Mahajan / Ghatak Microsoft 365 and SharePoint Online Cookbook

Over 100 practical recipes to help you get the most out of Office 365 and SharePoint Online
1. Auflage 2024
ISBN: 978-1-83864-450-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 810 Seiten

Microsoft Office 365 provides tools for managing organizational tasks like content management, communication, report creation, and business automation processes. With this book, you'll get to grips with popular apps from Microsoft, enabling workspace collaboration and productivity using Microsoft SharePoint Online, Teams, and the Power Platform.
In addition to guiding you through the implementation of Microsoft 365 apps, this practical guide helps you to learn from a Microsoft consultant's extensive experience of working with the Microsoft business suite. This cookbook covers recipes for implementing SharePoint Online for various content management tasks. You'll learn how to create sites for your organization and enhance collaboration across the business and then see how you can boost productivity with apps such as Microsoft Teams, Power Platform, Planner, Delve, and M365 Groups. You'll find out how to use the Power Platform to make the most of Power Apps, Power Automate, Power BI, and Power Virtual Agents. Finally, the book focuses on the SharePoint framework, which helps you to build custom Teams and SharePoint solutions.
By the end of the book, you will be ready to use Microsoft 365 and SharePoint Online to enhance business productivity using a broad set of tools.

Autoren/Hrsg.

Mahajan, Gaurav

Ghatak, Sudeep

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Groupware
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Microsoft Programmierung
• Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
• Mathematik | Informatik EDV | Informatik Betriebssysteme Windows Betriebssysteme

Inhaltsverzeichnis

Table of Contents - Overview of Microsoft 365
- Introduction to SharePoint Online

- Working with Modern Sites in SharePoint Online

- Working with Lists and Libraries in SharePoint Online
- Document Management in SharePoint
- Term Store and Content Types in SharePoint Online
- OneDrive for Business

- Search in Microsoft 365
- Office Delve
- Microsoft 365 Groups
- Microsoft Teams
- Yammer - The Enterprise Social Network
- Power Automate (Microsoft Flow)
- PowerApps

- Power BI

- Power Virtual Agents
- Planner
- Custom Development - SharePoint Framework
- Microsoft 365 on Mobile
- Appendix

Leseproben

Preface

We live in a world where the amount of data being generated is constantly increasing. While a few decades ago, an organization may have had a single database that could store everything they needed to track, today most organizations have tens, hundreds, or even thousands of databases, along with data warehouses, and perhaps a data lake. And these data stores are being fed from an increasing number of data sources (transaction data, web server log files, IoT and other sensors, and social media, to name just a few).

It is no surprise that we hear more and more companies talk about being data-driven in their decision making. But in order for an organization to be truly data-driven, they need to be masters of managing and drawing insights from these ever-increasing quantities and types of data. And to enable this, organizations need to employ people with specialized data skills.

Doing a search on LinkedIn for jobs related to data returns over 1.5 million results (and that is just for the United States!). The job titles include roles such as data engineers (with 185,000 results), data scientists (120,000 results), and data architects (75,000 results).

While this book will not magically make you a data engineer, it has been designed to accelerate your journey toward data engineering on AWS. By the end of this book, you will not only have learned some of the core concepts around data engineering, but you will also have a good understanding of the wide variety of tools available in AWS for working with data. You will also have been through numerous hands-on exercises, gaining practical experience with things such as ingesting streaming data, transforming and optimizing data, building visualizations, and even drawing insights from data using AI.

Who this book is for

This book has been designed for two groups of people; firstly, those people looking to get started with a career in data engineering, and who want to learn core data engineering concepts. This book introduces many different aspects of data engineering, providing a comprehensive high-level understanding of, and practical hands-on experience with, different focus areas of data engineering.

Secondly, this book is for those people who may already have an established career focused on data, but who are new to the cloud, and to AWS in particular. For these people, this book provides a clear understanding of many of the different AWS services for working with data and gets them hands-on experience with a variety of these AWS services.

What this book covers

Each of the chapters in this book takes the approach of introducing important concepts and key AWS services and then providing a hands-on exercise related to the topic of the chapter:

, , reviews the challenges of ever-increasing datasets, and the role of the data engineer in working with data in the cloud.

, , introduces foundational concepts and technologies related to big data processing.

, , provides an introduction to a wide range of AWS services that are used for ingesting, processing, and consuming data.

, , covers the all-important topics of keeping data secure, ensuring good data governance, and the importance of cataloging your data.

, , provides an approach for whiteboarding the high-level design of a data engineering pipeline.

, , looks at the variety of data sources that we may need to ingest from and examines AWS services for ingesting both batch and streaming data.

, , covers common transformations for optimizing datasets and for applying business logic.

, , is about better understanding the different types of data consumers that a data engineer may work to prepare data for.

, , focuses on the use of data warehouses as a data mart and looks at moving data between a data lake and data warehouse. This chapter also does a deep dive into Amazon Redshift, a cloud-based data warehouse.

, , looks at how various data engineering tasks and transformations can be put together in a data pipeline, and how these can be run and managed with pipeline orchestration tools such as AWS Step Functions.

, , does a deeper dive into the Amazon Athena service, which can be used for running SQL queries directly on data in the data lake, and beyond.

, , discusses the importance of being able to craft visualizations of data, and how the Amazon QuickSight service enables this.

, , reviews how AI and ML are increasingly important for gaining new value from data, and introduces some of the AWS services for both ML and AI.

, , concludes the book by looking at the bigger picture of data analytics, including real-world examples of data pipelines and a review of emerging trends in the industry.

To get the most out of this book

Basic knowledge of computer systems and concepts, and how these are used within large organizations, is helpful prerequisite knowledge for this book. However, no data engineering-specific skills or knowledge is required. Also, a familiarity with cloud computing fundamentals and core AWS systems will make it easier to follow along, especially with the hands-on exercises, but detailed step-by-step instructions are included for each task.

All hands-on exercises make use of cloud-based services, so beyond using a supported web browser with a stable internet connection, there are no additional hardware or software requirements.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Things change fast in the computing industry, and this is clearly seen within the cloud industry. AWS is constantly rolling out new services, as well as improvements for existing services, and some of these improvements lead to changes in the user interface provided via the AWS console.

As a result, some of the screenshots included in this book may not look identical to what you are seeing in the AWS console when completing hands-on exercises. Or, you may find that a specific screen has additional options beyond what is shown in the screenshot in this book. It is unlikely that these changes will prevent you from following along with the step-by-step instructions in this book, but anything that may significantly impact a hands-on exercise will be addressed with a note for that chapter in this book's GitHub repository. Therefore, please refer to the GitHub repository as you complete the hands-on exercises for each chapter. In addition to notes about any significant console changes, the GitHub repository also includes copies of code contained in this book and other useful resources.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Data-Engineering-with-AWS. If there's an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781800560413_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

import boto3

import awswrangler as wr

from urllib.parse import unquote_plus

Any command-line input or output is written as follows:

$ aws s3 cp test.csv s3://dataeng-landing-zone-initials/ testdb/csvparquet/test.csv

Bold: Indicates a new term, an important word, or words that you see on screen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Select System info from the Administration panel."

Tips or Important Notes

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Über Autor(innen)

Mahajan Gaurav :

Gaurav Mahajan is a Microsoft Technology Solutions Professional who specializes in Microsoft 365, SharePoint, and AI, with over 19 years of technical consulting experience. Gaurav has a bachelor's in engineering, is Stanford certified in machine learning, and holds a PG Diploma in management from the Indian Institute of Management. He speaks at and organizes technical events, code camps, and conferences. He is a co-chair of the M365 & SharePoint Saturday, Pittsburgh (US) annual conference.Ghatak Sudeep :

Sudeep Ghatak is a Microsoft MVP in Business Applications and a frequent contributor to the Power Automate Community forum. He works as a Principal Architect in Christchurch, New Zealand and loves designing solutions based on Office 365 and the Azure platform. He is a certified Solutions Developer (MCSD) and holds a postgraduate degree in instrumentation engineering. He is an active member of the Microsoft community and often seen speaking at user groups and conferences in New Zealand.