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Big data is hot, and data management and analytics skills are your ticket to a fast-growing, lucrative career. This course will quickly teach you two technologies fundamental to big data: MapReduce and Hadoop. Learn and master the art of framing data analysis problems as MapReduce problems with over 10 hands-on examples. Write, analyze, and run real code along with the instructor– both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. By course’s end, you’ll have a solid grasp of data management concepts.

  • Learn the concepts of MapReduce to analyze big sets of data w/ 56 lectures & 5.5 hours of content
  • Run MapReduce jobs quickly using Python & MRJob
  • Translate complex analysis problems into multi-stage MapReduce jobs
  • Scale up to larger data sets using Amazon’s Elastic MapReduce service
  • Understand how Hadoop distributes MapReduce across computing clusters
  • Complete projects to get hands-on experience: analyze social media data, movie ratings & more
  • Learn about other Hadoop technologies, like Hive, Pig & Spark

Hadoop is perhaps the most important big data framework in existence, used by major data-driven companies around the globe. Hadoop and its associated technologies allow companies to manage huge amounts of data and make business decisions based on analytics surrounding that data. This course will take you from big data zero to hero, teaching you how to build Hadoop solutions that will solve real world problems – and qualify you for many high-paying jobs.

  • Access 43 lectures & 10 hours of content 24/7
  • Learn how technologies like Mapreduce apply to clustering problems
  • Parse a Twitter stream Python, extract keywords w/ Apache Pig, visualize data w/ NodeJS, & more
  • Set up a Kafka stream w/ Java code for producers & consumers
  • Explore real-world applications by building a relational schema for a health care data dictionary used by the US Department of Veterans Affairs
  • Log collections & analytics w/ the Hadoop distributed file system using Apache Flume & Apache HCatalog

Have you ever wondered how major companies, universities, and organizations manage and process all the data they’ve collected over time? Well, the answer is Big Data, and people who can work with it are in huge demand. In this course you’ll cover the MapReduce algorithm and its most popular implementation, Apache Hadoop. Throughout this comprehensive course, you’ll learn essential Big Data terminology, MapReduce concepts, advanced Hadoop development, and gain a complete understanding of the Hadoop ecosystem so you can become a big time IT professional.

  • Access 76 lectures & 15.5 hours of content 24/7
  • Learn how to setup Node Hadoop pseudo clusters
  • Understand & work w/ the architecture of clusters
  • Run multi-node clusters on Amazon’s Elastic Map Reduce (EMR)
  • Master distributed file systems & operations including running Hadoop on HortonWorks Sandbok & Cloudera
  • Use MapReduce w/ Hive & Pig
  • Discover data mining & filtering
  • Learn the differences between Hadoop Distributed File System vs. Google File System

Hadoop is one of the most commonly used Big Data frameworks, supporting the processing of large data sets in a distributed computing environment. This tool is becoming more and more essential to big business as the world becomes more data-driven. In this introduction, you’ll cover the individual components of Hadoop in detail and get a higher level picture of how they interact with one another. It’s an excellent first step towards mastering Big Data processes.

  • Access 30 lectures & 5 hours of content 24/7
  • Install Hadoop in Standalone, Pseudo-Distributed, & Fully Distributed mode
  • Set up a Hadoop cluster using Linux VMs
  • Build a cloud Hadoop cluster on AWS w/ Cloudera Manager
  • Understand HDFS, MapReduce, & YARN & their interactions

Take your Hadoop skills to a whole new level by exploring its features for controlling and customizing MapReduce to a very granular level. Covering advanced topics like building inverted indexes for search engines, generating bigrams, combining multiple jobs, and much more, this course will push your skills towards a professional level.

  • Access 24 lectures & 4.5 hours of content 24/7
  • Cover advanced MapReduce topics like mapper, reducer, sort/merge, partitioning, & more
  • Use MapReduce to build an inverted index for search engines & generate bigrams from text
  • Chain multiple MapReduce jobs together
  • Write your own customized partitioner
  • Sort a large amount of data by sampling input files

Analyzing data is an essential to making informed business decisions, and most data analysts use SQL queries to get the answers they’re looking for. In this course, you’ll learn how to map constructs in SQL to corresponding design patterns for MapReduce jobs, allowing you to understand how these two programs can be leveraged together to simplify data problems.

  • Access 49 lectures & 1.5 hours of content 24/7
  • Master the art of “thinking parallel” to break tasks into MapReduce transformations
  • Use Hadoop & MapReduce to implement a SQL query like operations
  • Work through SQL constructs such as select, where, group by, & more w/ their corresponding MapReduce jobs in Hadoop

You see recommendation algorithms all the time, whether you realize it or not. Whether it’s Amazon recommending a product, Facebook recommending a friend, Netflix, a new TV show, recommendation systems are a big part of internet life. This is done by collaborative filtering, something you can perform through MapReduce with data collected in Hadoop. In this course, you’ll learn how to do it.

  • Access 4 lectures & 1 hour of content 24/7
  • Master the art of “thinking parallel” to break tasks into MapReduce transformations
  • Use Hadoop & MapReduce to implement a recommendations algorithm
  • Recommend friends on a social networking site using a MapReduce collaborative filtering algorithm

Data, especially in enterprise, will often expand at a rapid scale. Hadoop excels at compiling and organizing this data, however, to do anything meaningful with it, you may need to run machine learning algorithms to decipher patterns. In this course, you’ll learn one such algorithm, the K-Means clustering algorithm, and how to use MapReduce to implement it in Hadoop.

  • Access 7 lectures & 1.5 hours of content 24/7
  • Master the art of “thinking parallel” to break tasks into MapReduce transformations
  • Use Hadoop & MapReduce to implement the K-Means clustering algorithm
  • Convert algorithms into MapReduce patterns

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