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Making Big Data Agile Big data is defined as web-scale, large quantities of data ranging to several TeraBytes (TB) or PetaBytes (PB). Big data is inherently difficult to manage due to its sheer size and free format, which is best summarized by the three Vs (volume, velocity, and variety).
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The Value of Making Your Data Sources Reusable across Test Automation Tools Many automation tools have a mechanism for storing data used in their test scripts. Typically, the specifics of this mechanism is different across tools, making it difficult to use this data outside the tool itself. Using an external, reusable data source allows organizations to avoid the cost of migrating or duplicating existing data, thereby future-proofing their frameworks.
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Introducing the DevOps Database Gap Yaniv Yehuda details how DevOps is a natural evolution within the software industry as it drives business value and enables the organization. This article will describe how database management and the database administrators need to be part of any comprehensive DevOps approach.
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A Visualization of Your Data is Worth a Thousand Words Today I watched an old TED Talk by Dr. Hans Rosling entitled "Debunking third-world myths with the best stats you've ever seen." If you haven't seen it yet, I recommend taking a moment to watch it - I've never seen statistics presented in such an engaging and entertaining fashion. In this talk, Dr. Rosling uses his fantastic visualization software to demonstrate the changing relation between the wealth and health of nations over several decades.
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Attacking Quality Issues in Data Warehousing To fully detect, isolate, and resolve quality issues in a traditional, large-scale data warehouse requires that several approaches be used together. Wayne identifies types of data quality issues and then illustrates how to best attack and resolve those pesky issues.
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Demystifying Big Data: An Interview with Manish Arora In this interview, Manish Arora demystifies big data by covering some of the biggest misperceptions and pain points held by businesses and SMEs. Arora also talks about his recent article featured on LinkedIn and why it's important to put good teams and technology into proper perspective.
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Big Data Migration to the Cloud: Testing Challenges and Strategies
Slideshow
Moving to the cloud is no longer a question of if, but when. Most corporations are either underway in their cloud adoption or have it on their radar.
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Sanjay Srinivas
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Data Curation: Refine and Shine
Slideshow
We now live in a world where data is generated with every action taken. From buying groceries to walking the dog, we're generating data all the time, everywhere.
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Michael Hobbs
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Machine Data Is EVERYWHERE: Use It for Testing
Slideshow
As more applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are...
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Tom Chavez
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Leverage Streaming Data in a Microservices Ecosystem
Slideshow
Imagine a world where operational data is continuously flowing from applications and devices at an extremely high rate. Now imagine services intercepting this data and analyzing it real time. Sounds futuristic? It's not—it's here today. Mark Richards describes what streaming architecture...
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Mark Richards
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