Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP - Image 1

£26.72

Packt Publishing Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP

Name: Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP
Brand: Packt Publishing
Price: 26.72 GBP
Availability: InStock

Data Mining

This is the usual price. Wait for it to drop, or tell us your number.

£27 today · usual range £15–£53 · best ever £15

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 44 days · 44 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 44 days • 10 price levels

Days at Price

Current Price

Price Analysis

Most common price: £27 (8 days, 18.2%)

Price range: £15 - £53

Price levels: 10 different prices over 44 days

Description

Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book Description With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP. What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. Table of Contents Fundamentals of Data Engineering Big Data Capabilities on GCP Building a Data Warehouse in BigQuery Building Orchestration for Batch Data Loading Using Cloud Composer Building a Data Lake Using Dataproc Processing Streaming Data with Pub/Sub and Dataflow Visualizing Data for Making Data-Driven Decisions with Data Studio Building Machine Learning Solutions on Google Cloud Platform User and Project Management in GCP Cost Strategy in GCP CI/CD on Google Cloud Platform for Data Engineers Boosting Your Confidence as a Data Engineer

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1800561326
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 31 March 2022
Listed Since: 09 December 2021

Barcode

No barcode data available

Packt Publishing Data Engineering with Google Cloud Platform: A practical guide to operationalizing scalable data analytics systems on GCP

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional

Docker on Windows: From 101 to production with Docker on Windows, 2nd Edition

Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale

Official Google Cloud Certified Professional Data Engineer Study Guide

Azure Data Engineer Associate Certification Guide: A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam

Azure Data Engineering Cookbook: Get well versed in various data engineering techniques in Azure using this recipe-based guide, 2nd Edition

Data Engineeringon Azure

IBM Cloud Pak for Data: An enterprise platform to operationalize data, analytics, and AI

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

Python for Data Engineering Build Scalable Data Pipelines, Analytics, and Automation (Data Engineering Domain For professional)

Production-Ready Applied Deep Learning: Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks

Data Modeling for Azure Data Services: Implement professional data design and structures in Azure

Data Mesh Design: A Practical Pipeline Design Guide for Analytics, Data Science and Machine Learning

Rise of the Data Lakehouse: Building the Data Lakehouse, 2nd Edition

SAP Data Services 4.x Cookbook: Delve into the SAP Data Services environment to efficiently prepare, implement, and develop ETL processes

AWS Certified DevOps Engineer - Professional Certification and Beyond: Pass the DOP-C01 exam and prepare for the real world using case studies and real-life examples

Construire des pipelines de données pour les applications d'IA: Un guide pratique pour l'ingénierie des données, l'ingénierie des fonctionnalités et les chatbots basés sur l'IA

Grid and Cloud Database Management

Scala for Data Science: Leverage the power of Scala with different tools to build scalable, robust data science applications

Distributed Computing in Big Data Analytics: Concepts, Technologies and Applications (Scalable Computing and Communications)

Fundamentals of Data Observability: Implement Trustworthy End-To-End Data Solutions

Architecting Solutions with SAP Business Technology Platform: An architectural guide to integrating, extending, and innovating enterprise solutions using SAP BTP

Elastic Stack 8.x Cookbook: Over 80 recipes to perform ingestion, search, visualization, and monitoring for actionable insights

Strategic Engineering for Cloud Computing and Big Data Analytics