How to Automate your Enterprise Application / ERP Testing

QuerySurge, the smart data testing solution, QuerySurge, the smart data testing solution that automates data validation & testing of critical data, released the first-of-its-kind full DevOps solution for continuous data testing. The latest release, QuerySurge-for-DevOps, enables users to drive changes to their test components programmatically while interfacing with virtually all DevOps solutions in the marketplace. See how to implement a DevOps-for-Data solution in your delivery pipeline and improve your data quality at speed! Testers will now have the capability to dynamically generate, execute, and update tests and data stores utilizing API calls. QuerySurge for DevOps has 60+ API calls with almost 100 different properties. This will enable a higher percentage of automation in your current data testing practice and a more robust DevOps for Data, or DataOps pipeline. API Features Include: - Create and modify source and target test queries - Create and modify connections to data stores - Create and modify the tests associated with an execution suite - Create and modify new staging tables from various data connections - Create custom flow controls based on run results - Integration with virtually all build solutions in the market QuerySurge for DevOps integrates with: - Continuous integration/ETL solutions - Automated build/release/deployment solutions - Operations and DevOps monitoring solutions - Test management/issue tracking solutions - Scheduling and workload automation solutions For more information on QuerySurge for DevOps, visit: https://www.querysurge.com/solutions/querysurge-for-devops

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

Testing of Hadoop, NoSQL and Data Warehouses Visually ----------------------------------------------------------------------------- We just made automated data testing really easy. Automate your Big Data testing visually, with no programming needed. See how to automate Hadoop, No SQL and Data Warehouse testing visually, without writing any SQL or HQL. See how QuerySurge, the leading Big Data testing solution, provides novices and non-technical team members with a fast & easy way to be productive immediately while speeding up testing for team members skilled in SQL/HQL. This webinar 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 • Realize a huge ROI

This document discusses strategies for creating an effective data validation and testing process. It provides examples of common data issues found during testing such as missing data, wrong translations, and duplicate records. Solutions discussed include identifying important test points, reviewing data mappings, developing automated and manual testing approaches, and assessing how much data needs validation. The presentation also includes a case study of a company that improved its process by centralizing documentation, improving communication, and automating more of its testing.

Implementing Azure DevOps With Your Testing Project Are you challenged with different teams working on different platforms making it difficult to get insight into another team’s work? Is your team seeking ways to automate the code deployments so you can spend more time developing new features and writing more tests, and spend less time deploying and running manual tests? RTTS, a Microsoft Gold DevOps Partner, will take you through solving these challenges with Azure DevOps. Tuesday, June 16th 2020 @11am ET Session Overview ------------------------------------ During the webinar, we will walk you through the following process of utilizing Azure DevOps: - The challenges that inspired the Azure DevOps solution that you may experience as well - The strategy for implementing Azure Devops - Solutions in our every day processes to increase our times efficiency and save time - A demo of an Azure DevOps environment for testing teams The see a recording of the webinar, please visit: https://www.youtube.com/watch?v=2vIic3wxaS4 To learn more about RTTS, please visit: https://www.rttsweb.com

This document discusses testing of big data systems. It defines big data and its key characteristics of volume, variety, velocity and value. It provides examples of big data success stories and compares enterprise data warehouses to big data. The document outlines the typical architecture of a big data system including pre-processing, MapReduce, data extraction and loading. It identifies potential problems at each stage and for non-functional testing. Finally, it covers new challenges for testers in validating big data systems.

1. The document discusses continuous delivery pipelines for Hadoop analytics platforms using tools like Cloudera Director, Jenkins, Git, and Gerrit to automate builds, testing, and deployments. 2. It provides examples of different pipeline stages for data engineers, data scientists, and application developers including developing code, running unit tests, baking artifacts, deploying to test and production clusters, and conducting user acceptance testing. 3. The final section discusses how a logical continuous delivery pipeline would work with hourly-daily deployments for DevOps teams and weekly-monthly releases for data scientists and analysts to reduce bugs in production.

The document discusses QuerySurge, an automated data testing solution that helps verify data quality and find errors. It notes that traditional data quality tools focus on profiling, cleansing and monitoring data, while QuerySurge also enables data testing through easy-to-use query wizards and comparison of source and target data without SQL coding. QuerySurge allows collaborative testing across teams and platforms, integrates with development tools, and can significantly reduce testing time and improve data quality.

Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.

Big Data Analytics in the Cloud using Microsoft Azure services was discussed. Key points included: 1) Azure provides tools for collecting, processing, analyzing and visualizing big data including Azure Data Lake, HDInsight, Data Factory, Machine Learning, and Power BI. These services can be used to build solutions for common big data use cases and architectures. 2) U-SQL is a language for preparing, transforming and analyzing data that allows users to focus on the what rather than the how of problems. It uses SQL and C# and can operate on structured and unstructured data. 3) Visual Studio provides an integrated environment for authoring, debugging, and monitoring U-SQL scripts and jobs. This allows

Modern DW Architecture - The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.

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

In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access. Access additional slides from this meetup here: http://www.slideshare.net/CasertaConcepts/big-data-warehousing-meetup-january-20 For more information on our services or upcoming events, please visit http://www.actian.com/ or http://www.casertaconcepts.com/.

This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.

How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services? Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business. Join Hortonworks and Informatica as we discuss: - What is a data lake? - The modern data architecture for a data lake - How Hadoop fits into the modern data architecture - Innovative use-cases for a data lake

These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.

Fast and easy. No Programming needed. The latest QuerySurge release introduces the new Query Wizards. The Wizards allow both novice and experienced team members to validate their organization's data quickly with no SQL programming required. The Wizards provide an immediate ROI through their ease-of-use and ensure that minimal time and effort are required for developing tests and obtaining results. Even novice testers are productive as soon as they start using the Wizards! According to a recent survey of Data Architects and other data experts on LinkedIn, approximately 80% of columns in a data warehouse have no transformations, meaning the Wizards can test all of these columns quickly & easily, (The columns with transformations can be tested using the QuerySurge Design library using custom SQL coding.) There are 3 Types of automated Data Comparisons: - Column-Level Comparison - Table-Level Comparison - Row Count Comparison There are also automated features for filtering (‘Where’ clause) and sorting (‘Order By’ clause). The Wizards provide both novices and non-technical team members with a fast & easy way to be productive immediately and speed up testing for team members skilled in SQL. Trial our software either as a download or in the cloud at www.QuerySurge.com. The trial comes with a built-in tutorial and sample data.

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.

This document discusses applying DevOps practices and principles to machine learning model development and deployment. It outlines how continuous integration (CI), continuous delivery (CD), and continuous monitoring can be used to safely deliver ML features to customers. The benefits of this approach include continuous value delivery, end-to-end ownership by data science teams, consistent processes, quality/cadence improvements, and regulatory compliance. Key aspects covered are experiment tracking, model versioning, packaging and deployment, and monitoring models in production.

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.

This document discusses techniques for optimizing Power BI performance. It recommends tracing queries using DAX Studio to identify slow queries and refresh times. Tracing tools like SQL Profiler and log files can provide insights into issues occurring in the data sources, Power BI layer, and across the network. Focusing on optimization by addressing wait times through a scientific process can help resolve long-term performance problems.

The document provides an overview of DataOps and continuous integration/continuous delivery (CI/CD) practices for data management. It discusses: - DevOps principles like automation, collaboration and agility can be applied to data management through a DataOps approach. - CI/CD practices allow for data products and analytics to be developed, tested and released continuously through an automated pipeline. This includes orchestration of the data pipeline, testing, and monitoring. - Adopting a DataOps approach with CI/CD enables faster delivery of data and analytics, more efficient and compliant data pipelines, improved productivity, and better business outcomes through data-driven decisions.

This document discusses various business intelligence tools for data analysis including ETL, OLAP, reporting, and metadata tools. It provides evaluation criteria for selecting tools, such as considering budget, requirements, and technical skills. Popular tools are identified for each category, including Informatica, Cognos, and Oracle Warehouse Builder. Implementation requires determining sources, data volume, and transformations for ETL as well as performance needs and customization for OLAP and reporting.