Many teams use logs to track the behavior of applications, services, or platforms. But how actionable is that log data? There are better ways to parse that information and make it more visible, more useful, and easier to understand. Try converting your logs into metrics for a faster and more lightweight monitoring system.

Apache Hadoop is a framework for big data. One of its main components is HDFS, Hadoop Distributed File System, which stores that data. You might expect that a storage framework that holds large quantities of data requires state-of-the-art infrastructure for a file system that does not fail, but quite the contrary is true.

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.