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With this Hadoop tutorial, you'll not only understand what those systems are and how they fit together - but you'll go hands-on and learn how to use them to solve real business problems!
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| The Ultimate Hands-On Hadoop - Tame your Big Data! |
Learn and master the most popular big data technologies in this comprehensive course, taught by a former engineer and senior manager from Amazon and IMDb. We'll go way beyond Hadoop itself, and dive into all sorts of distributed systems you may need to integrate with.
- Install and work with a real Hadoop installation right on your desktop with Hortonworks (now part of Cloudera) and the Ambari UI
- Manage big data on a cluster with HDFS and MapReduce
- Write programs to analyze data on Hadoop with Pig and Spark
- Store and query your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix, and Presto
- Design real-world systems using the Hadoop ecosystem
- Learn how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin, and Hue
- Handle streaming data in real time with Kafka, Flume, Spark Streaming, Flink, and Storm
Understanding Hadoop is a highly valuable skill for anyone working at companies with large amounts of data.
Almost every large company you might want to work at uses Hadoop in some way, including Amazon, Ebay, Facebook, Google, LinkedIn, IBM, Spotify, Twitter, and Yahoo! And it's not just technology companies that need Hadoop; even the New York Times uses Hadoop for processing images.
This course is comprehensive, covering over 25 different technologies in over 14 hours of video lectures. It's filled with hands-on activities and exercises, so you get some real experience in using Hadoop - it's not just theory.
You'll find a range of activities in this course for people at every level. If you're a project manager who just wants to learn the buzzwords, there are web UI's for many of the activities in the course that require no programming knowledge. If you're comfortable with command lines, we'll show you how to work with them too. And if you're a programmer, I'll challenge you with writing real scripts on a Hadoop system using Scala, Pig Latin, and Python.
You'll walk away from this course with a real, deep understanding of Hadoop and its associated distributed systems, and you can apply Hadoop to real-world problems. Plus a valuable completion certificate is waiting for you at the end!
Please note the focus on this course is on application development, not Hadoop administration. Although you will pick up some administration skills along the way.
Knowing how to wrangle "big data" is an incredibly valuable skill for today's top tech employers. Don't be left behind - enroll now!
- "The Ultimate Hands-On Hadoop... was a crucial discovery for me. I supplemented your course with a bunch of literature and conferences until I managed to land an interview. I can proudly say that I landed a job as a Big Data Engineer around a year after I started your course. Thanks so much for all the great content you have generated and the crystal clear explanations. " - Aldo Serrano
- "I honestly wouldn’t be where I am now without this course. Frank makes the complex simple by helping you through the process every step of the way. Highly recommended and worth your time especially the Spark environment. This course helped me achieve a far greater understanding of the environment and its capabilities. Frank makes the complex simple by helping you through the process every step of the way. Highly recommended and worth your time especially the Spark environment." - Tyler Buck
Who this course is for:
- Software engineers and programmers who want to understand the larger Hadoop ecosystem, and use it to store, analyze, and vend "big data" at scale.
- Project, program, or product managers who want to understand the lingo and high-level architecture of Hadoop.
- Data analysts and database administrators who are curious about Hadoop and how it relates to their work.
- System architects who need to understand the components available in the Hadoop ecosystem, and how they fit together.
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