This book is your guide to the modern market of data analytics platforms and the benefits of using Snowflake, the data warehouse built for the cloud. As organizations increasingly rely on modern cloud data platforms, the core of any analytics framework—the data warehouse—is more important than ever. This updated 2nd edition ensures you are ready to make the most of the industry’s leading data warehouse. This book will onboard you to Snowflake and present best practices for deploying and using the Snowflake data warehouse. The book also covers modern analytics architecture, integration with leading analytics software such as Matillion ETL, Tableau, and Databricks, and migration scenarios for on-premises legacy data warehouses. This new edition includes expanded coverage of SnowPark for developing complex data applications, an introduction to managing large datasets with Apache Iceberg tables, and instructions for creating interactive data applications using Streamlit, ensuring readers are equipped with the latest advancements in Snowflake's capabilities. What You Will Learn Master key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake Integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Manage large datasets with Apache Iceberg Tables Implement continuous data loading with Snowpipe and Dynamic Tables Who This Book Is For Data professionals, business analysts, IT administrators, and existing or potential Snowflake users

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.

"Data Modeling with Tableau" provides a comprehensive guide to effectively utilizing Tableau Prep and Tableau Desktop for building elegant data models that drive organizational insights. You'll explore robust data modeling strategies and governance practices tailored to Tableau's diverse toolset, empowering you to make faster and more informed decisions based on data. What this Book will help me do Understand the fundamentals of data modeling in Tableau using Prep Builder and Desktop. Learn to optimize data sources for performance and better query capabilities. Implement secure and scalable governance strategies with Tableau Server and Cloud. Use advanced Tableau features like Ask Data and Explain Data to enable powerful analytics. Apply best practices for sharing and extending data models within your organization. Author(s) Kirk Munroe is an experienced data professional with a deep understanding of Tableau-driven analytics. With years of in-field expertise, Kirk now dedicates his career to helping businesses unlock their data's potential through effective Tableau solutions. His hands-on approach ensures this book is practical and approachable. Who is it for? This book is ideal for data analysts and business analysts aiming to enhance their skills in data modeling. It is also valuable for professionals such as data stewards, looking to implement secure and performant data strategies. If you seek to make enterprise data more accessible and actionable, this book is for you.

Explore the modern market of data analytics platforms and the benefits of using Snowflake computing, the data warehouse built for the cloud. With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use. Snowflake was built specifically for the cloud and it is a true game changer for the analytics market. This book will help onboard you to Snowflake, present best practices to deploy, and use the Snowflake data warehouse. In addition, it covers modern analytics architecture and use cases. It provides use cases of integration with leading analytics software such as Matillion ETL, Tableau, and Databricks. Finally, it covers migration scenarios for on-premise legacy data warehouses. What You Will Learn Know the key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Who This Book Is For Those working with data warehouse and business intelligence (BI) technologies, and existing and potential Snowflake users

If you're ready to untap the potential of data analytics in the cloud, 'Learning Google BigQuery' will take you from understanding foundational concepts to mastering advanced techniques of this powerful platform. Through hands-on examples, you'll learn how to query and analyze massive datasets efficiently, develop custom applications, and integrate your results seamlessly with other tools. What this Book will help me do Understand the fundamentals of Google Cloud Platform and how BigQuery operates within it. Migrate enterprise-scale data seamlessly into BigQuery for further analytics. Master SQL techniques for querying large-scale datasets in BigQuery. Enable real-time data analytics and visualization with tools like Tableau and Python. Learn to create dynamic datasets, manage partition tables and use BigQuery APIs effectively. Author(s) None Berlyant, None Haridass, and None Brown are specialists with years of experience in data science, big data platforms, and cloud technologies. They bring their expertise in data analytics and teaching to make advanced concepts accessible. Their hands-on approach and real-world examples ensure readers can directly apply the skills they acquire to practical scenarios. Who is it for? This book is tailored for developers, analysts, and data scientists eager to leverage cloud-based tools for handling and analyzing large-scale datasets. If you seek to gain hands-on proficiency in working with BigQuery or want to enhance your organization's data capabilities, this book is a fit. No prior BigQuery knowledge is needed, just a willingness to learn.

Fragmented, disparate backend data systems have become the norm in today’s enterprise, where you’ll find a mix of relational databases, Hadoop stores, and NoSQL engines, with access and analytics tools bolted on every which way. This mishmash of options presents a real challenge when it comes to choosing frontend analytics and visualization tools. How did we get here? In this O’Reilly report, IT veteran Rich Morrow takes you through the rapid changes to both backend storage and frontend analytics over the past decade, and provides a pragmatic list of requirements for an analytics stack that will centralize access to all of these data systems. You’ll examine current analytics platforms, including Looker—a new breed of analytics and visualization tools built specifically to handle our fragmented data space. Understand why and how data became so fractured so quickly Explore the tangled web of data and backend tools in today’s enterprises Learn the tool requirements for accessing and analyzing the full spectrum of data Examine the relative strengths of popular analytics and visualization tools, including Looker, Tableau, and MicroStrategy Inspect Looker’s unique focus on both the frontend and backend

How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results. Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python