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Welcome to the Effective Data Pipelines Series

This page provides many of the resources for books, videos and on-line trainings.

You can find more information on all current video and book titles and upcoming on-line trainings from O'Reilly.

Training Descriptions

Many of the trainings are run on a regular basis. Check the O'Reilly site for upcoming live events. Where possible, trainings are demonstrated using the freely available virtual machine. To facilitate continued exploration using the virtual machine, training notes (text files) available below.

  • Apache Hadoop, Spark, and Kafka Foundations - (POPULAR) 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 training in the Effective Data Pipelines Series (3 hours-1 day)
  • Bash Programming Quick-start for Data Science - Quickly learn the essentials of using the Linux command line for Data Science at scale. Download/upload files, run applications, monitor resources, edit files, write scripts, 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. (4 hours-1 day)
  • Hands-on Introduction to Apache Hadoop, Spark, and Kafka Programming - (POPULAR) 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)
  • Getting Started with Kafka - (POPULAR) Apache Kafka is designed to manage data flow by decoupling the data source from the destination. Kafka can provide a robust data buffer or broker that can help create and manage data pipelines. In this training, the basic Kafka data broker design and operation is explained and illustrated using both the command line and a GUI.
  • Kafka Methods and Administration - Additional Kafka features that go beyond those presented in Getting Started with Kafka will be addressed. These topics include writing to databases and HDFS, producer and consumer options, working with Kafka Connect, and Kafka installation and administration.
  • Up and Running with Kubernetes - Kubernetes can be considered a container operating system where application resource and storage needs are matched to an underlying cluster environment (either virtual or real). This course provides both background for users coupled with a practical hands-on introduction to Kubernetes.
  • Data Engineering at Scale with Apache Hadoop and Spark - As part of the Effective Data Pipelines series, this training 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)
  • Scalable Analytics with Apache Hadoop, Spark, and Kafka - A complete data science investigation requires different tools and strategies. In this training, 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)

About the Presenter

Douglas Eadline, began his career as Analytical Chemist with an interest in computer methods. Starting with the first Linux Cluster Beowulf How-to document, Doug has written instructional documents covering many aspects of Linux High Performance Computing (HPC) and scalable data analytics computing. Currently, Doug serves as editor of the 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). Doug also designs high performance desk-side clusters for both HPC and data analytics.

In addition to the on-line trainings, Doug also teaches graduate level courses as part of two Masters in Data science programs:

These classes include more in-depth treatment and practical application of the scalable computing tools and examples I cover in the one-day trainings. Consider enrolling in one of these excellent Data Science programs.

Contact: deadline(you know what goes here)eadline(and here)org
Twitter: @thedeadline

Class Notes for Bash Programming Quick-start

(Updated 19-Apr-2023)

Class Notes for Hands-on Introduction to Apache Hadoop and Spark Programming

(Updated 13-Oct-2021)

Class Notes for Data Engineering at Scale with Apache Hadoop and Spark

(Updated 17-Dec-2020)

Class Notes for Up and Running with Kubernetes

(Updated 07-Sep-2022)

Class Notes for Getting Started with Kafka

(Updated 09-Aug-2022 - fixes typos)

Class Notes for Kafka Methods and Administration

(Update 20-Mar-2023 - some code and typo fixes)

Class Notes for Scalable PySpark for Data Science

(Update 07-Jan-2024)

Old Notes

Zeppelin Notebook for Scalable Data Science with Hadoop and Spark

(Updated 15-Sep-2021)

Supporting Documents (Cheat Sheets)

Linux Hadoop Minimal (LHM) Virtual Machine Sandbox

Used for Hands-on, Command Line, and Scalable Data Science trainings above. Note: This VM can also be used for the Hadoop and Spark Fundamentals: LiveLessons video mentioned below.

VERSION 2-8.1: (Current)

(Updated Jan-25-2024) CentOS Linux 7.6, Anaconda 3:Python 3.7.4, R 3.6.0, Hadoop 3.3.0, Hive 3.1.2, Apache Spark 2.4.5, Derby, Zeppelin 0.8.2, Sqoop 1.4.7, Kafka 2.5.0, HBase 2.4.10, NiFi 1.17.0, KafkaEsque. Used in all current trainings.

Linux Hadoop Minimal Installation Instructions VERSION 2 (Read First)

For VirtualBox X86 PC, Mac, Linux Machines
For UTM Apple Mac M Machines

Cloudera-Hortonworks HDP Sandbox

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.

Other Resources for all Classes


For further questions or help with the Linux Hadoop Minimal Virtual Machine please email: deadline(you know what goes here)eadline(and here)org

Unless otherwise noted, all training content, notes, and examples © Douglas Eadline 2019-2024 All rights reserved.

start.txt · Last modified: 2024/01/29 21:19 by deadline