(previously Scalable Analytics with Apache Hadoop and Spark)
The six essential courses on the path to scalable data science pipelines nirvana–or at least a good start
Click on the course name for availability and further information. New courses are being added. For best results, courses should be taken in the recommended order (shown below). Courses 1 and (2&3) can be taken out of order. Course 4 builds on courses 1 and (2&3). Course 5 builds-on and assumes competence with topics in courses 4, (3&2), and 1. Finally, course 6 requires understanding of tools and topics in course 1-5.
NOTE: If the link does not lead you to the class, it has not yet been scheduled.
|1||Apache Hadoop, Spark, and Kafka Foundations: Effective Data Pipelines - A great introduction to the Hadoop Big Data Ecosystem with Spark and Kafka. A non-programming introduction to Hadoop, Spark, HDFS, MapReduce, and Kafka. After completing the workshop attendees will gain a workable understanding of the Hadoop/Spark/Kafka technical value proposition and provide a solid background for following courses in the Effective Data Pipelines Series (3 hours-1 day)|
|2||Beginning Linux Command Line for Data Engineers and Analysts: Effective Data Pipelines - Quickly learn the essentials of using the Linux command line on Hadoop/Spark clusters. Download/upload files, run applications, monitor resources, and navigate the Linux command line interface used on almost all modern analytics clusters. Students can download and run examples on the “Linux Hadoop Minimal” virtual machine, see below. (3 hours-1 day)|
|3||Intermediate Linux Command Line for Data Engineers and Analysts: Effective Data Pipelines - This course is a continuation of Beginning Linux Command Line for Data Engineers and Analysts covering more advanced topics. Coverage includes: Linux Analytics, Moving Data into Hadoop HDFS, Running Command Line Analytics Tools, Bash Scripting Basics, and Creating Bash Scripts|
|4||Hands-on Introduction to Apache Hadoop, Spark, and Kafka Programming - A hands-on introduction to using Hadoop, Hive, Sqoop, Spark, Kafka and Zeppelin notebooks. Students can download and run examples on the “Linux Hadoop Minimal” virtual machine, see below. (6 hours-2 days)|
|5||Data Engineering at Scale with Apache Hadoop and Spark - As part of the Effective Data Pipelines series, this course provides background and examples on data “munging” or transforming raw data into a form that can be used with analytical modeling libraries. Also referred to as data wrangling, transformation, or ETL these techniques are often performed “at scale” on a real cluster using Hadoop and Spark.(3 hours-1 days)|
|6||Scalable Analytics with Apache Hadoop, Spark, and Kafka - A complete data science investigation requires different tools and strategies. In this course, learn How to apply Hadoop, Spark, and Kafka tools to Predict Airline Delays. All programming will be done using Hadoop, Spark, and Kafka with the Zeppelin web notebook on a four node cluster. The notebook will be made available for download so student can reproduce the examples. (3 hours-1 day)|
Douglas Eadline, began his career as Analytical Chemist with an interest in computer methods. Starting with the first Linux Beowulf How-to document, Doug has written instructional documents covering many aspects of Linux HPC, Hadoop, and Analytics computing. Currently, Doug serves as editor of the ClusterMonkey.net website and was previously editor of ClusterWorld Magazine, and senior HPC Editor for Linux Magazine. He is also a writer and consultant to the scalable HPC/Analytics industry. His recent video tutorials and books include of the Hadoop Fundamentals LiveLessons (Addison Wesley) video, Hadoop 2 Quick Start Guide (Addison Wesley), High Performance Computing for Dummies (Wiley) and Practical Data Science with Hadoop and Spark (Co-author, Addison Wesley).
(Current Version 0.42, 03-June-2019) Not ready for Scalable Data Science with Hadoop and Spark (soon)
Used for Hands-on, Command Line, and Scalable Data Science courses above. Note: This VM can also be used for the Hadoop and Spark Fundamentals: LiveLessons video mentioned below.
The Cloudera-Hortonworks HDP Sandbox, a full featured Hadoop/Spark virtual machine that runs under Docker, VirtualBox, or VMWare. Please see Cloudera/Hortonworks HDP Sandbox for more information. Due to the number of applications the HDP Sandbox can require substantial resources to run.
For further questions or help with the Linux Hadoop Minimal Virtual Machine please email d...@b...g.com
Unless otherwise noted, all course content, notes, and examples © Copyright Basement Supercomputing 2019, All rights reserved.