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When to Use MapReduce with Big Data MapReduce is a programming model for distributed computation on big data sets in parallel. It's a module in the Apache Hadoop open source ecosystem, and a range of queries may be done based on the algorithms available. Here's when it's suitable (and not suitable) to use MapReduce for generating and processing data. |
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Trusting Your Data: Garbage In, Garbage Out Poor quality input will always produce faulty output. Improper validation of data input can affect more than just security; it can also affect your ability to make effective business decisions. Bad data can have impacts on how you make quantitative decisions or create reports, if you can’t trust the data you receive. |
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Before Data Analysis, You Need Data Preparation One of the prerequisites for any type of analytics in data science is data preparation. Raw data usually has several shortcomings in structure, format, and consistency, so first it has to be converted to a usable form. These are some types of data preparation you can conduct to make your data useful for analysis. |
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Getting Support for the Tests You Need Done It’s often hard for teams to get sufficient time and resources for the amount and quality of tests they think are needed. It’s like management wants testing done but at the same time doesn’t want to commit what’s needed to do it. If that's your case, look at the business side, rank priorities, and negotiate resources. |
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Selecting the Right Node.js Framework for Your App Node.js is an open source and cross-platform runtime environment for creating server-side web apps entirely using JavaScript. There are many frameworks that work with Node.js and each excels in different areas, so selecting one comes down to preference and the specific needs of the project. Here are some popular ones. |
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Exploring Big Data Options in the Apache Hadoop Ecosystem With the emergence of the World Wide Web came the need to manage large, web-scale quantities of data, or “big data.” The most notable tool to manage big data has been Apache Hadoop. Let’s explore some of the open source Apache projects in the Hadoop ecosystem, including what they're used for and how they interact. |
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7 Essential Quality Attributes for Your Test Automation Framework A common problem in software is that developers and designers tend to concentrate on pure functionality and neglect quality attributes. These are the famous “-abilities”: usability, reliability, portability, etc. If your testing framework is suffering, you might want to check if it has these seven quality attributes. |
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Designing Data Models for Self-Documented Tests When testing applications, documenting and interpreting test results can be a challenge. Data models enable us to collect and process test data more dynamically and uniformly. To design effective data models for self-documented tests, there are three important things to consider: what to document, collect, and report. |
