
This course introduces Hadoop and big data for beginners, covering core components, architecture, and MapReduce, with hands-on installation and configuration of Hadoop and HDFS.
Explains big data concepts—volume, velocity, and variety—and shows how flight data, log files, NYSE data, and social media require analysis.
Explore the growth and challenges of big data, from structured and unstructured data to data quality, security, and scalable capture, storage, and real-time visualization.
Explore real-time, stream, and batch data processing, explain time windows, and show how Hadoop and Cassandra enable fast, scalable big data workflows.
Learn to set up an Amazon EC2 environment for big data projects. Create an instance, configure security groups and key pairs, SSH in, and install Java.
Set up a local environment for hands-on big data tools by importing an Ubuntu image into Oracle VirtualBox, configuring memory and storage, and completing Java JDK installation.
Learn how Hadoop enables distributed processing and scalable storage by scaling out with clusters of commodity hardware, using MapReduce to process massive data sets.
Explore the Hadoop ecosystem’s core components—distributed file system, MapReduce, and common utilities—along with open-source tools like Hive, Pig, Sqoop, Flume, and Spark, to understand scalable data processing.
See how Hadoop implementations power big data ecosystems by collecting, storing, and analyzing terabytes of multi-structured data from devices and logs.
Learn to install and configure a Hadoop cluster on Amazon, including downloading Hadoop package, setting up directories, creating symbolic links, and configuring core-site, mapred-site, and yarn-site for data and logs.
Install and configure Hadoop by editing key configuration files to set the default filesystem, replication factors, and namenode settings, including zookeeper and resource manager components.
Explore how HDFS architecture uses a single name node to manage the file system namespace and regulate access, while data nodes handle block storage and replication.
Explore Hadoop dfs admin and fs shell commands to manage the distributed storage, view reports and capacity, perform file system operations, and practice safe mode options.
Learn how Apache Pig translates Pig Latin scripts into MapReduce jobs on Hadoop, enabling high-level data transformations and ad hoc processing on data stored in the Hadoop distributed file system.
Learn how MapReduce processes large datasets on a Hadoop cluster by splitting input into maps, sorting map outputs, and reducing key-value pairs into results stored in the distributed file system.
Master a MapReduce Hadoop word count by implementing a mapper and reducer, configuring the job, and running the map and reduce workflow to tally word frequencies.
Explore Apache Hive, an open source framework for reading, writing, and managing large data sets on distributed storage with sql-like Hive query language.
Learn to install and configure Apache Hive on Amazon EMR, including downloading, extracting, creating symbolic links, and updating Hive configurations for seamless big data processing.
Perform a hands-on lab with Apache Hive to create a table, define columns, and load driver data into Hive; copy files to HDFS or Amazon and verify with Hive shell.
Discover Apache Hive, a database system for reading, writing, and managing large datasets in distributed storage. See how Hive executes queries with a sql-like language, partitions, and an execution engine.
Master Big Data with our PRACTICAL Big Data Course!
Big Data isn’t always found in a sorted filed cabinet; sometimes, it’s just in a huge mess of bits and bytes. The value of data isn’t understood until we start finding patterns and trends within the data, which we can then start using for making more sound and informed decisions.
However, there are technological solutions to help you not only sort data, but also help you find these trends within them. Hadoop and HDFS are two of the many different technologies that are available to help you. And, this is exactly what we are offering in this Big Data for absolute beginners course!
Hadoop is an open-source framework and is the more popular solution to big data. It works for storing and processing big data sets using the MapReduce programming model, in which it split files into large blocks and distributes them across nodes in a cluster. These clustered packaged codes are then transferred to nodes to process the data in parallel. This simplifies the process of sorting and processes data faster and more efficiently.
And
in this course, you will learn not only about Hadoop and associated technologies but also everything you need to know about Big Data.
From installation to configuration and even to actually tackling big
data, you will become a big master expert with our course.
Designed from the ground up, the only pre-requisite is knowing UNIX and Java. In collaboration with Big Data experts, at the end of this course you will not only have the theoretical knowledge, but also the confidence for putting this knowledge into practical application. You will be handling big data projects with our course in no time.
The course will cover topics such as different concepts of big data, setting up and configuring Hadoop and EC2 instance, Hadoop core concepts, HDFS architecture, Map Reduce, as well as installation and configuration of Apache Pig and Hive.
Enroll now and become a Big Data Ninja!