File system used with Hadoop

- [Instructor] Let's talk a little bit more about modern file systems for Hadoop. Core HDFS has not changed much. Now with some of the newer Apache distributions, the time of the recording of my Hadoop fundamentals course, we were on Apache distribution number 2.5 and the most current as of this recording is 2.7. There have been some improvements to the core distribution around enterprise needs such as encryption. However, the core remains pretty much similar and it's used for the types of batch and extract transformation and load jobs that it traditionally has been.

What's new on the file systems for Hadoop is this idea of cloud-based file systems. So for Amazon, that's S3. For Google, that's Google Cloud Storage. And for Microsoft Azure, that's Blob storage. This is sometimes called a data lake, and this is really changing the landscape of Hadoop because it's decoupling the need to take data that you pull in to a file location and then transport it or move it over to HDFS. What it does is it enables more use of Hadoop because there's less steps in the processes and because the cloud-based file systems are cheaper to store massive amounts of information than using HDFS, either locally or on the cloud.

So this is a really important change in the world of Hadoop and I see more and more customers being interested in applying Hadoop style processes or MapReduces or some of these new processes like Spark Jobs to files that they traditionally would just have a S3 and they would use some other mechanism for querying and looking at them. Examples of these files would be log files from all their nodes of their networks globally. Another example would be event files, from IoT devices. Another example would medical information.

In addition to the cloud-based file systems, there are certain commercial vendors and we're going to be focusing on Databricks, who I think is really leading the industry now, that are creating enhancements to the HDFS file system and offering alternatives. Their file system is the Databricks file system, and we'll be working with that in this course as well.