no wonder data engineering expertise is in high demand, given the large costs and enormous decision-making impact data systems can have. With this practical book, you'll learn industry-tested methods for taking advantage of cloud services while avoiding complexity and out-of-control costs.
Cloud service providers deliver most of the high-quality education in this space, but their offerings have two major downsides. Their documentation tends to focus on solving problems with their products rather than addressing the complexity of working in an enterprise environment. Author Sahil Jhangiani provides a holistic approach to managing data in the cloud using specific examples from the three major cloud providers (AWS, GCP, and Azure).
You'll learn how to:
Navigate the large swath of cloud services available
Cut through marketing hype to identify the root technologies and concepts behind big data tooling
Build modular pipelines and systems that manage change smoothly
Understand the tricks and pitfalls of processing large datasets, from both a cost and a performance viewpoint
Create systems that integrate smoothly and can adapt to ever-changing analytical workloads
Avoid vendor lock-in and leverage individual cloud services for what they do best
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