For example, the IPython kernel executes Python code in a notebook. The book also covers Spark and explains how it interacts with other tools.īy the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.Ī kernel, in Jupyter parlance, is a computation engine that runs the code that is typed into a code cell in a notebook. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. You'll then get familiar with statistical analysis and plotting techniques. The book begins with an introduction to data manipulation in Python using pandas. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |