Google Cloud Platform for Data Engineering: Learn fundamental to advanced data engineering concepts and techniques using 30+ real-world use cases
- Length: 505 pages
- Edition: 1
- Language: English
- Publisher: Independently published
- Publication Date: 2019-10-23
- ISBN-10: 1701913615
- ISBN-13: 9781701913615
- Sales Rank: #3687168 (See Top 100 Books)
Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc – GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable.
Google Cloud Platform for Data Engineering – From Beginner to Data Engineer using Google Cloud Platform Google Cloud Platform for Data Engineering Chapter 1: An Introduction to Data Engineering Chapter 2 - Defining Data Types Chapter 3 – Deriving Knowledge from Information Chapter 5 – Data Modelling Chapter 6 – Alternative OLAP Data Schemas Chapter 7 - Designing a Data Warehouse Chapter 8–Advanced Data Analysis & Business Intelligence Chapter 9 - Introduction to Data Mining Algorithms Chapter 10 – On-premise vs. Cloud Technologies Chapter 11 –An introduction to Machine Learning Chapter 12 – Working with Error Chapter 13 – Planning the ML Process Part II – Google Cloud Platform Fundamentals Chapter 14 - An Introduction to the Google Cloud Platform Chapter 15 – Introduction to Cloud Security Chapter 16 - Interacting with Google Cloud Platform Chapter 17 - Compute Engine and Virtual Machines Chapter 18 – Cloud Data Storage Chapter 19 - Containers and Kubernetes Engine Chapter 20 - App Engine Chapter 21 – Serverless Compute with Cloud Functions and Cloud Run Chapter 22 – Using GCP Cloud Tools Chapter 23 - Cloud Big Data Solutions Chapter 24 - Machine Learning Part III – Data Engineering on GCP Chapter 25 – Data Lifecycle from a GCP Perspective Ingest Store Process and Analyse Access and Query data Explore and Visualize Chapter 26 - Working with Cloud DataProc Hadoop Ecosystem in GCP Cloud Dataflow and Apache Spark Chapter 27 - Stream Analytics and Real-Time Insights Streaming - Processing and Storage Cloud Pub/Sub Chapter 28 - Working with Cloud Dataflow SDK (Apache Beam) Chapter 29 - Working with BigQuery Big Query – GCP’s Data Warehouse Chapter 30 - Working with Dataprep Chapter 31 - Working with Datalab Chapter 32 – Integrating BigQuery BI Engine with Data Studio Chapter 33 - Orchestrating Data Workflows with Cloud Composer Chapter 34 - Working with Cloud AI Platform Training a TensorFlow model with Kubeflow (Optional) Test the code in a Jupyter notebook Chapter 35 – Cloud Migration
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