Automated Data Warehousing Testing is a beginner's step by step guide for novice to intermediate level testers who want to try their hands at automated testing. It provides step by step instructions for setting up the Automation Framework from scratch. The framework is quite generic and as such can be applied to most data warehousing projects. This book concentrates on the 'practical side' of automated testing rather than the 'theoretical side'. It includes complete listings of the automated code for a sample data warehouse that is set up for testing. The code listings explain the logic for the individual tests and generic functions. The book covers: An overview of the data warehouse architecture and modelling. Set up the environment for automation. Software used is open source and freeware. Set up the Automation Framework from scratch and add important features to it like logging information to console and files. Set up the sample data warehouse for automation including the source systems and staging area. Automate the testing of staging area, dimension and fact tables. Automate the testing of CSV and Microsoft Excel data sources. Generate HTML reports of the test results. Automate Data Profiling tests.
The quality of a data warehouse (DWH) is the elusive aspect of it, not because it is hard to achieve [once we agree what it is], but because it is difficult to describe. We propose the notion that quality is not an attribute or a feature that a product has to possess, but rather a relationship between that product and each and every stakeholder. More specifically, the relationship between the software quality and the organization that produces the products is explored. Quality of data that populates the DWH is the main concern of the book, therefore we propose a definition for data quality as: "fitness to serve each and every purpose". Methods are proposed throughout the book to help readers achieve data warehouse quality.
With the urgent demand for rapid turnaround on new software releases--without compromising quality--the testing element of software development must keep pace, requiring a major shift from slow, labor-intensive testing methods to a faster and more thorough automated testing approach. Automated Software Testing is a comprehensive, step-by-step guide to the most effective tools, techniques, and methods for automated testing. Using numerous case studies of successful industry implementations, this book presents everything you need to know to successfully incorporate automated testing into the development process. In particular, this book focuses on the Automated Test Life Cycle Methodology (ATLM), a structured process for designing and executing testing that parallels the Rapid Application Development methodology commonly used today. Automated Software Testing is designed to lead you through each step of this structured program, from the initial decision to implement automated software testing through test planning, execution, and reporting. Included are test automation and test management guidance for: Acquiring management support Test tool evaluation and selection The automated testing introduction process Test effort and test team sizing Test team composition, recruiting, and management Test planning and preparation Test procedure development guidelines Automation reuse analysis and reuse library Best practices for test automation
Testing in data warehouse systems is substantial because it is oriented towards the correctness and validation of data/ information supplied for decision making. Keeping in view the idiosyncratic characteristics of data warehouse testing and the complexity of data warehouse projects, this research has reviewed and revised the scope of automated testing in assuring quality data warehouse solutions. Initially a data set generator has been developed to generate synthetic but near to real data; followed by the classification of anomalies in synthesized data with the help of a hand coded Extraction, Transformation and Loading (ETL) routine. To ensure quality data for a data warehouse and to promulgate the importance of Extraction, Transformation and Loading (ETL) routines some test cases of prime importance were identified. Later on automated testing procedures were embedded in hand coded ETL routine to ensure quality data. The statistical analysis revealed major enhancement in data quality with the introduction of automated testing procedures. The various data warehouse architectures have been analyzed to endorse a refined data warehouse architecture named as Data Sharehouse.
Through expanded intelligence, the use of robotics has fundamentally transformed the business industry. Providing successful techniques in robotic design allows for increased autonomous mobility, which leads to a greater productivity and production level. Rapid Automation: Concepts, Methodologies, Tools, and Applications provides innovative insights into the state-of-the-art technologies in the design and development of robotics and their real-world applications in business processes. Highlighting a range of topics such as workflow automation tools, human-computer interaction, and swarm robotics, this multi-volume book is ideally designed for computer engineers, business managers, robotic developers, business and IT professionals, academicians, and researchers.
An increasing number of organizations are implementing data warehouses to strengthen their decision support systems. This comes with the challenges of the population and the periodic update of data warehouses. In this thesis, we present a tool that provides users with features to create a warehouse database and transform structures of the source database into structures for the warehouse database. It is highly interactive, easy to use, and hides the underlying complexity of manual SQL code generation from its users. Attributes from source tables can be mapped into new attributes in the warehouse database tables using aggregate functions. Then, relevant data is automatically transported from the source database to the newly created warehouse. The tool thus integrates warehouse creation, schema mapping and data population into a single general-purpose tool. This tool has been designed as a component of the framework for an automated data warehouse being developed at the Computer Science Department, University of New Orleans. Users of this framework are the database administrators, who will also be able to synchronize updates of multiple copies of the data warehouse. Warehouse images that need to be updated are taken offline and applications that need to access the data warehouse can now access any of the other image warehouses. The Switching Application built into this framework switches between databases in a way that is totally transparent to applications so that they do not realize existence of multiple copies of the data warehouse. In effect, even non-technical users can create, populate and update data warehouses with minimal time and effort.
Test automation is an essential tool in today’s software development environments. It increases testing efficiency and makes test procedures reliably repeatable.
This book provides a complete overview of how to design test automation processes and integrate them into your organization or existing projects. It details functional and technical strategies and goes into detail on the relevant concepts and best practices. The book’s main focus is on functional system testing.
Topics covered:
• An introduction to test automation
• Objectives and success factors
• Preparing for test automation
• Introduction to generic test automation architectures
• Design and development of a test automation solution
• Risks and contingencies during deployment
• Metrics and reporting
• Transitioning manual testing to an automated environment
• Verifying a test automation solution
• Continuous improvement
The appendix contains an overview of software quality characteristics according to the ISO 25010 standard, and lists potential test automation applications within this context. It also provides an introduction to load and performance testing, and a sample catalog of criteria for selecting test automation tools.
This book is fully compliant with the ISTQB® syllabus and, with its many explanatory examples, is equally suitable for preparation for certification, as a concise reference book for anyone who wants to acquire this essential skill, or for university-level study.
The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term
This groundbreaking book is the first in the Kimball Toolkit series to be product-specific. Microsoft’s BI toolset has undergone significant changes in the SQL Server 2005 development cycle. SQL Server 2005 is the first viable, full-functioned data warehouse and business intelligence platform to be offered at a price that will make data warehousing and business intelligence available to a broad set of organizations. This book is meant to offer practical techniques to guide those organizations through the myriad of challenges to true success as measured by contribution to business value. Building a data warehousing and business intelligence system is a complex business and engineering effort. While there are significant technical challenges to overcome in successfully deploying a data warehouse, the authors find that the most common reason for data warehouse project failure is insufficient focus on the business users and business problems. In an effort to help people gain success, this book takes the proven Business Dimensional Lifecycle approach first described in best selling The Data Warehouse Lifecycle Toolkit and applies it to the Microsoft SQL Server 2005 tool set. Beginning with a thorough description of how to gather business requirements, the book then works through the details of creating the target dimensional model, setting up the data warehouse infrastructure, creating the relational atomic database, creating the analysis services databases, designing and building the standard report set, implementing security, dealing with metadata, managing ongoing maintenance and growing the DW/BI system. All of these steps tie back to the business requirements. Each chapter describes the practical steps in the context of the SQL Server 2005 platform. Intended Audience The target audience for this book is the IT department or service provider (consultant) who is: Planning a small to mid-range data warehouse project; Evaluating or planning to use Microsoft technologies as the primary or exclusive data warehouse server technology; Familiar with the general concepts of data warehousing and business intelligence. The book will be directed primarily at the project leader and the warehouse developers, although everyone involved with a data warehouse project will find the book useful. Some of the book’s content will be more technical than the typical project leader will need; other chapters and sections will focus on business issues that are interesting to a database administrator or programmer as guiding information. The book is focused on the mass market, where the volume of data in a single application or data mart is less than 500 GB of raw data. While the book does discuss issues around handling larger warehouses in the Microsoft environment, it is not exclusively, or even primarily, concerned with the unusual challenges of extremely large datasets. About the Authors JOY MUNDY has focused on data warehousing and business intelligence since the early 1990s, specializing in business requirements analysis, dimensional modeling, and business intelligence systems architecture. Joy co-founded InfoDynamics LLC, a data warehouse consulting firm, then joined Microsoft WebTV to develop closed-loop analytic applications and a packaged data warehouse. Before returning to consulting with the Kimball Group in 2004, Joy worked in Microsoft SQL Server product development, managing a team that developed the best practices for building business intelligence systems on the Microsoft platform. Joy began her career as a business analyst in banking and finance. She graduated from Tufts University with a BA in Economics, and from Stanford with an MS in Engineering Economic Systems. WARREN THORNTHWAITE has been building data warehousing and business intelligence systems since 1980. Warren worked at Metaphor for eight years, where he managed the consulting organization and implemented many major data warehouse systems. After Metaphor, Warren managed the enterprise-wide data warehouse development at Stanford University. He then co-founded InfoDynamics LLC, a data warehouse consulting firm, with his co-author, Joy Mundy. Warren joined up with WebTV to help build a world class, multi-terabyte customer focused data warehouse before returning to consulting with the Kimball Group. In addition to designing data warehouses for a range of industries, Warren speaks at major industry conferences and for leading vendors, and is a long-time instructor for Kimball University. Warren holds an MBA in Decision Sciences from the University of Pennsylvania's Wharton School, and a BA in Communications Studies from the University of Michigan. RALPH KIMBALL, PH.D., has been a leading visionary in the data warehouse industry since 1982 and is one of today's most internationally well-known authors, speakers, consultants, and teachers on data warehousing. He writes the "Data Warehouse Architect" column for Intelligent Enterprise (formerly DBMS) magazine.
