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 Microsoft Track, you will get 8 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.
The most important interview question you will get asked is "What experience do you have?". Through the ProjectPro live classes, you will build projects, that have been carefully designed in partnership with companies.
The same companies that contribute projects to ProjectPro also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.
Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.
For any doubt clearance, you can use:
In the last module, ProjectPro faculty will assist you with:
This Hadoop certification exam tests a candidate's ability to implement batch data processing, real-time processing, and interactive processing on Hadoop in HDInsight. The Microsoft Hadoop certification exam 70-775 aims to test a candidates ability to accomplish the below mentioned technical tasks –
The Hadoop Projects at ProjectPro are based on real use cases from the industry. Working on Hadoop projects will solidify your working knowledge in Hadoop. In any Hadoop project - the process remains the same. Gathering the data, opening it in HDFS, identifying the attributes required for analysis, cleaning the data and then doing the transformation in analysis. But there are different projects that students can practice on, because it is a challenge to understand a particular set of data if you are not from that field.
In the ProjectPro 1-1 mentor track, you can choose any of the following projects to work on. You will be assigned an Industry mentor, who will oversee your project and guide you throughout the duration of the Hadoop Project. You will get 6 hours of 1-1 sessions with the mentors.
Once you complete the final project, you will receive the certificate in Big Data and Hadoop from ProjectPro. You can also mention this Hadoop project that you worked on, in your resume and on your LinkedIn profile. Let's get started.
Medicare is a Govenrment heatlh insurance program. Every month in your paycheck you will see a deduction for Medicare. The way this program works is - every month from your paycheck, the Govt. deducts an amount, whether you like it or not. Once you turn 65 or when you retire, or if someone turns out to be terminally ill, Medicare will give health insurance benefits throughout the remaining life of that person.
In this project, you will need to do some analysis of medicare data. This is a complicated data set. This is because private companies administer this program. It means Medicare claims are offered by private companies. The private companies, in order to attract the customers, will list some other benefits, apart from the basic benefits listed by the Govt. scheme and therefore will charge something extra for it. Medicare maintains the details of what plans are available in each county, across the country. Also these details include - what is the plan, who is providing it, what are the benefits offered in this plan and the charges for that. All this information is available in Medicare and is accessible.
In this project - you will directly get the data from Medicare, to perform analysis on the data. Different private companies offer a variety of Medicare plans in many counties. Since senior citizens have to enroll in these plans every year - it becomes important to choose the right plan, because every plan has different benefits and different charges for availing the benefits. For senior citizens - this is the biggest financial decision they make every year. Medicare data is focused on choosing the right Medicare plan. Some queries that you will be working on are:
All these questions need to be answered through big data analytics - so that it can help members choose the right kind of plan based on their requirements.
All mobile operators will have call records. Every day, all the calls that are made by people are recorded and a log file is maintained for these records. This is known as the CDR data. In this project, to get real insights on the data - you have to analyse over 100 million calls in the logs(each day). The objective of this analysis is to figure out how to resolve the call-drop issue. For example, if somebody is experiencing more than 17 call-drops in a month, then there is 90% chance that the person will drop out of the network.
You will need to perform the call-drop analysis on the call log data, on a daily basis, so that-
The reason this kind of analysis is becoming critical is - for a company, it is always cheaper to retain a customer than to acquire new customers. This will allow u to advise the tech support team to optimize the towers from which more call-drops are occuring, to expand the capacity of these towers from where there are call drops occuring. To do this per day data should be analysed. Per day the data produced for one mobile network is 100 million call records. Since it cannot be fit into one machine - you will need to split that into 2 million blocks - so that all of it can fit into one machine for simultaneous analysis.
In a bank, Hadoop tools can be used to predict mortgage defaulters and improve market segmentation. It requires you to move the data from multiple data warehouses into the Hadoop cluster and then build some queries on that data.
For market segmentation for increased business, you need to:
The requirement of this project, is to load the data into Hadoop clusters and build these queries.
You can build a listening platform based on Twitter data analysis. If a company wants to understand what people think about their products or services and what are the sentiments of their customers, they can turn to social media platforms like Twitter. To extract the comments that are related to the company, its products or brands, Hadoop can be used to gather that data and run some analysis on that data. You can build queries around the data like:
This kind of analysis is useful for the company for marketing campaigns and customer support. This will help curb distatified customers leaving bad comments in social media which could affect their brand.
In a short span of time, we have helped many people move up in their careers or change their career paths.
- 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
ProjectPro is an authorised Microsoft Training Partner. We train you for the Microsoft Big Data Engineering Certification. We will assign a Hadoop Architect as your mentor. You will get 8,1-to-1 live online sessions with this mentor. You will jointly implement a project. You will also receive study materials from Microsoft. The mentor will also help you prepare for the Microsoft certification.
ProjectPro's hadoop training follows a complete hands-on approach where professionals/students get to work on multiple hadoop projects that are based on real big data use cases in the industry.Apart from this ProjectPro also has hundreds of other big data projects and hadoop projects for practice across diverse business domains that students can enrol for at a nominal fee per project.
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. ProjectPro’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
ProjectPro 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 ProjectPro.
These short Hadoop tutorials help boost in-depth knowledge around each of the components in the Hadoop ecosystem as they are in the form of advanced lessons for a quick memory recap of all that you learnt in your Hadoop training course. With to-the-point solutions to every problem a professional might encounter, while using any of the Hadoop components, these short Hadoop tutorials can be your guide to working with Hadoop on a daily basis.
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
One of the ways of reading more than one database simultaneously is to export the data from RDBMS as raw text files, followed by using MultipleInputs do process the jobs. However there is an alternate of using MultipleInputs. It is feasible to execute 2 map only jobs and then combining the output from these jobs at input to a final job which performs the necessary logical processing required for the reducer.
In a normal MapReduce job the task is divided in different nodes and executed simultaneously. This parallel processing is a key feature for MapReduce, where the consequential jobs are independent of each other and therefore can be executed simultaneously. As the name suggests in a Sequential MapReduce job, the data has to be processed from a single node at some point because there is no way of dividing the processing.
Hadoop MapReduce is a programming paradigm designed for database that exceeds the system memory, whereas on dedicated clusters Spark has less latency especially when all the data can be stored in system memory. In terms of ease of use, Hadoop MapReduce is difficult to program compared to Spark, but since it is widely used in Industry, a lot of tools are available to make it easier. Both of the technology offer more or less same amount of compatibility for data types and sources. As a conclusion, Hadoop MapReduce is better suited for Batch Processing whereas Spark is meant for Data Processing.
Name node is the master node which has all the metadata information. It contains the information about no. of blocks, size of blocks, no. of vacant blocks, no. of replicated blocks etc. DataNode is a slave node, which sends information to the Name node about the files and blocks stored in and responds to the Name node for all file.
Any Hadoop cluster will have these basic 4 roles; NameNode, JobTracker, TaskTracker and DataNode. The machines in the cluster will perform the task of Data storage and processing. In order to operate DataNode/TaskTracker in a balanced Hadoop cluster, the below mentioned configuration is recommended: -- 64 to 512 GB of RAM -- 2 quad-/hex-/octo-core CPUs (Minimum Operating Frequency 2.5 GHz) -- 10 Gigabit Ethernet or Bonded Gigabit Ethernet -- 12-24 1-4TB hard disks in a JBOD Hardware requirement for running NameNode and JobTracker are relaxed in terms of RAM size and memory, nearly one third; though it depends upon the requirement and redundancy.
InputFormats in Hadoop defines the method to read data from a file into the Mapper instances. There are multiple implementation of the InputFormats, they are suitable for text files and their various interpretations and suitable for reading particular binary file formats. InputFormats generates the key-value pair inputs for the mappers by parsing the input data. In Hadoop architecture, InputFormats is responsible for splitting the input data into logical partitions.
This is a unit testing framework designed for Java Programming Language. Junit is used for test driven development and belongs to unit testing frameworks. JUnit is linked as a JAR at compile-time; the framework resides under package junit.framework for JUnit 3.8 and earlier, and under package org.junit for JUnit 4 and later.
Let us assume there are 4 tables with name p,q,r,s and all these tables have a common column "ObjectId". The objective is to join all these tables together in HIveQL using JOIN ON command.
Syntax:
SELECT p.column1, q.column1, r.column1, s.column1 FROM p JOIN q ON (p.ObjectId=q.ObjectId) JOIN r ON (r.ObjectId=q.ObjectId) JOIN s ON (s.ObjectId=r.ObjectId)
The data being used in HiveQL is case independent. Hive parser doesn't discrimated between upper or lower case letters. Therefore for to and from CamelCase conversion, the 'to_json' and 'from_json' take and extra boolean argument, which acts as a flag for conversion. This allows to ouput the format in the required case.
Syntax:
select
to_json(
named_struct("camel_case",1,"check_casing",2)
from table_name;
Output:
{"CamelCase":1,"Check_Casing":2}
Responsibilities
Responsibilities
Responsibilities:
Strategy/Business: