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%)
Power BI: Introduction with a use case and solutionAlvina Verghis
This PPT gives a brief introduction to the Power BI software. It gives the brief intro of the software with a use case of how Power BI is used in Heathrow Airport for ease of functions
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Data Quality With or Without Apache Spark and Its EcosystemDatabricks
Few solutions exist in the open-source community either in the form of libraries or complete stand-alone platforms, which can be used to assure a certain data quality, especially when continuous imports happen. Organisations may consider picking up one of the available options – Apache Griffin, Deequ, DDQ and Great Expectations. In this presentation we’ll compare these different open-source products across different dimensions, like maturity, documentation, extensibility, features like data profiling and anomaly detection.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Informatica products and usage, informatica developer,informatica analyst,informatica powerexchange,informatica powercenter,informatica data quality,master data management,data masking,data visualization,informatica products list
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
HBase is an open-source, distributed, versioned, key-value database modeled after Google's Bigtable. It is designed to store large volumes of sparse data across commodity hardware. HBase uses Hadoop for storage and provides real-time read and write capabilities. It scales horizontally and is highly fault tolerant through its master-slave architecture and use of Zookeeper for coordination. Data in HBase is stored in tables and indexed by row keys for fast lookup, with columns grouped into families and versions stored by timestamps.
Ramesh Retnasamy provides an overview of his background and courses on Azure Databricks, PySpark, Spark SQL, Delta Lake, Azure Data Lake Storage Gen2, Azure Data Factory, and PowerBI. The document outlines the structure and topics that will be covered in the courses, including Databricks, clusters, notebooks, data ingestion, transformations, Spark, Delta Lake, orchestration with Data Factory, and connecting to other tools. It also discusses prerequisites, commitments to students, and an estimated cost for taking the courses.
Database migration is the process of transferring data between different database systems or upgrades. It involves analyzing and mapping data from the source to the target system, transforming the data, validating data quality, and maintaining the migrated data. For example, Capital One migrated from Oracle to Teradata databases as their data volume grew too large for Oracle to efficiently handle. The migration process includes pre-migration planning, extraction, transformation, data loading, validation, and post-migration maintenance.
DataOps for the Modern Data Warehouse on Microsoft Azure @ NDCOslo 2020 - Lac...Lace Lofranco
Talk Description:
The Modern Data Warehouse architecture is a response to the emergence of Big Data, Machine Learning and Advanced Analytics. DevOps is a key aspect of successfully operationalising a multi-source Modern Data Warehouse.
While there are many examples of how to build CI/CD pipelines for traditional applications, applying these concepts to Big Data Analytical Pipelines is a relatively new and emerging area. In this demo heavy session, we will see how to apply DevOps principles to an end-to-end Data Pipeline built on the Microsoft Azure Data Platform with technologies such as Data Factory, Databricks, Data Lake Gen2, Azure Synapse, and AzureDevOps.
Resources: https://aka.ms/mdw-dataops
You will learn about source control principles and source control systems. You will also learn about Azure repositories, migrating strategies and authentication options.
Power BI has become a product with a ton of exciting features. This presentation will give an overview of some of them, including Power BI Desktop, Power BI service, what’s new, integration with other services, Power BI premium, and administration.
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
High Performance Data Lake with Apache Hudi and Alluxio at T3GoAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
High Performance Data Lake with Apache Hudi and Alluxio at T3Go
Trevor Zhang & Vino Yang (T3Go)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
This document contains a presentation on servlets and HTTP basics. It includes slides on:
- The structure of an HTTP request and response
- HTTP methods like GET, POST, PUT, DELETE
- What servlets are and how they work within a servlet container like Tomcat
- An overview of the javax.servlet package, including interfaces like Servlet and classes like GenericServlet and HttpServlet
- The servlet lifecycle methods like init, service, destroy
- How to set up and use Tomcat as a servlet container
In summary, it provides an introduction to key concepts of HTTP and the servlet programming model in Java web applications.
QuerySurge - the automated Data Testing solutionRTTS
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.
A apresentação discute a evolução dos bancos de dados SQL e NoSQL, introduzindo o conceito de NewSQL. O VoltDB é apresentado como um exemplo de banco de dados NewSQL, destacando suas vantagens de desempenho e escalabilidade em memória principal através do particionamento horizontal, replicação síncrona e recuperação de desastres com snapshots contínuos e registro de comandos. Os benefícios e desafios do uso do VoltDB são descritos.
In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.
Testing Big Data: Automated Testing of Hadoop with QuerySurgeRTTS
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.
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.
DocumentDB is a powerful NoSQL solution. It provides elastic scale, high performance, global distribution, a flexible data model, and is fully managed. If you are looking for a scaled OLTP solution that is too much for SQL Server to handle (i.e. millions of transactions per second) and/or will be using JSON documents, DocumentDB is the answer.
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
This document discusses how organizations can save money on database management systems (DBMS) by moving from expensive commercial DBMS to more affordable open-source options like PostgreSQL. It notes that PostgreSQL has matured and can now handle mission critical workloads. The document recommends partnering with EnterpriseDB to take advantage of their commercial support and features for PostgreSQL. It highlights how customers have seen cost savings of 35-80% by switching to PostgreSQL and been able to reallocate funds to new business initiatives.
This document provides an overview of big data fundamentals and considerations for setting up a big data practice. It discusses key big data concepts like the four V's of big data. It also outlines common big data questions around business context, architecture, skills, and presents sample reference architectures. The document recommends starting a big data practice by identifying use cases, gaining management commitment, and setting up a center of excellence. It provides an example use case of retail web log analysis and presents big data architecture patterns.
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
Webinar - QuerySurge and Azure DevOps in the Azure CloudRTTS
Session Overview
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During this webinar, we covered the following topics while demonstrating our plug-in for Azure DevOps:
- Installing the QuerySurge Azure DevOps Extension
- Key features of Azure DevOps
- Azure DevOps Pipeline creation
- QuerySurge offerings in the Azure Marketplace
- Virtual machine options in the Azure Cloud
- Azure Cloud versus on-prem deployment options for QuerySurge
And we answered the following questions:
- Is QuerySurge in the Azure Cloud the right solution for my team?
- Where does QuerySurge fit into the Azure DevOps platform?
- What are QuerySurge’s various offerings in the Azure Cloud?
- If QuerySurge in the cloud is not the right choice, what is my best deployment option?
T o see a recording of the wwebinar, go to:
https://www.youtube.com/watch?v=Cd7P_nJOejE
Prague data management meetup 2018-03-27Martin Bém
This document discusses different data types and data models. It begins by describing unstructured, semi-structured, and structured data. It then discusses relational and non-relational data models. The document notes that big data can include any of these data types and models. It provides an overview of Microsoft's data management and analytics platform and tools for working with structured, semi-structured, and unstructured data at varying scales. These include offerings like SQL Server, Azure SQL Database, Azure Data Lake Store, Azure Data Lake Analytics, HDInsight and Azure Data Warehouse.
This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.
IBM's Big Data platform provides tools for managing and analyzing large volumes of structured, unstructured, and streaming data. It includes Hadoop for storage and processing, InfoSphere Streams for real-time streaming analytics, InfoSphere BigInsights for analytics on data at rest, and PureData System for Analytics (formerly Netezza) for high performance data warehousing. The platform enables businesses to gain insights from all available data to capitalize on information resources and make data-driven decisions.
Building Big Data Solutions with Azure Data Lake.10.11.17.pptxthando80
The document discusses Microsoft's use of a data lake approach to better leverage large amounts of data from various sources using tools like Azure Data Lake Store, Azure Data Lake Analytics, HDInsight, and Spark. It provides an overview of how Microsoft built their own data lake to handle exabytes of data from different parts of the company and support analytics, machine learning, and real-time streaming. Common patterns for using Azure Data Lake tools for ingesting, storing, analyzing, and visualizing data are also presented.
Presentation big dataappliance-overview_oow_v3xKinAnx
The document outlines Oracle's Big Data Appliance product. It discusses how businesses can use big data to gain insights and make better decisions. It then provides an overview of big data technologies like Hadoop and NoSQL databases. The rest of the document details the hardware, software, and applications that come pre-installed on Oracle's Big Data Appliance - including Hadoop, Oracle NoSQL Database, Oracle Data Integrator, and tools for loading and analyzing data. The summary states that the Big Data Appliance provides a complete, optimized solution for storing and analyzing less structured data, and integrates with Oracle Exadata for combined analysis of all data sources.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
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.
IBM's Big Data platform provides tools for managing and analyzing large volumes of data from various sources. It allows users to cost effectively store and process structured, unstructured, and streaming data. The platform includes products like Hadoop for storage, MapReduce for processing large datasets, and InfoSphere Streams for analyzing real-time streaming data. Business users can start with critical needs and expand their use of big data over time by leveraging different products within the IBM Big Data platform.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Automated Testing of Microsoft Power BI ReportsRTTS
WEBINAR:Automated Testing of Microsoft Power BI Reports
Learn how QuerySurge automates the testing of Microsoft Power BI reports in minutes using our new Power BI Testing Wizard.
Learn:
- How to utilize the QuerySurge -Power BI wizard to automate data validation tests against Microsoft Power BI Reports
- How to quickly generate SQL using the Power BI wizard’s built-in SQL generator
- How to handle Power BI report slicer variations in your data validation tests
- How to access report data that has Row Level security enabled using the Power BI Wizard
The Goal
Gain valuable insights into how QuerySurge’s Power BI testing wizard can benefit your organization, including:
- Providing a dramatic reduction in test development time through built-in SQL generation
- Reduced skillset needed for test creation
- Expanded coverage of your Microsoft Power BI report testing efforts
For more, visit www.QuerySurge.com
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
Learn statistics and expert opinions on the state of the market regarding data quality in 2023.
Learn about:
- statistics and expert opinions
- the key focus of data quality in 2023
- the Data Maturity Model
- DevOps for data and CI/CD pipelines
- data validation and ETL testing
- test automation
TestGuild and QuerySurge Presentation -DevOps for Data TestingRTTS
This slide deck is from one of our 4 webinars in our half-day series in conjunction with Test Guild.
Chris Thompson and Mike Calabrese, Senior Solution Architects and QuerySurge experts, provide great information, a demo and lots of humor in this webinar on how to implement DevOps for Data in your DataOps pipeline.
This webinar was performed in conjunction with Test Guild.
To watch the video, go to:
https://youtu.be/1ihuRPgY_rs
Creating a Project Plan for a Data Warehouse Testing AssignmentRTTS
This document provides guidance on creating a project plan for testing a data warehouse project. It discusses key aspects to consider such as reviewing documentation, estimating resources like test engineers, determining the number of ETL legs and release cycles, assessing test complexity, and ensuring the test automation tool QuerySurge is configured. An example project plan estimates the time to review documentation, identifies one test engineer and ETL leg, plans for four release cycles, and provides estimates of 7 low complexity, 21 medium complexity, and 8 high complexity tests.
RTTS Postman and API Testing Webinar Slides.pdfRTTS
RTTS Webinar Slide Deck: Postman & API Testing
In this webinar about Postman, we reviewed the importance of API testing and the need for this as organizations continue to move towards an API centered architecture and demonstrate how Postman can be used to perform this testing efficiently.
Webinar Details
Session Overview
During this webinar, we covered the following topics while demonstrating API testing with
Postman:
- What are API's and how they are being used today
- Importance of API testing
- How RTTS uses Postman to verify API's and the features Postman provides
- Demo of Postman using public API's
The video of the webinar can be found on YouTube at:
https://youtu.be/xWHSXu64T2o
How to Automate your Enterprise Application / ERP TestingRTTS
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.
Creating a Data validation and Testing StrategyRTTS
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 ProjectRTTS
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
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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
Data Warehouse Testing in the Pharmaceutical IndustryRTTS
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: Test Data + Data Validation = SuccessRTTS
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
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
Leveraging HPE ALM & QuerySurge to test HPE VerticaRTTS
Are you using HPE ALM or Quality Center (QC) for your requirements gathering and test management?
RTTS, an alliance partner of HPE and a member of HPE’s Big Data community, can show you how to use ALM/QC and RTTS’ QuerySurge to effectively manage your data validation & testing of Vertica (or any data warehouse).
In this webinar video you will see:
- a custom view of ALM to store source-to-target mappings
- data validation tests in QuerySurge
- the execution of QuerySurge tests from ALM
- the results of data validation tests stored in ALM
- custom ALM reports that show data validation coverage of Vertica
how we improve your data quality while reducing your costs & risks
Presented by:
Bill Hayduk, Founder & CEO of RTTS, the developers of QuerySurge
Chris Thompson, Senior Domain Expert, Big Data testing
To learn more about QuerySurge, visit www.QuerySurge.com
Whitepaper: Volume Testing Thick Clients and DatabasesRTTS
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.
Query Wizards - data testing made easy - no programmingRTTS
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.
Case study: Open Source Automation Framework using Selenium WebDriverRTTS
Synopsis: The client provides training, nutrition, and physical therapy programs by a team of specialists. As part of their program, they utilize software that integrates with workout machines to provide the user with recommended training exercises based on previous workouts, weekly workout challenges, and member goals. Athletes’ Performance is looking to implement a functional test automation framework for their application in order to perform regression testing as new builds are released.
Enterprise Business Intelligence & Data Warehousing: The Data Quality ConundrumRTTS
The document summarizes the findings of a study on data quality challenges in business intelligence and data warehousing. It found that most companies test less than 10% of their data and experience issues from bad data like incorrect reports, poor delivery quality, and missed opportunities. Common challenges are a lack of automation in testing and not enough testing resources. The survey indicates that as data volumes continue growing, more comprehensive automated testing solutions will be needed to ensure data quality.
How healthy is your data?
Data health is a multi-dimensional indicator of the integrity and effectiveness of your organization's most valuable asset. It is something that is increasingly difficult to be sure of when your data is growing in size and complexity, and when your team is becoming more dispersed.
Get insight into your Big Data like never before with the Data Health Dashboards in QuerySurge, the leading Data Testing software. These dashboards will enable you to easily see trends in both your data and your team's performance.
In this slide deck, you will learn how to:
- Improve your data quality
- Reduce your costs & risks
- Accelerate your data testing cycles
- Share information with your team
- Gain a holistic view of the health of your data
To see the Webinar, please visit:
http://www.querysurge.com/solutions/data-warehouse-testing/improve-data-health
Big Data Testing: Ensuring MongoDB Data QualityRTTS
You've made the move to MongoDB for its flexible schema and querying capabilities in order to enhance agility and reduce costs for your business. Shouldn't your data quality process be just as organized and efficient?
Using QuerySurge for testing your MongoDB data as part of your quality effort will increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your Big Data store. QuerySurge will help you keep your team organized and on track too!
To learn more about QuerySurge, visit www.QuerySurge.com
Old Tools, New Tricks: Unleashing the Power of Time-Tested Testing ToolsBenjamin Bischoff
In the rapidly evolving landscape of software development and testing, it is tempting to chase the latest tools and technologies. However, some of the most effective solutions have been in existence for decades. In this talk, we’ll delve into the enduring value of these timeless testing tools.
We’ll explore how established tools like Selenium, GNU Make, Maven, and Bash remain vital in today’s software development and testing toolkit even though they have been around for a long time (some were even invented before I was born). I’ll share examples of how these tools have addressed our testing and automation challenges, showcasing their adaptability, versatility, and reliability in various scenarios. I aim to demonstrate that sometimes, the “old” ways can indeed be the best ways.
The SQDC (Safety, Quality, Delivery, Cost) process enhances manufacturing performance through daily safety meetings, defect tracking, and waste reduction. Orcalean’s FactoryKPI digital dashboard streamlines this process, providing real-time data and AI-powered analytics for continuous improvement.
In today's dynamic business landscape, ERP software systems are essential tools for businesses worldwide, including those in the UAE. These systems cater to the unique needs of the UAE's rapidly changing economy and expanding industries.
This blog examines the top 10 ERP companies in the UAE, highlighting their innovative products, exceptional customer support, and significant impact on the regional business community. These companies excel in providing ERP solutions that enhance efficiency and growth for businesses throughout the UAE.
1. **Odoo**
- Odoo ERP is a comprehensive business management solution with features like accounting, HR, sales, inventory control, and CRM. Its user-friendly interface simplifies processes and boosts productivity. Banibro IT Solutions leverages Odoo to transform business operations.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: Yes
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Chat, Email
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Windows, Mac, iOS, Android
- API: Available
2. **Microsoft Dynamics 365**
- Dynamics 365 offers a centralized platform for small and medium-sized businesses, integrating with Microsoft apps and cloud services for scalability. It simplifies data processing with user-friendly interfaces and customizable reporting.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, iOS, Android
- API: Not specified
3. **FirstBIT ERP**
- Known for serving small and medium-sized businesses, FirstBIT ERP offers comprehensive solutions and exceptional customer service, enhancing productivity and efficiency.
- **Details:**
- Suitable for: Medium, Large Businesses
- Open Source: Yes/No
- Cloud-based: Yes (Cloud and On-premises)
- Support: Phone, Email, Video Tutorials
- Payment: Yearly, Monthly
- Multi-Language: Yes
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Available
4. **Ezware Technologies**
- Ezware Technologies provides top-notch ERP solutions for various industries with user-friendly modules that streamline complex business processes.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Support: Phone, Chat, Email, Knowledge Base
- Payment: One-Time, Yearly, Monthly
- Multi-Language: No
- OS Support: Web App, Windows, Mac, iOS, Android
- API: Not specified
5. **RealSoft**
- RealSoft by Coral is popular in Dubai, offering modules for contracting, real estate, job costing, manufacturing, trading, and finance. It's VAT-enabled and affordable for medium-sized businesses.
- **Details:**
- Suitable for: Small, Medium, Large Businesses
- Open Source: No
- Cloud-based: On-premises
-
Understanding Automated Testing Tools for Web Applications.pdfkalichargn70th171
Automated testing tools for web applications are revolutionizing how we ensure quality and performance in software development. These tools help save time, reduce human error, and increase the efficiency of web application testing processes. This guide delves into automated testing, discusses the available tools, and highlights how to choose the right tool for your needs.
Waze vs. Google Maps vs. Apple Maps, Who Else.pdfBen Ramedani
Let’s face it, getting lost isn’t really part of the adventure anymore (unless you’re into that sort of thing!). Nowadays, a good navigation app is like your trusty compass, guiding you through busy city streets and winding country roads. But with so many options out there—from big names like Waze, Google Maps, and Apple Maps to some lesser-known contenders—choosing the right one can feel a bit overwhelming.
Think about it: you're about to head out on a road trip, and the last thing you want is to end up in the middle of nowhere because you took a wrong turn. Or maybe you're just trying to navigate your daily commute without hitting every single red light. That's where a solid navigation app comes in handy.
Google Maps is like the old reliable friend who knows every shortcut and scenic route. It's packed with features, from real-time traffic updates to detailed directions, making it a top choice for many. But then there's Waze, the social butterfly of navigation apps. It's all about community, with drivers sharing real-time updates on traffic, accidents, and even speed traps. It’s perfect if you want to feel like you’re part of a huge driving club, all working together to get everyone to their destination faster.
And let’s not forget Apple Maps, which has come a long way since its rocky start. If you're deep into the Apple ecosystem, it's a seamless choice, integrating smoothly with all your devices and offering some pretty neat features like Flyover for 3D city views.
But wait, there are also some underdog apps worth considering! Have you heard of MapQuest? It's still around and offers some great features, especially for planning long trips with multiple stops. Then there's HERE WeGo, which is fantastic for offline navigation—a real lifesaver if you're heading somewhere with spotty cell service.
So, whether you're planning a cross-country adventure or just trying to find the quickest route to work, we’ll help you sift through these options. We’ll dive into what makes each app unique, their pros and cons, and ultimately, guide you to the perfect navigation app for your needs. Buckle up and get ready for a smooth ride!
Test Polarity: Detecting Positive and Negative Tests (FSE 2024)Andre Hora
Positive tests (aka, happy path tests) cover the expected behavior of the program, while negative tests (aka, unhappy path tests) check the unexpected behavior. Ideally, test suites should have both positive and negative tests to better protect against regressions. In practice, unfortunately, we cannot easily identify whether a test is positive or negative. A better understanding of whether a test suite is more positive or negative is fundamental to assessing the overall test suite capability in testing expected and unexpected behaviors. In this paper, we propose test polarity, an automated approach to detect positive and negative tests. Our approach runs/monitors the test suite and collects runtime data about the application execution to classify the test methods as positive or negative. In a first evaluation, test polarity correctly classified 117 tests as as positive or negative. Finally, we provide a preliminary empirical study to analyze the test polarity of 2,054 test methods from 12 real-world test suites of the Python Standard Library. We find that most of the analyzed test methods are negative (88%) and a minority is positive (12%). However, there is a large variation per project: while some libraries have an equivalent number of positive and negative tests, others have mostly negative ones.
Get to know Autonomous 2.0, the latest innovation from Applitools, in this sneak peek session showcasing how our AI-powered testing solutions revolutionize how you create, debug, and manage test scripts. See more and sign up for a free trial at https://applitools.info/ml6
Tube Magic Software | Youtube Software | Best AI Tool For Growing Youtube Cha...David D. Scott
Tube Magic Software is your ultimate tool for creating stunning video content with ease. Designed with both beginners and professionals in mind, it offers a user-friendly interface packed with powerful features. From seamless editing to eye-catching effects, Tube Magic helps you bring your creative vision to life. Elevate your videos and captivate your audience effortlessly. Join our community of content creators and experience the magic today!
Fix Production Bugs Quickly - The Power of Structured Logging in Ruby on Rail...John Gallagher
Rails apps can be a black box. Have you ever tried to fix a bug where you just can’t understand what’s going on? This talk will give you practical steps to improve the observability of your Rails app, taking the time to understand and fix defects from hours or days to minutes. Rails 8 will bring an exciting new feature: built-in structured logging. This talk will delve into the transformative impact of structured logging on fixing bugs and saving engineers time. Structured logging, as a cornerstone of observability, offers a powerful way to handle logs compared to traditional text-based logs. This session will guide you through the nuances of structured logging in Rails, demonstrating how it can be used to gain better insights into your application’s behavior. This talk will be a practical, technical deep dive into how to make structured logging work with an existing Rails app.
I talk about the Steps to Observable Software - a practical five step process for improving the observability of your Rails app.
What is Micro Frontends and Why Use it.pdflead93317
🚀 Let's Deep Dive into 𝐖𝐡𝐲 𝐌𝐢𝐜𝐫𝐨 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 🚀
In today's fast-paced tech landscape, agility, scalability, and maintainability are more crucial than ever. Traditional monolithic frontend architectures often struggle to keep up with these demands. Enter Micro Frontends: a revolutionary approach that's transforming the way we build web applications.
Crowd Strike\Windows Update Issue: Overview and Current Statusramaganesan0504
Crowd Strike\Windows Update Issue: Overview and Current Status
Discover the latest on the CrowdStrike Windows update issue, including an overview, current status, and support steps for affected customers. Learn about the identified defect, its impact on Windows hosts, and CrowdStrike's committed actions to ensure ongoing security and stability.
What is CrowdStrike?
CrowdStrike is a prominent cybersecurity technology company that specializes in providing advanced threat intelligence and endpoint protection solutions. Founded in 2011 by George Kurtz, Dmitri Alperovitch, and Gregg Marston, CrowdStrike has quickly established itself as a leader in the cybersecurity industry. Here are some key aspects of
Crowd Strike\Windows Update Issue: Overview and Current Status
QuerySurge Slide Deck for Big Data Testing Webinar
1. Bill Hayduk
CEO, RTTS
Business Leader, QuerySurge
(the software division of RTTS)
Testing Big Data:
Automated ETL Testing of Hadoop and NoSQL
Jeff Bocarsly, Ph.D.
Chief Architect
QuerySurge Division, RTTS
2. built by
QuerySurge™
• About Big Data and Hadoop
• About NoSQL
• Hadoop and DWH Use Case
• How to test Big Data
• Demo of QuerySurge
w/ Hadoop and NoSQL
AGENDA
Testing Big Data:
Automated ETL Testing of
Hadoop and NoSQL
Host: RTTS/QuerySurge
Date: July 30, 2022
Time: 1:00 pm, Eastern
Standard Time
(New York, GMT-05:00)
Session number:
630 771 732
4. Regional Consulting firms
Technology Partners Global System Integrators
Argentina, Australia, Belgium, Brazil, Canada, Chile, India,
Malaysia, Netherlands, New Zealand, Norway, Sweden,
Singapore, South Africa, Ukraine, US
5. Data Warehouse
Data Warehouse
ETL
ETL
Mainframe
Business Intelligence
& Analytics
C-level executives are using BI &
Analytics to make critical
business decisions with the
assumption that the underlying
data is fine
We know it is not
ETL
Typical data
issue areas
6. Big data – defined as too much
volume, velocity and variety to
work on normal database
architectures.
Size
Defined as 5 petabytes or more
1 petabyte = 1,000 terabytes
1,000 terabytes = 1,000,000 gigabytes
1,000,000 gigabytes = 1,000,000,000 megabytes
built by
built by
QuerySurge™
7. Handles more than 1 million customer transactions every hour.
• data imported into databases that contain > 2.5 petabytes of data
• the equivalent of 167 times the information contained in all the books in the US Library of
Congress.
Facebook handles 40 billion photos from its user base.
Google processes 1 Terabyte per hour
Twitter processes 85 million tweets per day
eBay processes 80 Terabytes per day
others
built by
QuerySurge™
8. Requires exceptional technologies to efficiently process large quantities of
data within tolerable elapsed times.
Technologies include:
• massively parallel processing (MPP) databases
• data warehouses
• Data mining grids
• distributed file systems
• distributed databases
• cloud computing platforms
• the Internet, and
• scalable storage system
built by
QuerySurge™
9. built by
QuerySurge™
• easily deals with complexities of high of data
Hadoop is an open-source project that
develops software for scalable, distributed computing.
• is a of large data sets across
clusters of computers using simple programming models.
from single servers to 1,000’s of machines, each offering local
computation and storage.
• detects and at the application layer
10. built by
QuerySurge™
• Redundant and reliable
• Extremely powerful
• Easy to program distributed apps
• Runs on commodity hardware
11. built by
QuerySurge™
“Spending on Hadoop software and subscriptions will increase to
approximately $677 million, with overall big data market
anticipated to reach the $50 billion mark.”
- Wikibon
13. built by
QuerySurge™
Cluster
Add more machines for scaling – from 1 to 100 to 1,000
Job Tracker accepts jobs, assigns tasks, identifies failed machines
Name Node
Coordination for HDFS. Inserts and extraction are communicated through the Name Node.
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Task
Tracker
Data
Node
Name Node
15. What is NoSQL?
A term used to describe high-performance, non-relational databases that provide a mechanism for
storage and retrieval of data that is modeled in means other than the tabular relations used in
relational databases
NoSQL Database Types
Document databases pair each key with a complex data structure known as a document.
Documents can contain many different key-value pairs, or key-array pairs, or even nested documents.
Graph stores are used to store information about networks of data, such as social connections.
Graph stores include Neo4J and Giraph.
Key-value stores are the simplest NoSQL databases. Every single item in the database is stored as
an attribute name (or 'key'), together with its value. Examples of key-value stores are Riak and
Berkeley DB. Some key-value stores, such as Redis, allow each value to have a type, such as 'integer',
which adds functionality.
Wide-column stores such as Cassandra and HBase are optimized for queries over large datasets,
and store columns of data together, instead of rows.
a software division of
QuerySurge™
18. built by
™
• Online real-time processing
• Data set is smaller
• Measured in milliseconds
• Offline big data processing
• Offline analytics
• Measured in minutes & hours
Source: classpattern.com
When to use NoSQL? / When to use Hadoop?
21. built by
QuerySurge™
USE CASE 1***
Use Hadoop as a landing zone for big data & raw data
1) bring all raw, big data into Hadoop
2) perform some pre-processing of this data
3) determine which data goes to Data Warehouse
4) Extract, transform and load (ETL) pertinent data into Data Warehouse
***Source: Vijay Ramaiah, IBM product manager, datanami magazine, June 10, 2013
built by
QuerySurge™
22. Recommended functional test strategy: Test every entry point in the system
(feeds, databases, internal messaging, front-end transactions).
The goal: provide rapid localization of data issues between points
test entry point
built by
Business
Intelligence
software
ETL
Source Data
Source Hadoop ETL Process Target DWH
built by
QuerySurge™
test entry point
test entry points
23. Relational DB & Data
Warehousing
Source Data
@
BI, Analytics &
Reporting
Ingestion
built by
™
test entry point
test entry point
test entry point
test entry point test entry point
24. built by
QuerySurge™
- we need to verify more data and to do it faster
- we need to automate the testing
effort
- We need to be able to test across different platforms
We need a testing tool!
26. built by
QuerySurge™
QuerySurge
is the smart Data Testing solution
that automates
the data validation and ETL testing
of Big Data
with full DevOps functionality
for continuous testing
built by
27. a software division of
QuerySurge™
Data Quality at Speed
→ Automate the launch, execution, comparison & auto-email results
Test across different platforms
→ Data Warehouse, Hadoop, NoSQL, DB, flat files, XML, JSON, BI Reports
Smart Query Wizards - no coding needed
→ Query Wizards create tests visually, without writing SQL
Data Analytics & Data Intelligence
→ Data Analytics Dashboard, Data Intelligence Reports, emailed results,
Ready-for-Analytics back-end data access
Create Custom Tests
→ Modularize functions with snippets, set thresholds, stage data, check data types
DevOps for Data & Continuous Testing
→ API Integration with Build/Release, Continuous Integration/ETL ,
Operations/DevOps Monitoring, Test Management/Issue Tracking, more
Projects
→ Multi-project support, global admin user, activity log reports
28. Web-based…
Supported OS...
Connects through…
…to any JDBC compliant data source
QuerySurge™
QuerySurge
Controller
QuerySurge Server
DB Server (MySQL)
App Server (Tomcat)
QuerySurge Agents
(Ships with 10 Agents)
a software division of
Installs...
…in the Cloud
…on a VM
…on a Bare Metal Server
30. QuerySurge™ a software division of
Multi-Project Support
Multiple projects can now be created in a single QuerySurge instance. This allows for multiple groups to
work on the same QuerySurge server without seeing each other’s assets (project-level security).
Features supported in Multi-Projects are:
• Global Admin User: This new user type administers the QuerySurge instance
across multiple projects.
• Assign Users to Projects: Users can be assigned to one or more projects. In
each assignment, a user can have a different project role (administrator,
standard user or participant user).
• Assign Agents to Projects: Agents can be shared across projects or dedicated
to specific projects.
• Project Import: Import project data into another project on the same instance
or into a different environment (Dev/QA/Prod).
• Project Export: Export entire projects and store for backup purposes.
• Activity Log Reports: Two reports that track specific changes for auditing
purposes, including manipulations to users or connections.
31. Fast and Easy.
No programming needed.
QuerySurge™
• Perform 80% of all data tests with no SQL coding
• Opens up testing to novices & non-technical members
• Speeds up testing for skilled coders
• provides a huge Return-On-Investment
a software division of
33. Design Library
• Create custom Query Pairs (source & target
SQLs for tests that have transformations)
Scheduling
Build groups of Query Pairs
Schedule Test Runs
• Run immediately
• Run at set date/time
• Have event kick it off
™
a software division of
34. Deep-Dive Reporting
Examine and automatically
email test results
Run Dashboard
View real-time execution
Analyze real-time results
™
a software division of
35. a software division of
QuerySurge™
QuerySurge DevOps for Data
• First full DevOps for Data testing solution
• Both RESTful and command line APIs
• Improves Data Quality at Speed
QuerySurge DevOps for Data 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
60+ API calls with almost 100 different properties
that users can utilize to retrieve, edit, update, or
delete information.
36. QuerySurge™
• view data reliability & pass rate
• add, move, filter, zoom-in on any
data widget & underlying data
• verify build success or failure
a software division of
37. Large Suite March 5, 2021 16:20:44 March 5, 2021
March 5, 2021 4:24 PM
Start Time
QuerySurge™
6 minutes
38. (1) Trial in the Cloud of QuerySurgeTM, including self-learning
tutorial that works with sample data for 3 days
(2) Downloaded Trial of QuerySurgeTM, including self-learning
tutorial with sample data or your data for 15 days
for more information on our Trials, please visit:
www.querysurge.com/compare-trial-options
TRIAL
IN THE CLOUD
built by
QuerySurge™
http://www.rttsweb.com/training/courses/big-data-testing-courses
Big Data Testing Courses
Filled with examples and labs, this hands-on training teaches concepts
and HQL techniques used in Big Data testing.
For more information on our Big Data Testing classes, please visit:
39. built by
built by
QuerySurge™
To see the video of our Big Data testing webinar please visit:
http://www.querysurge.com/solutions/testing-big-data/big-data-testing-for-hadoop
Big Data is on the verge of revolutionizing enterprise data
management architectures.
- DeZyre