In today's data-driven world, earning the Tableau Certified Data Analyst credential signals your ability to connect, analyze, and communicate insights using one of the industry's leading visualization platforms. This study guide offers practical and comprehensive preparation for the certification exam, with walk-throughs, best practices, vocabulary, and example questions to help you build both confidence and competence in Tableau. Written by Christopher Gardner, business intelligence analyst and lead Tableau developer at the University of Michigan, this guide supports first-time test-takers and seasoned users alike. You'll begin with foundational skills in Tableau Prep Builder and Tableau Desktop—connecting, combining, and preparing data—before progressing to building effective visualizations, performing calculations, and applying advanced tools like level-of-detail expressions, parameters, forecasts, and predictive analytics. Read, manipulate, and prepare data for analysis Navigate Tableau's tools to build impactful visualizations Write calculations and functions to enhance your dashboards Share your work responsibly with secure publishing options

Your company relies on data to succeed—data that traditionally comes from a business's transactional processes, pulled from the transaction systems through an extract-transform-load (ETL) process into a warehouse for reporting purposes. But this data flow is no longer sufficient given the growth of the internet of things (IOT), web commerce, and cybersecurity. How can your company keep up with today's increasing magnitude of data and insights? Organizations that can no longer rely on data generated by business processes are looking outside their workflow for information on customer behavior, retail patterns, and industry trends. In this report, author Christopher Gardner examines the challenges of building a framework that provides universal access to data. You will: Learn the advantages and challenges of universal data access, including data diversity, data volume, and the speed of analytic operations Discover how to build a framework for data diversity and universal access Learn common methods for improving database and performance SLAs Examine the organizational requirements that a fast universal data access platform must meet Explore a case study that demonstrates how components work together to form a multiaccess, high-volume, high-performance interface About the author: Christopher Gardner is the campus Tableau application administrator at the University of Michigan, controlling security, updates, and performance maintenance.

Storing data and making it accessible for real-time analysis is a huge challenge for organizations today. In 2020 alone, 64.2 billion GB of data was created or replicated, and it continues to grow. With this report, data engineers, architects, and software engineers will learn how to do deep analysis and automate business decisions while keeping your analytical capabilities timely. Author Christopher Gardner takes you through current practices for extracting data for analysis and uncovers the opportunities and benefits of making that data extraction and analysis continuous. By the end of this report, you’ll know how to use new and innovative tools against your data to make real-time decisions. And you’ll understand how to examine the impact of real-time analytics on your business. Learn the four requirements of real-time analytics: latency, freshness, throughput, and concurrency Determine where delays between data collection and actionable analytics occur Understand the reasons for real-time analytics and identify the tools you need to reach a faster, more dynamic level Examine changes in data storage and software while learning methodologies for overcoming delays in existing database architecture Explore case studies that show how companies use columnar data, sharding, and bitmap indexing to store and analyze data Fast and fresh data can make the difference between a successful transaction and a missed opportunity. The report shows you how.