Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.
The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.
You will get access to a remote Hadoop cluster for this purpose. Assignments include running MapReduce jobs/Pig & Hive queries. The final project will give you a complete understanding of the Hadoop Ecosystem.
Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.
If you opt for the Mentorship Track with Industry Expert, you will get 6 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.
DeZyre has a 'No Questions asked' 100% money back guarantee. You can attend the first 2 webinars and if you are not satisfied, please let us know before the 3rd webinar and we will refund your fees.
For any doubt clearance, you can use:
In the last module, DeZyre faculty will assist you with:
- Learn to use Apache Hadoop to build powerful applications to analyse Big Data
- Understand the Hadoop Distributed File System (HDFS)
- Learn to install, manage and monitor Hadoop cluster on cloud
- Learn about MapReduce, Hive and PIG - 3 popular data analysing frameworks
- Learn about Apache Sqoop,Flume and how to run scripts to transfer/load data
- Learn about Apache HBase, how to perform real-time read/write access to your Big Data
- Work on Projects with live data from Twitter, Reddit, StackExchange and solve real case studies
Hadoop is an open source programming framework used to analyse large and sometimes unstructured data sets. Hadoop is an Apache project with contributions from Google, Yahoo, Facebook, Linkedin, Cloudera, Hortonworks etc. It is a Java based programming framework that quickly and cost efficiently processes data using a distributed environment. Hadoop programs are run across individual nodes that make up a cluster. These clusters provide a high level of fault tolerance and fail safe mechanisms since the framework can effortlessly transfer data from failed nodes to other nodes. Hadoop splits programs and data across many nodes in a cluster.
The Hadoop ecosystem consists of HDFS, MapReduce. This is accompanied by a series of other projects like Pig, Hive, Oozie, Zookeeper, Sqoop, Flume etc. There are various flavours of Hadoop that exist including Cloudera, Hortonworks and IBM Big Insights. Hadoop is increasingly used by enterprises due to its flexibility, scalability, fault tolerance and cost effectiveness. Anyone with a basic sql and database background will be able to learn hadoop.
Oozie is a scheduling component on top of hadoop for managing hadoop jobs. It is a java based web application that combines multiple jobs into a single logical unit of work. It was developed to simplify the workflow and coordination of hadoop jobs. Hadoop developers define actions and dependencies between these actions. Oozie then runs the workflow of dependent jobs i.e. it schedules various actions to be executed, once the dependencies have been met. Oozie consists of two important parts -
1) Workflow Engine - It stores and runs the workflows composed of Hadoop MapReduce jobs, hive jobs or pig jobs.
2) Coordinator Engine - Runs the workflow jobs based on the availability of data and scheduled time.
Read more on “How Oozie works?”
It is not necessary for one to have SQL knowledge to begin learning hadoop. For people, who have difficulty in working with java or have no knwoledge about java programming,some basic knowledge of SQL is a plus.However, there is no hard rule that you must know SQL but knowing the basics of SQL will give you the freedom to accomplish your Hadoop job using multiple components like Pig and Hive.
If you are getting started with Hadoop then you must read this post on -"Do we need SQL knowledge to learn Hadoop?"
Learning Hadoop is not a walk in the park, it takes some time to understand and gain practical experience on the hadoop ecosystem and its components. The best way to learn hadoop is to start reading popular hadoop books like – “Hadoop: The Definitive Guide”, read some interesting and informative hadoop blogs or hadoop tutorials that will give you some theoretical knowledge about the hadoop architecture and various tools in the ecosystem. However, to get a hadoop job theoretical knowledge does not suffice and gaining hands-on working experience to get a hang of the hadoop ecosystem is a must to land a top gig as a hadoop developer or hadoop administrator. DeZyre’s online hadoop training covers all the basics right from understanding “What is Hadoop?” to deploying your own big data application on the hadoop cluster. After the hadoop training, you can keep yourselves abreast with the latest tools and technologies in the hadoop ecosystem by working on hadoop projects in various business domains through Hackerday to add an extra feather to the cap on your hadoop resume.
A hadoop developer is responsible for actually programming and coding the business logic of big data applications using the various components- Pig, Hive, Hbase, etc of the hadoop ecosystem. The core responsibility of a Hadoop Developer is to load disparate datasets, perform analysis on them and unveil valuable insights. The job responsibilities of a Hadoop developer are like any other software developer but in the big data domain. Read More on Hadoop Developer – Job Responsibilities and Skills.
Hadoop Admin responsibilities are similar to that of system administrator responsibilities but a hadoop admin deals with the configuration, management and maintenance of hadoop clusters unlike a system admin who deals with servers. Quick overview of Hadoop Admin responsibilities –
Read More – Hadoop Admin Job Responsibilities and Skills
DeZyre offers corporate discounts for the hadoop course based on the number of students enrolling for the course. Contact us by filling up the Request Info. form on the top of the hadoop training page. Our career counsellors will get back to you at the earliest and provide you with all the details.
The Hadoop Distributed File System [HDFS] is a highly fault tolerant distributed file system, that is designed to run on low-cost, commodity hardware. HDFS is a Java-based file system that forms the data management layer of Apache Hadoop. HDFS provides scalable and reliable data storage thereby making it apt for applications with big data sets. In Hadoop, data is broken into small 'blocks' and stored in several clusters so that the data can be analyzed at a faster speed. HDFS has master/slave architecture. The HDFS cluster has one NameNode - a master server that manages the file system and several DataNodes. A large data file is broken into small 'blocks' of data and these blocks are stored in the Data Nodes. Click to read more on HDFS.
Hadoop MapReduce is a programming framework which provides massive scalability across Hadoop clusters on commodity hardware. MapReduce concept is inspired by the 'Map' and 'Reduce' functions that can be seen in functional programming. MapReduce programs are written in Java. A MapReduce 'job' splits big data sets into independent 'blocks' and distributes them in the Hadoop cluster for fast processing. Hadoop MapReduce performs two separate tasks and operates on [key,value] pairs. The 'map' job takes a set of data' converts it into another set of data which breaks the individual elements into tuples [key,value] pairs. The 'reduce' job comes after the 'map' job. Where the output of the 'map' job is treated as input and these data tuples are combined into smaller set of tuples. Click to read more on MapReduce.
HBase is open source, distributed, non relational database which has been modeled after Google's 'BigTable: A Distributed Storage System for Structured Data'. Apache HBase provide BigTable like capabilities on top of Hadoop HDFS. Hbase allows applications to read/write and randomly access Big Data. Hbase is written in Java, built to scale and can handle massive data tables with billions of rows and columns. HBase does not support a structured query language like SQL. With HBase schemas have to be predefined and the column families have to be specified. But HBase schemas are very flexible, as in, new columns can be added to the families at any time - this way HBase adapts to the changing requirement of the applications.
Apache PIG is a platform which consists of a high level scripting language that is used with Hadoop. Apache PIG was designed to reduce the complexities of Java based MapReduce jobs. The high level language used in the platform is called PIG Latin. Apache PIG abstracts the Java MapReduce idiom into a notation which is similar to an SQL format. Apache PIG does not necessarily write queries for the data, but it allows creating a complex data flow which shows how the data will be transformed, using graphs which include multiple inputs, transforms and outputs. PIG Latin can be extended using UDFs [User Defined Functions] using any other scripting language like Java, Python or Ruby. Click to read more on Apache PIG.
Apache Hive was developed at Facebook. Hive runs on top of Apache Hadoop as an open source data warehouse system for querying and analyzing big data sets stored in Hadoop's HDFS. Hive provides a simple SQL like query language - Hive QL, which translates Hadoop MapReduce jobs into SQL like queries. Hive and PIG though perform the same kind of functions, like, data summarization, queries and analysis - Hive is more user friendly, as anyone with a SQL or relational database background can work on it. HiveQL supports custom MapReduce jobs to be plugged into queries. But Hive is not built to support OTPL workloads. It means there can be no real time queries or row level updates made. Click to read more on Apache Hive.
Sqoop was designed to transfer structured data from relational databases to Hadoop. Sqoop is a 'SQL-to-Hadoop' command line tool which is used to import individual tables or entire databases into files in HDFS. This data is transformed into Hadoop MapReduce and again the data is transferred back to the relational database. It is not possible for MapReduce jobs to join with data directly on separate platforms. The database servers will suffer a high load due to concurrent connections while the MapReduce jobs are running. Instead if MapReduce jobs join with the data that is loaded on HDFS, it will further speed up the process. Sqoop automates this entire process with a single command line.
Apache Flume is a highly reliable distributed service used for collecting, aggregating and moving huge volumes of streaming data into the centralized HDFS. It has a simple and flexible architecture which works well while collecting and defining unstructured log data from different sources. Flume defines a unit of data as an 'event'. These events will then flow through one or more Flume agents to reach its destination. This agent is a Java process which hosts these 'events' during the data flow. Apache Flume components are a combination of sources, channels and sinks. Apache Flume sources, consumes events. Channels transfer events to their sinks. Sinks provides the Flume agent with pluggable output capability. Click to read more on Apache Flume.
Apache Zookeeper(often referred to as the "King of Coordination" in Hadoop) is high-performance, replicated synchronization service which provides operational services to a Hadoop cluster. Zookeeper was originally built at Yahoo in order to centralize infrastructure and services and provide synchronization across a Hadoop cluster. Since then, Apache Zoopkeeper has grown into a full standard of co-ordination on its own. It is now used by Storm, Hadoop, HBase, Elastic search and other distributed computing frameworks. Zookeeper allows distributed processes to co-ordinate with each other through a shared hierarchical name space of data registers knows as znodes. This will look like a normal file system, but Zookeeper provides higher reliability through redundant services.
Read More on "How Zookeeper works?"
- Learn by working on an end to end Hadoop project approved by DeZyre.
The complete Hadoop Distributed File System relies on the Namenode , so Namenode cannot be a commodity hardware. Namenode is the single point of failure in HDFS and has to be a high availability machine and not a commodity hardware.
DataNode also referred to as the Slave in Hadoop architecture is the place where the actual data resides. DataNode in the Hadoop architecture is configured with lots of hard disk space because it is the place for the actual data to be stored. DataNode continuously communicates with the NameNode through the heartbeat signal. When a DataNode is down, the availability of data within the hadoop cluster is not affected. The NameNode replicates the blocks managed by the DataNode that is down.
Sample DataNode Configuration in Hadoop Architecture
Processors: 2 Quad Core CPUs running @ 2 GHz
Network: 10 Gigabit Ethernet
RAM: 64 GB
Hard Disk 12-24 x 1TB SATA
NameNode is the single point of failure (centrepiece) in a hadoop cluster and stores the metadata of the Hadoop Distributed File System. NameNode does not store the actual data but consists of the directory tree of all the files present in the hadoop distributed file system. NameNode in Hadoop is configured with lots of memory and is a critical component of the HDFS architecture because if the NameNode is down, the hadoop cluster becomes out-of-the-way.
Hadoop scales best with dual core machines or processors having 4 to 8 GB of RAM that use ECC memory based on the requirements of the workflow. The machines chosen for hadoop clusters must be economical i.e. they should cost ½ to 2/3 rd of the cost of production application servers but should not be desktop class machines. When purchasing hardware for Hadoop, the utmost criteria is look for quality commodity equipment so that the hadoop clusters keep running efficient. When buying hardware for hadoop clusters, there are several factors to be considered including the power, network and any other additional components that might be included in large high-end big data applications.
Short Hadoop Tutorial for Beginners - Steps for Hadoop Installation on Ubuntu
Read more for detailed instructions on Installing Hadoop on Ubuntu
There are four hadoop cluster configuration files present in the hadoop/conf directory which should be configured to run HDFS. These configuration files should be modified accordingly based on the requirements of the Hadoop infrastructure. To configure Hadoop, the following four cluster configuration files have to be modified –
MapReduce vs Pig vs Hive - Professionals learning hadoop are likely to work with these 3 important hadoop components when writing Hadoop jobs. Programmers who know java, prefer to work directly hadoop mapreduce whilst others from a database background work with Pig or Hive components in the hadoop ecosystem. The major difference here is that Hadoop MapReduce is a compiled language based programming paradigm whereas Hive is more like SQL and Pig is a scripting language.Considering in terms of the development efforts that programmers have to spend when working with these hadoop components - pig and hive require less development effort than mapreduce programming.
Whenever developers need to process the data through ETL jobs and want tp load the resultant dataset into hive without manual intervention then external tables in Hive are used. External tables are also helpful when the data is not just being used by Hive but also other applications are using it. Here’s how we can create an External Table in Hive –
CREATE EXTERNAL TABLE DeZyre_Course ( CourseId BIGINT, CourseName String, No_of_Enrollments INT ) COMMENT â€˜DeZyre Course Informationâ€™ LOCATION /user/dezyre/datastore/DeZyre_Course
The above piece of HiveQL code will create an external table in Hive named DeZyre_Course. The location specifies where the data files would be put in.Name of the folder and the table name should always be the same.