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Fundamentals of Analytics Engineering
Author: Dumky De Wilde
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-03-29
Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
Using Digital Analytics for Smart Assessment
Author: Tabatha Farney
language: en
Publisher: American Library Association
Release Date: 2018-12-13
Tracking the library user's journey is no simple task in the digital world; users can often navigate through a series of different websites, including library websites, discovery tools, link resolvers, and more just to view a single journal article. Your library collects massive amounts of data related to this journey—probably more than you realize, and almost certainly more than you analyze. Too often library analytic programs simplify data into basic units of measurements that miss useful insights. Here, data expert Farney shows you how to maximize your efforts: you’ll learn how to improve your data collection, clean your data, and combine different data sources. Teaching you how to identify and analyze areas that fit your library’s priorities, this book covers case studies of library projects with digital analytics;ways to use email campaign data from MailChimp or ConstantContact;how to measure click-through rates from unavailable items in the catalog to the ILL module;getting data from search tools such as library catalogs, journal search portals, link resolvers, and digital repositories;using COUNTER compliant data from your electronic resources;techniques for using Google Tag Manager for custom metrics and dimensions;descriptions of analytics tools ranging from library analytics tools like Springshare’s LibInsights and Orangeboy’s Savannah to more focused web analytics tools like Google Analytics, Piwik, and Woopra; anddata visualization tools like Tableau or Google Data Studio. Focusing on digital analytics principles and concepts, this book walks you through the many tools available, including step-by-step examples for typical library needs.