Query Wizards - data testing made easy - no programming

In the U.S., pharmaceutical firms must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11. Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development. Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11. QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, is the market leader in testing data warehouses used by Part 11-governed companies. For more on QuerySurge and Pharma, please visit http://www.querysurge.com/solutions/pharmaceutical-industry

In the U.S., pharmaceutical firms and medical device manufacturers must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11. Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development. Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11 (for example, Safety Data and Clinical Study project data). QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, has been effective in testing data warehouses used by Part 11-governed companies. The purpose of QuerySurge is to assure that your warehouse is not populated with bad data. In industry surveys, bad data has been found in every database and data warehouse studied and is estimated to cost firms on average $8.2 million annually, according to analyst firm Gartner. Most firms test far less than 10% of their data, leaving at risk the rest of the data they are using for critical audits and compliance reporting. QuerySurge can test up to 100% of your data and help assure your organization that this critical information is accurate. QuerySurge not only helps in eliminating bad data, but is also designed to support Part 11 compliance. Learn more at www.QuerySurge.com

Completing the Data Equation In this presentation, we tackle 2 major challenges to assuring your data quality: 1) Test Data Generation 2) Data Validation We illustrate how GenRocket and QuerySurge, used in conjunction, can solve these challenges. Also see how they can be easily integrated into your Continuous Integration/Continuous Delivery pipeline. Session Overview - Primary challenges organizations are facing with their data projects - Key success factors for data validation & testing - How to setup a workflow around test data generation and data validation using GenRocket & QuerySurge - How to automate this workflow in your CI/CD DataOps pipeline to see the video, go to https://www.youtube.com/embed/Zy25i74l-qo?autoplay=1&showinfo=0

Even in the current age of cloud computing there are still endless benefits of developing thick client software: non-dependency on browser version, offline support, low hosting fees, and utilizing existing end user hardware, to name a few. It's more than likely that your organization is utilizing at least a few thick client applications. Now consider this: as your user base grows, does your think client's back-end server need to grow as well? How quickly? How do you ensure that you provide the correct amount of additional capacity without overstepping and unnecessarily eating into your profits? The answer is volume testing. Read how RTTS does this with IBM Rational Performance Tester.

This is a slide deck from QuerySurge's Big Data Testing webinar. Learn why Testing is pivotal to the success of your Big Data Strategy . Learn more at www.querysurge.com The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth. Learn why testing your enterprise's data is pivotal for success with big data, Hadoop and NoSQL. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool. This information is geared towards: - Big Data & Data Warehouse Architects, - ETL Developers - ETL Testers, Big Data Testers - Data Analysts - Operations teams - Business Intelligence (BI) Architects - Data Management Officers & Directors You will learn how to: - Improve your Data Quality - Accelerate your data testing cycles - Reduce your costs & risks - Provide a huge ROI (as high as 1,300%)

Let’s face it: marketing is in a big data bubble. Everyone is talking about data: big data, data analytics, and big data analytics. At the root of all this data mania in marketing is the very real revolution that’s shaping more data-driven organizations. Big data is finally opening the door to the executive suite for a more hybrid creative-analytical method. The key question data raises is how do we use it to not only know more about our customers, but to directly grow our business in significant ways? Iron Mountain has the answer: broadly embrace testing and controlled experimentation as the new “operating system” of marketing. The answer to big data’s potential is big testing.

ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them. Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace. Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them? RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies. You will learn the answer to the following questions: • What is Big Data and what does it mean to me? • What are the business reasons for a building a Data Warehouse and for using Business Intelligence software? • How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective? • Who are the primary players in this software space? • How do I test these environments? • What tools should I use? This slide deck is geared towards:  QA Testers  Data Architects  Business Analysts  ETL Developers  Operations Teams  Project Managers ...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.

This document discusses challenges and opportunities in automating testing for data warehouses and BI systems. It notes that while BI projects have adopted agile methodologies, testing has not. Large and diverse data volumes make testing nearly infinite test cases difficult. It proposes a testing lifecycle and V-model for BI systems. Automating complex functional tests, SQL validation, reconciliation, and test data generation can help address challenges by shortening regression cycles and enabling continuous testing. Various automation tools are discussed, including how they can validate ETL processes and reporting integrity. Automation can help complete testing and ensure data quality, compliance, and performance.

Bill Hayduk is the founder and CEO of QuerySurge, a software division that provides data integration and analytics solutions, with headquarters in New York; QuerySurge was founded in 1996 and has grown to serve Fortune 1000 customers through partnerships with technology companies and consulting firms. The document discusses the data and analytics marketplace and provides an overview of concepts like data warehousing, ETL, BI, data quality, data testing, big data, Hadoop, and NoSQL.

Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy. According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage. The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth. Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.

This document summarizes a Hadoop testing workshop agenda. The agenda covers testing fundamentals like unit tests, integration tests, and performance tests. It discusses why testing is important for catching bugs early and allowing for easy development. Specific best practices and examples are provided for writing unit tests for MapReduce jobs using MRUnit. Integration testing techniques using MiniMRCluster and MiniDFSCluster are also covered. The document concludes with suggestions for profiling and benchmarking performance as well as diagnosing issues using Hadoop APIs.

Big Data is perceived as a huge amount of data and information but it is a lot more than this. Big Data may be said to be a whole set of approach, tools and methods of processing large volumes of unstructured as well as structured data. The three parameters on which Big Data is defined i.e. Volume, Variety and Velocity describes how you have to process an enormous amount of data in different formats at different rates. QualiTest is the world’s second largest pure play software testing and QA company. Testing and QA is all that we do! visit us at: www.QualiTestGroup.com

This document provides an overview and summary of a SQL Saturday event on automated database testing. It discusses: 1. The presenter's background and their company Protegra which focuses on Agile and Lean practices. 2. The learning objectives of the presentation which are around why and how to automate database testing using tools like tSQLt and SQLtest. 3. A comparison of Waterfall and Agile methodologies with a focus on how Agile lends itself better to test automation. 4. A demonstration of setting up and running simple tests using tSQLt to showcase how it can automate database testing and make it easier compared to traditional methods.

This document discusses automating enterprise application and data warehouse testing using QuerySurge. It begins with an introduction to QuerySurge and its modules for automating data interface testing. These modules allow testing across different data sources with no coding required. The document then covers data maturity models and how QuerySurge can help improve testing processes. It demonstrates how QuerySurge can automate testing to gain full coverage while decreasing testing time. In conclusion, it discusses how QuerySurge provides value through increased testing efficiency and data quality.

Data Warehouse, ETL & Migration projects are exposed to huge financial risks due to lack of QA automation. At iCEDQ, we suggest the agile rules based testing approach for all data integration projects.

Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.

This document presents a business case for adopting Real Application Testing (RAT) to test upgrades and patches for a large insurance company's data warehousing system. It estimates that using RAT could reduce testing labor hours by 41% and yield a payback period of less than 12 months due to savings in upgrade costs, monthly patching costs, and production support costs. Adopting RAT could potentially provide even greater savings if it was used to test all 53 applications in the system or reduce issues across all patch upgrades.

Slide deck of our webinar about QuerySurge AI, a new paradigm that provides a radical shift in ETL testing by leveraging artificial intelligence through its no-code low-code solution. During this webinar, we covered the following topics, showcasing the features of QuerySurge AI: - How to utilize QuerySurge AI to fully automate the test development process - How to quickly convert data mapping documents with complex logic transformations from plain text into data validation tests in the data store’s native SQL with little to no human intervention - How QuerySurge AI automatically injects these tests into QuerySurge folders, ready for execution - How quickly these test can be run to completion The Goal - Gain valuable insights into how QuerySurge AI can benefit your organization, including: - A dramatic reduction in test development time through artificial intelligence - Reduced skillset needed for test creation -A massive increase in ROI For more information on QuerySurge AI, go to www.QuerySurge.com

We explore how extract, transform and load (ETL) testing with SQL scripting is crucial to data validation and show how to test data on a large scale in a streamlined manner with an Informatica ETL testing tool.

Sailaja Prasad Mohanty is a software test engineer with 3 years of experience in testing data warehouses and reporting tools. He has worked on projects involving Teradata, SAP HANA, Vertica, and Tableau. His skills include test automation using Selenium, Protractor, Python and Java. He is proficient in test data management tools like CA TDM and performance testing tools like JMeter. He is currently working as a test engineer at Infosys where he performs data warehouse testing, requirement gathering, test automation, and knowledge transfer.

Alok Singh is seeking challenging assignments in Business Intelligence/Data warehousing. He has nearly 7 years of experience in BI/DW, ETL, data integration, and data warehousing solution design. He is proficient in SQL, ETL tools like Informatica and SSIS, and visualization tools like QlikView and Tableau. He has experience designing and developing ETL solutions, requirements gathering, and data analysis. His past roles include positions at Technologia, Subex, and Reliance Communications where he worked on projects involving Teradata, Oracle, billing systems, and fraud detection. He has a bachelor's degree in electronics and telecommunications.