Data Transformations with Apache Pig

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on performing data transformations using Apache Pig. I'll introduce myself first. I have a master's degree in electrical engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. Pig is an open source engine, which is part of the Hadoop ecosystem of technologies. Pig is great at working with data which are beyond traditional databases or data warehouses. Pig can deal well with missing, incomplete, or inconsistent data, which has no schema. Pig has its own language for expressing data manipulations, which is Pig Latin. This course starts from the very basics. It gives you an overview of Pig, shows you how to get Pig installed and running on your system, and gets you started working with the Grunt shell. You will see how you can load data into relations in Pig, store transformed relations to files via load and store commands. The main focus of the course is on how the data can be transformed to make it more useful for analysis. This course will cover the foreach generate command, along with a range of evaluation and filter functions. You'll also work on a real world dataset that you analyze accidents in New York City using collision data from the city of New York website. And finally, we'll cover advanced constructs such as the nested foreach, and also get a brief glimpse into the world of MapReduce, the parallel programming model which powers Hadoop.