Breathe new life into older applications by refactoring T-SQL queries and code using modern techniques. This book shows you how to significantly improve the performance of older applications by finding common anti-patterns in T-SQL code, then rewriting those anti-patterns using new functionality that is supported in current versions of SQL Server, including SQL Server 2019. The focus moves through the different types of database objects and the code used to create them, discussing the limitations and anti-patterns commonly found for each object type in your database. Legacy code isn’t just found in queries and external applications. It’s also found in the definitions of underlying database objects such as views and tables. This book helps you quickly find problematic code throughout the database and points out where and how modern solutions can replace older code, thereby making your legacy applications run faster and extending their lifetimes. Author Lisa Bohm explains the logic behind each anti-pattern, helping you understand why each pattern is a problem and showing how it can be avoided. Good coding habits are discussed, including guidance on topics such as readability and maintainability. What You Will Learn Find specific areas in code to target for performance gains Identify pain points quickly and understand why they are problematic Rewrite legacy T-SQL to reduce or eliminate hidden performance issues Write modern code with an awareness of readability and maintainability Recognize and correlate T-SQL anti-patterns with techniques for better solutions Make a positive impact on application user experience in your organization Who This Book Is For Database administrators or developers who maintain older code, those frustrated with complaints about slow codewhen there is so much of it to fix, and those who want a head start in making a positive impact on application user experience in their organization

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

Credit Data and Scoring: The First Triumph of Big Data and Big Algorithms illuminates the often-hidden practice of predicting an individual’s economic responsibility. Written by a leading practitioner, it examines the international implications of US leadership in credit scoring and what other countries have learned from it in building their own systems. Through its comprehensive contemporary perspective, the book also explores how algorithms and big data are driving the future of credit scoring. By revealing a new big picture and data comparisons, it delivers useful insights into legal, regulatory and data manipulation. Provides insights into credit scoring goals and methods Examines U.S leadership in developing credit data and algorithms and how other countries depart from it Analyzes the growing influence of algorithms in data scoring

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Explore, create, and manage highly interactive data visualizations using Microsoft Power BI Extract meaningful business insights from your disparate enterprise data using the detailed information contained in this practical guide. Written by a recognized BI expert and bestselling author, Data Analysis with Microsoft Power BI teaches you the skills you need to interact with, author, and maintain robust visualizations and custom data models. Hands-on exercises based on real-life business scenarios clearly demonstrate each technique. Publishing your results to the Power BI Service (PowerBI.com) and Power BI Report Server are also fully covered. Inside, you will discover how to: •Understand Business Intelligence and self-service analytics •Explore the tools and features of Microsoft Power BI •Create and format effective data visualizations •Incorporate advanced interactivity and custom graphics •Build and populate accurate data models •Transform data using the Power BI Query Editor •Work with measures, calculated columns, and tabular models •Write powerful DAX language scripts •Share content on the PowerBI Service (PowerBI.com) •Store your visualizations on the Power BI Report Server

The SQL Workshop is your go-to guide for delving into the essential techniques and best practices of working with SQL. You'll start with the basics of querying and database management, progressing to advanced concepts like joins, normalization, and database security. What this Book will help me do Construct and maintain relational databases that meet real-world requirements. Perform CRUD operations efficiently using SQL queries. Design effective and optimized database schemas through normalization. Secure and safeguard data with access controls and privilege management. Leverage SQL for data analysis and reporting through advanced query techniques. Author(s) Frank Solomon, Prashanth Jayaram, and Awni Al Saqqa bring together decades of practical and academic experience in SQL and database management. Their informative and hands-on approach helps readers bridge the gap between theoretical concepts and practical applications. Who is it for? Written for newcomers and intermediate learners, this book is ideal for aspiring software developers, data scientists, and database managers looking to advance their SQL skills. Beginners with no database experience will find this book's gradual learning curve approachable.

For nearly a decade, Apache Kafka has been the go-to publish-subscribe (pub-sub) messaging system—and for good reason. It offers functionality for a wide range of enterprise use cases, along with a large ecosystem of tools and a dedicated community. But lately, upstart Apache Pulsar has been gaining ground. This detailed report explains why. Apache Pulsar takes the best parts of Kafka and expands on them to solve problems that were out of scope of Kafka’s original design. Author Chris Bartholomew shows you how Kafka and Pulsar compare and where they differ. Engineers and other technical decision makers will learn the advantages that make Pulsar a compelling alternative to Kafka. Explore the architecture and major components of Kafka and Pulsar Discover the benefits of Pulsar’s subscription model for messaging Understand how Pulsar simplifies the messaging system for organizations that need high performance pub-sub messaging, delivery guarantees, and traditional messaging patterns Learn how Pulsar’s separation of serving and storing makes it natural to run in cloud native environments like Kubernetes See how Kafka and Pulsar perform on the OpenMessage Project benchmark

Fast access to data has become a critical game changer. Today, a new breed of company understands that the faster they can build, access, and share well-defined datasets, the more competitive they’ll be in our data-driven world. In this practical report, Scott Haines from Twilio introduces you to operational analytics, a new approach for making sense of all the data flooding into business systems. Data architects and data scientists will see how Apache Kafka and other tools and processes laid the groundwork for fast analytics on a mix of historical and near-real-time data. You’ll learn how operational analytics feeds minute-by-minute customer interactions, and how NewSQL databases have entered the scene to drive machine learning algorithms, AI programs, and ongoing decision-making within an organization. Understand the key advantages that data-driven companies have over traditional businesses Explore the rise of operational analytics—and how this method relates to current tech trends Examine the impact of can’t wait business decisions and won’t wait customer experiences Discover how NewSQL databases support cloud native architecture and set the stage for operational databases Learn how to choose the right database to support operational analytics in your organization

The demand for data scientists is well-known, but when it comes time to build solutions based on data, your company also needs data engineers—people with strong data warehousing and programming backgrounds. In fact, whether you’re powering self-driving cars or creating music playlists, this field has emerged as one of the most important in modern business. In this report, Lewis Gavin explores key aspects of data engineering and presents a case study from Spotify that demonstrates the tremendous value of this role.

Don’t be afraid of the GDPR wolf! How can your business easily comply with the new data protection and privacy laws and avoid fines of up to $27M? GDPR For Dummies sets out in simple steps how small business owners can comply with the complex General Data Protection Regulations (GDPR). These regulations apply to all businesses established in the EU and to businesses established outside of the EU insofar as they process personal data about people within the EU. Inside, you’ll discover how GDPR applies to your business in the context of marketing, employment, providing your services, and using service providers. Learn how to avoid fines, regulatory investigations, customer complaints, and brand damage, while gaining a competitive advantage and increasing customer loyalty by putting privacy at the heart of your business. Find out what constitutes personal data and special category data Gain consent for online and offline marketing Put your Privacy Policy in place Report a data breach before being fined 79% of U.S. businesses haven’t figured out how they’ll report breaches in a timely fashion, provide customers the right to be forgotten, conduct privacy impact assessments, and more. If you are one of those businesses that hasn't put a plan in place, then GDPR For Dummies is for you.

Tableau Desktop Certified Associate: Exam Guide is your companion for mastering Tableau and preparing for the certification exam with confidence. Through this book, you will gain a comprehensive understanding of Tableau Desktop's features and learn to implement them in practical scenarios to solve analytics challenges. What this Book will help me do Understand and apply Tableau best practices for analyzing and visualizing data effectively. Visualize geographic data using vector maps and gain insights into spatial distributions. Leverage advanced analytics techniques such as forecasting to predict key metrics. Build effective dashboards that convey information clearly and efficiently. Gain confidence in tackling Tableau Desktop Certified Associate exam questions with expert tips and mock exams. Author(s) The authors, Dmitry Anoshin, JC Gillet, Peri Biyani, and others, are experienced professionals in data analytics and business intelligence. With significant expertise in teaching and applying Tableau, they bring a wealth of knowledge to this guide, offering clear instructions and practical insights. Their dedication to empowering learners fosters a supportive and assured journey through this book. Who is it for? This book is ideal for business analysts, BI professionals, and data analysts aiming to become certified Tableau Desktop Associates. If you have a foundational understanding of Tableau Desktop and are looking to deepen your expertise while preparing for certification, this book is tailored to help you achieve that goal.

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the Technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the Book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's Inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the Reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the Authors Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science. Quotes Full of useful shared experience and practical advice. Highly recommended. - From the Foreword by Jeremy Howard and Rachel Thomas Great examples and an informative walk-through of the data science process. - David Meza, NASA Offers interesting perspectives that cover many aspects of practical data science; a good reference. - Pascal Barbedor, BL SET R you ready to get data science done the right way? - Taylor Dolezal, Disney Studios

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

Harness the power of PolyBase data virtualization software to make data from a variety of sources easily accessible through SQL queries while using the T-SQL skills you already know and have mastered. PolyBase Revealed shows you how to use the PolyBase feature of SQL Server 2019 to integrate SQL Server with Azure Blob Storage, Apache Hadoop, other SQL Server instances, Oracle, Cosmos DB, Apache Spark, and more. You will learn how PolyBase can help you reduce storage and other costs by avoiding the need for ETL processes that duplicate data in order to make it accessible from one source. PolyBase makes SQL Server into that one source, and T-SQL is your golden ticket. The book also covers PolyBase scale-out clusters, allowing you to distribute PolyBase queries among several SQL Server instances, thus improving performance. With great flexibility comes great complexity, and this book shows you where to look when queries fail, complete with coverageof internals, troubleshooting techniques, and where to find more information on obscure cross-platform errors. Data virtualization is a key target for Microsoft with SQL Server 2019. This book will help you keep your skills current, remain relevant, and build new business and career opportunities around Microsoft’s product direction. What You Will Learn Install and configure PolyBase as a stand-alone service, or unlock its capabilities with a scale-out cluster Understand how PolyBase interacts with outside data sources while presenting their data as regular SQL Server tables Write queries combining data from SQL Server, Apache Hadoop, Oracle, Cosmos DB, Apache Spark, and more Troubleshoot PolyBase queries using SQL Server Dynamic Management Views Tune PolyBase queries using statistics and execution plans Solve common business problems, including "cold storage" of infrequentlyaccessed data and simplifying ETL jobs Who This Book Is For SQL Server developers working in multi-platform environments who want one easy way of communicating with, and collecting data from, all of these sources

This IBM® Redpaper™ publication is a comprehensive guide covering the IBM Power System L922 (9008-22L) server, which was designed for data-intensive workloads such as databases and analytics in the Linux operating system. The objective of this paper is to introduce the major innovative Power L922 offering and its relevant functions: The new IBM POWER9™ processor, available at frequencies of 2.7 - 3.8 GHz, 2.9 - 3.8 GHz, and 3.4 - 3.9 GHz. Significantly strengthened cores and larger caches. Two integrated memory controllers that allow double the memory footprint of IBM POWER8® processor-based servers. An integrated I/O subsystem and hot-pluggable Peripheral Component Interconnect Express (PCIe) Gen4 and Gen3 I/O slots. I/O drawer expansion options offer greater flexibility. Support for Coherent Accelerator Processor Interface (CAPI) 2.0. New feature IBM EnergyScale™ technology provides new variable processor frequency modes that provide a significant performance boost beyond the static nominal frequency. This publication is for professionals who want to acquire a better understanding of IBM Power Systems™ products. The intended audience includes the following roles: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the current set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the Power L922 system. This paper does not replace the current marketing materials and configuration tools. It is intended as an extra source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions.

Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators.

IBM Storage for Red Hat OpenShift is a comprehensive container-ready solution that includes all the hardware & software components necessary to setup and/or expand your Red Hat OpenShift environment. This blueprint includes Red Hat OpenShift Container Platform and uses Container Storage Interface (CSI) standards. IBM Storage brings enterprise data services to containers. In this blueprint, learn how to: · Combine the benefits of IBM Systems with the performance of IBM Storage solutions so that you can deliver the right services to your clients today! · Build a 24 by 7 by 365 enterprise class private cloud with Red Hat OpenShift Container Platform utilizing new open source Container Storage interface (CSI) drivers · Leverage enterprise class services such as NVMe based flash performance, high data availability, and advanced container security IBM Storage for Red Hat OpenShift Container Platform is designed for your DevOps environment for on-premises deployment with easy-to-consume components built to perform and scale for your enterprise. Simplify your journey to cloud with pre-tested and validated blueprints engineered to enable rapid deployment and peace of mind as you move to a hybrid multicloud environment. You now have the capabilities.

This document is intended to facilitate the deployment of the Splunk Enterprise Solutions using IBM All Flash Array systems for the Hot and Warm tiers, and IBM Elastic Storage System for the Cold and Frozen tiers. This document provides the reference architecture and configuration guidelines for the IBM Storage systems. The information in this document is distributed on an "as is" basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Storage Systems are supported, entitled and where the issues are specific to a blueprint implementation.

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Discover the methodologies and best practices for getting started with Google Cloud Platform relational services – CloudSQL and CloudSpanner. The book begins with the basics of working with the Google Cloud Platform along with an introduction to the database technologies available for developers from Google Cloud. You'll then take an in-depth hands on journey into Google CloudSQL and CloudSpanner, including choosing the right platform for your application needs, planning, provisioning, designing and developing your application. Sample applications are given that use Python to connect to CloudSQL and CloudSpanner, along with helpful features provided by the engines. You''ll also implement practical best practices in the last chapter. Hands On Google Cloud SQL and Cloud Spanner is a great starting point to apply GCP data offerings in your technology stack and the code used allows you to try out the examples and extend them in interestingways. What You'll Learn Get started with Big Data technologies on the Google Cloud Platform Review CloudSQL and Cloud Spanner from basics to administration Apply best practices and use Google’s CloudSQL and CloudSpanner offering Work with code in Python notebooks and scripts Who This Book Is For Application architects, database architects, software developers, data engineers, cloud architects.

Focus on the most important and most often overlooked factor in a successful Tableau project—data. Without a reliable data source, you will not achieve the results you hope for in Tableau. This book does more than teach the mechanics of data preparation. It teaches you: how to look at data in a new way, to recognize the most common issues that hinder analytics, and how to mitigate those factors one by one. Tableau can change the course of business, but the old adage of "garbage in, garbage out" is the hard truth that hides behind every Tableau sales pitch. That amazing sales demo does not work as well with bad data. The unfortunate reality is that almost all data starts out in a less-than-perfect state. Data prep is hard. Traditionally, we were forced into the world of the database where complex ETL (Extract, Transform, Load) operations created by the data team did all the heavy lifting for us. Fortunately, we have moved past those days. With the introduction of the Tableau Data Prep tool you can now handle most of the common Data Prep and cleanup tasks on your own, at your desk, and without the help of the data team. This essential book will guide you through: The layout and important parts of the Tableau Data Prep tool Connecting to data Data quality and consistency The shape of the data. Is the data oriented in columns or rows? How to decide? Why does it matter? What is the level of detail in the source data? Why is that important? Combining source data to bring in more fields and rows Saving the data flow and the results of our data prep work Common cleanup and setup tasks in Tableau Desktop What You Will Learn Recognize data sources that are good candidates for analytics in Tableau Connect tolocal, server, and cloud-based data sources Profile data to better understand its content and structure Rename fields, adjust data types, group data points, and aggregate numeric data Pivot data Join data from local, server, and cloud-based sources for unified analytics Review the steps and results of each phase of the Data Prep process Output new data sources that can be reviewed in Tableau or any other analytics tool Who This Book Is For Tableau Desktop users who want to: connect to data, profile the data to identify common issues, clean up those issues, join to additional data sources, and save the newly cleaned, joined data so that it can be used more effectively in Tableau