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Learn to Build Big Data Apps by working on Hadoop Projects

You have read some of the best Hadoop books, taken online hadoop training and done thorough research on Hadoop developer job responsibilities – and at long last, you are all set to get real-life work experience as a Hadoop Developer. But when you browse through hadoop developer job postings, you become a little worried as most of the big data hadoop job descriptions require some kind of experience working on projects related to Hadoop. DeZyre industry expert’s advice that you should build a project portfolio with some well-thought out Hadoop projects that will help you demonstrate your range of Hadoop skills to your prospective employers.

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How working on Hadoop projects will help professionals in the long run?

Hadoop Projects

The collection of these projects on Hadoop and Spark will help professionals master the big data and Hadoop ecosystem concepts learnt during their hadoop training. The changed paradigm, increasing demand and competition requires Hadoop developers to be very strong at applying Hadoop concepts in practicality. Hadoop projects for beginners are simply the best thing to do to learn the implementation of big data technologies like Hadoop. Building a project portfolio will not merely serve as a tool for hiring managers but also will boost your confidence on being able to speak about real hadoop projects that you have actually worked on. Having multiple hadoop projects on your resume will help employers substantiate that you can learn any new big data skills and apply them to real life challenging problems instead of just listing a pile of hadoop certifications.

"Hadoop created this centre of gravity for a new data architecture to emerge. Hadoop has this ecosystem of interesting projects that have grown up around it."-  said Shaun Connolly, VP of corporate strategy at Hadoop distribution company Hortonworks.

“What are some interesting beginner level big data hadoop projects that I can work on to build my project portfolio?” – This is one of the most common question asked by students who complete Hadoop Training and Certification from DeZyre. There are various kinds of hadoop projects that professionals can choose to work on which can be around data collection and aggregation, data processing, data transformation or visualization.

DeZyre has collated a list of major big data projects within the Hadoop and spark ecosystem that will help professionals learn on how to weave these big data technologies together in production deployments. Working on these Hadoop projects will not just help professionals master the nuances of Hadoop and Spark technologies but understand how they actually help solve real world challenges and how various companies use them. These Hadoop projects come with detailed understanding of the problem statement, source code, dataset and a video tutorial explaining the entire solution. You can always rely on these Hadoop projects and make the best use of available time and resources to master the Hadoop ecosystem that will help you fetch your next best Hadoop developer job.

Big Data and Hadoop Projects for Beginners

1) Visualizing Website Clickstream Data with Hadoop

A clickstream is the recording of various parts of the computer screen a user clicks while browsing an application or website. The actions of the user on clicking various parts of the web pages are recorded on the client side or inside the browser or the web server. Clickstream data is captured in semi structured web log files that contain various data elements like data and timestamp, IP address of the visitor, visitor identification number , web browser information, device information, referral page info, destination URL, etc.

Problem Statement

With increasing number of ecommerce businesses, there is a need to track and analyse clickstream data. Using traditional databases to load and process clickstream data has several complexities in storing and streaming customer information and also requires huge amount of processing time to analyse and visualize it. This problem can be solved by using various tools in the hadoop ecosystem. In this hadoop project, JSON data format is loaded into Hive and the data is analysed in Hive.

What will you learn from this hadoop project?

  • Analyse JSON data; Loading JSON format to Hive
  • Create a Schema to the fields in the table.
  • Creating queries to set up the EXTERNAL TABLE in Hive
  • Create new desired TABLE to copy the data.
  • Creating query to populate and filter the data.
  • Analyse log files in HIVE.

Access the Solution to “Visualize Website Clickstream Data” Hadoop Project

2) Million Song Dataset Challenge

This is a famous Kaggle competition for evaluating a music recommendation system. Users will work on the Million Song Dataset released by the Columbia University’s Lab for Recognition and Organization of Speech and Audio. The dataset consists of metadata and audio features for 1M contemporary and popular songs.

Problem Statement

For a given user, we have their song history and the count on how many times the song was played by a user.  In this big data project, we want to provide a set of recommendations to the user by understanding on how the song history can be used. This can be done by looking at songs which are most similar to user songs and also by grouping similar users based on their listening history. The challenging aspect of this big data hadoop project is to decide on what features need to be used to calculate the song similarity because there is lots of metadata for each song.

What will you learn from this hadoop project?

  • Learn to build a music recommendation system using Collaborative Filtering method.
  • Analysis large datasets easily and efficiently.
  • Using data flow programming language "Pig Latin" for analysis
  • Data compression using LZO codec
  • PigLatin UDF "DataFu" (Created by LinkedIn) for data localization
  • Working with Hierarchical Data Format (HDF5)

Access solution to the popular Kaggle Challenge- “Million Song Dataset

 

3) MovieLens Dataset Exploratory Analysis

MovieLens dataset is mostly used for building recommender systems that predict user movie ratings based on similar user ratings. MovieLens dataset consists of 22884377 ratings and 586994 tag applications across 34208 movies created by 247753 users. For professionals who have no idea about hadoop MapReduce or have no interest in writing MapReduce programs, this is an interesting hadoop project as it allows exposing the best parts of hadoop by using Hive.

Problem Statement

In this project we will explore the MovieLens dataset to find trends in movie preferences. We expect that users having similar taste are likely to rate movies with high correlation. The data is provided as an input to hive which is analysed and partitioned based on various attributes like Genre, occupation, ratings.

What will you learn from this big data hadoop project?

  • Working with different file formats (.dat, CSV and text)
  • HQL for effective data analysis
  • Serde packages to load data
  • Internal and External tables in Hive
  • Logical queries for efficient scripting

Access Solution to MovieLens Dataset Exploratory Analysis

Have you completed working on any of these hadoop projects or do you already have a portfolio of big data and hadoop projects?  Let us know about the various big data and hadoop projects you have worked on to build your project portfolio.

Having worked on these big data and Hadoop projects, professionals should be confident enough to build any big data application using the Hadoop family of big data technologies. Get started now on your big data journey. Dig up on some of the best hadoop projects listed above and put them together on your hadoop resume to demonstrate your knowledge, interest and big data skills to prospective employers.

Begin your journey in the big data space by working on interesting Hadoop Projects for just $9!

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