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5 Pitfalls to Avoid When Developing AI Tools Developing a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of. |
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Code Integration: When Moving Slowly Actually Has More Risk Many decisions about code branching models are made in the name of managing risk, and teams sometimes pick models that make integration harder in the name of safety. Moving slowly and placing barriers to change can seem safer, but agile teams work best when they acknowledge that there is also risk in deferring change. |
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Benefits of Using Columnar Storage in Relational Database Management Systems Relational database management systems (RDBMS) store data in rows and columns. Most relational databases store data row-wise by default, but a few RDBMS provide the option to store data column-wise, which is a useful feature. Let’s look at the benefits of being able to use columnar storage for data and when you'd want to. |
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Comparing Apache Sqoop, Flume, and Kafka Apache Sqoop, Flume, and Kafka are tools used in data science. All three are open source, distributed platforms designed to move data and operate on unstructured data. Each also supports big data in the scale of petabytes and exabytes, and all are written in Java. But there are some differences between these platforms. |
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Fearless Refactoring, Not Reckless Refactoring Fearless refactoring is the agile concept that a developer should be able to incrementally change code without worrying about breaking it. But it's not believing that you don't need a safety net to detect and correct defects quickly when changes are made—that's just reckless. Here's how to avoid reckless refactoring. |
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Defensive Design Strategies to Prevent Flaky Tests Flaky tests could be the result of issues in the code, but more often they are due to assumptions in the test code that lead to non-relatable results. There are many reasons that tests can fail intermittently, and some can be easily avoided by applying good defensive design strategies. It's all about making your code agile. |
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Comparing XML and JSON: What’s the Difference? XML (Extensible Markup Language) and JSON (JavaScript Object Notation) are the two most common formats for data interchange. Although either can be used to receive data from a web server, there are differences that set them apart. Here are the abilities and support for each option so you can choose what works for you. |
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Should You Use XML or Protocol Buffers to Store and Exchange Data? XML is a flexible text format used for a wide variety of applications, including data serialization and exchange of data. More recently, protocol buffers were also introduced for data exchange and data serialization. Even though their purpose is the same, these are very different technologies. Which is better for you? |
