Articles
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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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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. |
Better Software Magazine Articles
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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. |
Interviews
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Improving the Efficiency of Your Metrics: An Interview with Mike Trites
Podcast
In this interview, Mike Trites, a senior test consultant, talks about his upcoming presentation at STAREAST 2014, the future of metrics, the importance of improving the efficiency of your metrics, and even an interesting take on the old phrase that numbers never lie. |
Conference Presentations
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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. |
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. |
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... |
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... |
Mark Richards
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