Hadoop, a collection of open-source software utilities that was released in the year 2006 encourages utilizing an organization of numerous PCs to tackle issues including vast measures of information and calculation. It gives a product structure to appropriated capacity and handling of enormous information utilizing the MapReduce programming model.
It is a framework that bolsters the preparation of huge informational indexes in a circulated processing climate and gives astounding information about the board arrangement. Hadoop is intended to extend from single workers to a huge number of machines, each giving calculation and capacity.
Huge Information aces are confronting genuine difficulties in putting away, cleaning, and dissecting huge informational collections financially progressively. Progressively, endeavors are searching for information answers to transform examination into bits of knowledge for settling on strong choices.
For that, they need information experts who realize how to change over Huge Information into Enormous Chances. Portions from a discourse conveyed by Hadoop organizer Doug cutting at Cloud manufacturing plant in Banff, Canada are recorded underneath. This only shows that Hadoop is the future for these big data masters.
From the above-mentioned points, you will come to realize that learning Hadoop can open a new portal of opportunities in your career and you are ready for it! So, we have curated a list of the Best Hadoop Classes that you can take to learn and get a good experience.
Best Hadoop Classes
1. Learn Big Data: The Hadoop Ecosystem Masterclass
Master the Hadoop ecosystem using HDFS, Map, Reduce, Yarn, Pig, Hive, Kafka, HBase, Spark, Knox, Ranger, Ambari, and Zookeeper.
Course rating: 4.4 out of 5.0 ( 3,397 Ratings total)
In this course, you will learn how to:
- process Big Data using batch.
- process Big Data using real-time data.
- be familiar with the technologies in the Hadoop Stack.
- be able to install and configure the Hortonworks Data Platform (HDP).
You will learn how to use the most popular software in the Big Data industry at moment, using batch processing as well as real-time processing. This course will give you enough background to be able to talk about real problems and solutions with experts in the industry.
Updating your LinkedIn profile with these technologies will make recruiters want you to get interviews at the most prestigious companies in the world.
2. Big Data
Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted.
Course rating: 4.5 out of 5.0 ( 18,061 Ratings total)
In this course, you will learn:
- explanation of the architectural components and programming models used for scalable big data analysis.
- the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system, and the MapReduce programming model.
- how to install and run a program using Hadoop!
- about different data elements in your own work and in everyday life problems.
- why your team needs to design a Big Data Infrastructure Plan and Information System Design.
- how you can identify the frequent data operations required for various types of data.
- selecting a data model to suit the characteristics of your data.
- applying techniques to handle streaming data.
- how to differentiate between a traditional Database Management System and a Big Data Management System.
- why there are so many data management systems.
- how to design a big data information system for an online game company.
- retrieving data from example databases and big data management systems.
- about the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications.
- how to identify when a big data problem needs data integration.
- executing simple big data integration and processing on Hadoop and Spark platforms.
Initially, the course will describe the Big Data landscape including examples of real-world big data problems including the three key sources of Big Data: people, organizations, and sensors. Next, it will explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis, and reporting.
You will learn how to get value out of Big Data by using a 5-step process to structure your analysis. The course will also identify what are and what are not big data problems and be able to recast big data problems as data science questions.
In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.
You will also become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources.
Lastly, you will build a big data ecosystem using tools and methods from the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing an imaginary game "Catch the Pink Flamingo".
During the project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. Initially, you will be introduced to the data set and guide through some exploratory analysis using tools such as Splunk and Open Office.
Then, you will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib, and Gephi. Finally, the course will show you how to bring it all together to create engaging and compelling reports and slide presentations.
After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.
3. The Ultimate Hands-On Hadoop - Tame your Big Data!
Hadoop tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka.
Course rating: 4.5 out of 5.0 ( 22,941 Ratings total)
In this course, you will learn how to:
- design distributed systems that manage "big data" using Hadoop and related technologies.
- use HDFS and MapReduce for storing and analyzing data at scale.
- use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
- analyze relational data using Hive and MySQL.
- analyze non-relational data using HBase, Cassandra, and MongoDB.
- query data interactively with Drill, Phoenix, and Presto.
- choose an appropriate data storage technology for your application.
- understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
- publish data to your Hadoop cluster using Kafka, Sqoop, and Flume.
- consume streaming data using Spark Streaming, Flink, and Storm.
The course includes:
- Installing and working with a real Hadoop installation right on your desktop with Hortonworks (now part of Cloudera) and the Ambari UI
- Managing big data on a cluster with HDFS and MapReduce
- Writing programs to analyze data on Hadoop with Pig and Spark
- Storing and querying your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix, and Presto
- Designing real-world systems using the Hadoop ecosystem
- Learning how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin, and Hue
- Handling streaming data in real-time with Kafka, Flume, Spark Streaming, Flink, and Storm
4. Learning Hadoop
Learn about Hadoop, key file systems used with Hadoop, its processing engine—MapReduce—and its many libraries and programming tools.
Course rating: 8,615 total enrollments
The course includes:
- Why Change?
- What Is Hadoop?
- Understanding Hadoop Core Components
- Setting up Hadoop Development Environment
- Understanding MapReduce 1.0
- Tuning MapReduce
- Understanding MapReduce 2.0/YARN
- Understanding Hive
- Understanding Pig
- Understanding Workflows and Connectors
- Using Spark
- Hadoop Today
This course is your introduction to Hadoop; key file systems used with Hadoop; its processing engine, MapReduce, and its many libraries and programming tools.
It shows how to set up a Hadoop development environment, run and optimize MapReduce jobs, code basic queries with Hive and Pig, and build workflows to schedule jobs.
Plus, learn about the depth and breadth of available Apache Spark libraries available for use with a Hadoop cluster, as well as options for running machine learning jobs on a Hadoop cluster.
5. Taming Big Data with MapReduce and Hadoop - Hands On!
Learn MapReduce fast by building over 10 real examples, using Python, MRJob, and Amazon's Elastic MapReduce Service.
Course rating: 4.5 out of 5.0 ( 2,538 Ratings total)
In this course, you will learn:
- how MapReduce can be used to analyze big data sets.
- how to write your own MapReduce jobs using Python and MRJob.
- how to run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce.
- other Hadoop-based technologies, including Hive, Pig, and Spark.
- what Hadoop is for, and how it works.
- how to chain MapReduce jobs together to analyze more complex problems.
- how to analyze social network data using MapReduce.
- how to analyze movie rating data using MapReduce and produce movie recommendations with it.
The course includes:
- Learning the concepts of MapReduce
- Running MapReduce jobs quickly using Python and MRJob
- Translating complex analysis problems into multi-stage MapReduce jobs
- Scaling up to larger data sets using Amazon's Elastic MapReduce service
- Understanding how Hadoop distributes MapReduce across computing clusters
- Learning about other Hadoop technologies, like Hive, Pig, and Spark
Disclosure: This post includes affiliate links; our team may receive compensation if you purchase products or services from the different links provided in this article.
Top comments (0)