Videos
Each project comes with 2-5 hours of micro-videos explaining the solution.
Code & Dataset
Get access to 102+ solved projects with iPython notebooks and datasets.
Project Experience
Add project experience to your Linkedin/Github profiles.
The goal of this IoT project is to build an argument for generalized streaming architecture for reactive data ingestion based on a microservice architecture.
In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.
In this project, we will look at two database platforms - MongoDB and Cassandra and look at the philosophical difference in how these databases work and perform analytical queries.
The goal of this hadoop project is to apply some data engineering principles to Yelp Dataset in the areas of processing, storage, and retrieval.
In this big data project, we will continue from a previous hive project "Data engineering on Yelp Datasets using Hadoop tools" and do the entire data processing using spark.
Build a fully working scalable, reliable and secure AWS EMR complex data pipeline from scratch that provides support for all data stages from data collection to data analysis and visualization.
In this big data project, we will embark on real-time data collection and aggregation from a simulated real-time system using Spark Streaming.
In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.
In this big data project, we will be performing an OLAP cube design using AdventureWorks database. The deliverable for this session will be to design a cube, build and implement it using Kylin, query the cube and even connect familiar tools (like Excel) with our new cube.
In this big data project, we will discover songs for those artists that are associated with the different cultures across the globe.
Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.
In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.
In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.
In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
Hadoop Project- Perform basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala.
In this hive project, you will design a data warehouse for e-commerce environments.
This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.
Hadoop Projects for Beginners -Learn data ingestion from a source using Apache Flume and Kafka to make a real-time decision on incoming data.
In this spark streaming project, we are going to build the backend of a IT job ad website by streaming data from twitter for analysis in spark.
Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.
In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.
The goal of this Spark project is to analyze business reviews from Yelp dataset and ingest the final output of data processing in Elastic Search.Also, use the visualisation tool in the ELK stack to visualize various kinds of ad-hoc reports from the data.
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this big data project, we will continue from a previous hive project "Data engineering on Yelp Datasets using Hadoop tools" and do the entire data processing using spark.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
The goal of this spark project is to analyse the level and strength of interactions across areas of coverage of a telecom provider between different areas in the city of Milan.
In this Neo4j project, you will do network analysis using a graph database to find patterns on how a social network affects business reviews and ratings.
In this big data project, we will look at how to mine and make sense of connections in a simple way by building a Spark GraphX Algorithm and a Network Crawler.
In this hadoop project, you will be using a sample application log file from an application server to a demonstrated scaled-down server log processing pipeline.
Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
Learn to perform 1) Twitter Sentiment Analysis using Spark Streaming, NiFi and Kafka, and 2) Build an Interactive Data Visualization for the analysis using Python Plotly.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.
In this Hackerday, we will go through the basis of statistics and see how Spark enables us to perform statistical operations like descriptive and inferential statistics over the very large dataset.
In this project, we will look at two database platforms - MongoDB and Cassandra and look at the philosophical difference in how these databases work and perform analytical queries.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this project, we are going to talk about insurance forecast by using regression techniques.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.
In this big data project, we'll work with Apache Airflow and write scheduled workflow, which will download data from Wikipedia archives, upload to S3, process them in HIVE and finally analyze on Zeppelin Notebooks.
In this big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.
The goal of this IoT project is to build an argument for generalized streaming architecture for reactive data ingestion based on a microservice architecture.
In this project, we are going to analyze streaming logfile dataset by integrating Kafka and Kylin.
In this project, we will show how to build an ETL pipeline on streaming datasets using Kafka.
In this big data project using Neo4j, we will be remodelling the movielens dataset in a graph structure and using that structures to answer questions in different ways.
In this Neo4j project, you will do network analysis using a graph database to find patterns on how a social network affects business reviews and ratings.
Spark Project - Discuss real-time monitoring of taxis in a city. The real-time data streaming will be simulated using Flume. The ingestion will be done using Spark Streaming.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this GCP Project, you will learn to build a data processing pipeline With Apache Beam, Dataflow & BigQuery on GCP using Yelp Dataset.
In this AWS Project, you will build an end-to-end log analytics solution to collect, ingest and process data. The processed data can be analysed to monitor the health of production systems on AWS.
In this AWS Project, you will learn the best practices for website monitoring using AWS services like Lambda, Aurora MySQL, Amazon Dynamo DB and Kinesis.
Big Data Projects with Source Code
Every year, people looking to begin their big data career run into a familiar conundrum - "How can I land a big data job with limited experience in this field?".
For an emerging field like big data, finding internships or full-time big data jobs requires you to showcase relevant achievements working with popular open-source big data tools like Hadoop, Spark, Kafka, Pig, Hive, and more. Big data and project-based learning are a perfect fit. The best way to get started is to begin working on diverse big data project titles under the mentorship of industry experts. Professionals will love working on these big data projects because it's like a secret. There is so much practical learning involved you don't realize it. ProjectPro's big data projects are perfect for beginners, college students, engineering students, professionals wanting to make a career switch, and anyone who wants to master big data skills with hands-on experience.
Big Data Projects for Beginners
If you have a graduate degree in analytics or a relevant field from a top-tier college, it is easy for you to get a big data job. Employers believe that you will be able to add value to their business because of the prestige of the college that has awarded you the degree, and the reality that it is in a subject that is relevant to the kind of skills they are looking for. If you do not have an analytics degree from a top-tier college then you need to build that trust yourself that you have the big data skills that the employer is looking for. The best way to build trust with the hiring manager is to work on interesting big data project ideas and build a portfolio of multiple big data projects - Hadoop projects, spark projects, hive projects, Kafka projects, impala projects, and more. The more "real-world" the big data projects are, the more the hiring manager will trust that you will be an asset to their organization, and the greater are your chances of landing the big data job. The best thing about big data careers is that the work you do on building diverse big data projects often looks exactly similar to the work you will do once you are hired.
For IT professionals or anybody with basic big data knowledge, Dezyre's mini projects on big data will help them take responsibility in solving challenging data problems, and help gain expertise on the popular big data tools like Hadoop, Spark, Hive, Pig,
-
Airline Dataset Analysis using Hadoop, Hive, Pig, and Impala
-
Analyse movie rating data for better movie recommendation
-
Analyzing Big Data with Twitter Sentiments using Spark Streaming
-
Building a Data warehouse using Spark on Hive
-
Visualizing Website Clickstream Data with Apache Hadoop
Big Data Projects for Engineering Students
The good news for people in search of big data projects for CSE students is that there are a couple of websites that have big data projects with source code. If you google for search terms like "big data projects GitHub" or "big data projects Quora", you might find suggestions on multiple big data project titles, however, for students on the hunt for big data final year projects, titles and source code is not what all they need for learning. Students need industry expert guidance for deeper understanding and greater retention of knowledge so that they can apply what they know to new real-world big data problems. ProjectPro has an excellent project-based learning platform where students will enjoy using a spectrum of big data tools under expert guidance.
Here are some popular big data project titles among the college students-
-
Building a Data warehouse using Spark on Hive
-
Building end-to-end data warehousing pipeline with Kafka
-
E-Commerce Data Warehouse
-
Design a Hadoop Architecture
-
Processing web server log
IT professionals and college students rate our big data projects as exceptional. Whether you are looking to upgrade your skills or you are looking to learn about the complete end-to-end implementation of various big data tools like Hadoop, spark, pig, hive, Kafka, and more, Dezyre's mini projects on big data are just what you want.
Different types of big data projects we offer for practice
ProjectPro’s repository has solved end-to-end big data projects with source code across diverse big data tools and technologies. As of today, we have big data projects with source code that implement the following tools, however, the project repository is updated every month with novel projects on big data that make use of the latest tools and technologies in the industry.
Big Data Projects using Hadoop
Apache Hadoop is an open-source Java-based software platform specifically designed to handle large amounts of data. Apache Hadoop works on the concept of distributed data sets and analytics jobs across nodes in a computing cluster so that the processing jobs can be broken down into smaller tasks and can be made to run in parallel. Hadoop is able to process both structured and unstructured data.
Get Access to Apache Hadoop Projects with Source Code
Big Data Projects using Apache Hive
Apache Hive is a data warehouse tool built on top of Apache Hadoop that lets you query data from the Hadoop. Hive allows users to read, write and manage the data within the Hadoop Distributed File System (HDFS) using SQL. Hive is closely integrated with Hadoop and hence allows querying of large datasets.
Get Access to Apache Hive Projects on Big Data with Source Code
Apache HBase Projects
Apache HBase is a distributed column-oriented database built on top of the Hadoop file system. Apache HBase is modeled after Google’s Bigtable and is written in Java. Apache HBase provides a fast lookup for large tables that contain large amounts of structured data. The purpose of HBase is to provide random, real-time access to data.
Get Access to Apache HBase Projects with Source Code
Apache Pig Projects
Apache Pig is a high-level platform that is an abstraction over MapReduce and is generally used to analyze large sets of data by representing them as data flows. In a MapReduce framework, programs need to be structured into a series of Map and Reduce stages. Since not all data analysts are familiar with this, an abstraction called Pig was built on top of Apache Hadoop. Hence, Pig allows users to spend more time and focus more on analyzing large datasets and spend less time on writing programs in Map and Reduce stages.
Get Access to Apache Pig Projects with Source Code
Hadoop HDFS Projects
The Hadoop Distributed File System is an architecture that was developed using distributed file system design. This means that large amounts of data are distributed across multiple machines. HDFC allows the storage of huge amounts of data and permits easier access. HDFS is built for distributed storage and processing. With the data distributed across multiple machines, there is less risk associated with hardware failure because all the data is not stored in a single location. In addition, the distributed file system allows breaking up a large job into smaller jobs which can also be distributed and allowed to run in parallel.
Get Access to HDFS Projects with Source Code
Apache Oozie Projects
Apache Oozie is a scheduling tool used to specify the pipelining of programs to run in a particular order on Hadoop’s distributed environment. It is a Java Web application that allows workflow scheduling of dependent jobs.
Get Access to Apache Oozie Projects with Source Code
Apache Impala Projects
Apache Impala is a massively parallel processing SQL query engine for processing large volumes of data stored in a computer cluster running Apache Hadoop. Impala provides the fastest way to access data stored in the HDFS. With Impala, users can query data on the HDFS or HBase using SQL in a faster manner when compared to other SQL querying engines like Hive.
Get Access to Apache Impala Projects with Source Code
Apache Flume Projects
Apache Flume is a component of the Hadoop ecosystem used to process large amounts of streaming log data. Many companies/businesses store unstructured data of their customers like likes, emails, hashtags, etc. It is difficult for them to draw relevant conclusions from such unstructured data, and this is where Apache Flume comes in handy. It collects and aggregates vast amounts of unstructured data from multiple sources and then transfers it to Hadoop HDFS/Hive. The companies can use Apache flume to utilise customer log files for analysing customer behaviour and implement machine learning algorithms over it through Apache Hadoop.
Commonly Asked FAQs for Big Data Projects
Why is now the time to hone big data skills?
More data is created every hour today than in an entire year just 20 years ago, according to the Seagate Rethink Data Survey by IDC, which was released in January 2020. Today’s tech market is dominated by technologies like big data, data science, machine learning, and cloud computing - the areas that usually go hand-in-hand and see a crossover in skillsets for diverse data job roles like data analyst, data engineer, data architect, and data scientist to name a few. According to Forbes, over 90% of companies state that they are still facing the need to manage unstructured data. Demand for data skills is growing and there is a huge shortage of skilled talent and honing these skills with the know-how of the latest tools and technologies will definitely make you stand out from the crowd of job applicants.ProjectPro helps you hone the most in-demand big data skills through real-time big data projects that have been vetted and created along with industry experts from Uber, JPMorgan, Paypal. This ensures relevance in the big data industry and provides you with the skills that matter the most at any given point in time in your career.
Why should I practice big data skills by building big data projects?
One of the hardest things to do when beginning a big data or data science career is to create a project portfolio of real-time big data projects. Whether you’re taking the first steps in your big data career or just want to brush up on your big data skills, the most important thing to have on your resume is hands-on projects. Most students or beginners do not have any substantial work experience and the big data courses they do might not just be enough in putting up a worthy portfolio to showcase to the hiring managers. ProjectPro’s big data projects have been vetted and created along with industry experts from Uber, JPMorgan, Paypal. This ensures relevance in the big data industry and providing you with the content that is industry-standard and matters the most. We have simple big data projects for practice and also advanced big data projects with source code that will test your data skills and help you build a well-rounded analytics portfolio that a hiring manager will definitely take a notice of. Included in each big data project with source code is –
- 5 to 6 hours of mini-videos, where the industry expert is implementing the big data project.
- Downloadable source code with a solution methodology document and the architectural implementations.
- Downloadable datasets.
What will you get when you enroll for ProjectPros Big Data projects?
- Big Data Project Source Code: Examine and implement end-to-end real-world big data projects from the Banking, eCommerce, and Entertainment sector using this source code.
- Recorded Demo: Watch a video explanation on how to execute these big data projects.
- Complete Solution Kit: Get access to the solution design, documents, and supporting reference material, if any.
- Mentor Support: Get your technical questions answered with mentorship from the best industry experts for a nominal fee.
- Hands-On Knowledge: Equip yourself with practical skills in the big data ecosystem.
How will you build these big data projects?
- Each big data analytics project is a 4-6 hour video recording (consisting of 20+ smaller videos).
- In these project videos, the industry expert walks you through how to implement the big data project in real-time and troubleshoot.
- Prepare, practice, and experience firsthand how a real big data job interview feels like through a virtual face-to-face 90-minute mock interview session with an industry expert. Boost confidence and also get constructive feedback to improve your areas of weakness.
- Connect with a big data expert if you need further career guidance.(for an additional fee)
How many big data projects with source code do you have?
The number of big data projects available at any point in time cannot be pinned at an exact number because we keep actively building up our project repository every month. The repository of real-time big data projects is updated every month with new projects based on the most in-demand and novel big data tools and technologies, some of which consists of big data tools like Hadoop, Spark, Redis, Kafka, Kylin, Redis, to name a few and popular cloud platforms like AWS, Azure, and GCP. We have projects right from beginner level to the advanced level so there are big data projects for beginners, big data projects for students, and big data projects for professionals willing to upskill. The independent projects available will help you showcase your versatile big data skills to employers making them a great fit for winning the big data job that you have dreamed of.
What kinds of datasets are used in these big data projects and what is their source?
We understand that while working on big data projects, one of the toughest challenges is having access to large datasets. If you have been struggling to find the right dataset for your project, then these big data projects can come to your rescue. All our big data projects provide downloadable datasets so right from the dataset to the source code to video explanations of the source code- these have everything you need to deploy a project in production. Our projects are developed using popular big datasets from online repositories like Kaggle, UCI Machine Learning Repositories, Data.Gov, Google Public Datasets, AWS Public Datasets, and by scraping data from other sources.. Irrespective of whether you require a dataset to start working on a specific big data or data science project, or whether you need it to just practice your big data skills, you need not waste time browsing the internet for the required datasets. We’ve got all sorts of datasets covered to help you meet your project goal.
Can I get a trial period for a week to practice these sample big data projects?
No, we do not provide any free trial. However, you can avail a free demo on the real-time big data projects that are available on the site. You can drop an email to binny@dezyre.com to schedule a free demo with one of our Project Advisors.
Can we download the videos for every big data project?
No, the video lectures for the big data projects are not available for download on your device. However, the all-access annual subscription plan gives you unlimited access to the videos, reusable solution code, datasets, and documentation. They can be accessed 24x7, 365 days a year. All you need is your login credentials and a good internet connection.
How do I start a big data analytics project?
With so many mini-projects on big data out there on the platform, users might sometimes be confused about where should I begin with. We have curated learning paths for students, beginners, and big data professionals that will take away the time and effort on making a decision as to which real-time big data project to get started with learning big data. The big data learning path has been curated to bring forward the best big data based projects using Hadoop, Spark, and other big data tools to streamline the way you master big data skills. However, you can also explore any big data project with the source code that is available in our repository and start any of the big data apache projects that appeal to you. You can go through the solution methodology document of each big data based mini-project to understand the scope before you get started with any real-time project of your choice.
Subscribe today and get started on your big data learning journey through hands-on big data projects for practice.
What are the big data projects using Hadoop for practice?
If you are looking for big data project ideas for your final year project or to test your big data skills as a professional, all our big data projects for practice are categorized based on the specific big data tools they use. Whether you are looking for Hadoop projects for practice or Hadoop projects for beginners or you’re a student looking for a final year Hadoop project- we’ve got you covered with an all-exclusive repository of Hadoop projects with source code.
What are the big data projects using spark for practice?
Whether you are looking for spark projects for practice or Apache Spark projects for beginners or you’re a student looking for a sample spark project to learn big data - we’ve got you covered with an all-exclusive repository of apache spark real-time projects with source code.
What are the pre-requisites to get started working on big data analytics projects?
The project repository of real-time big data analytics projects offers incredible opportunities to find your way into the big data world no matter your previous knowledge and experience. There is no strict prerequisite to get started working on these real-time mini projects on big data.
Does the subscription of big data analytics projects with source code guarantee placements?
We currently do not provide any placements. However, all the big data mini projects with source code are designed using the latest big data tools and technologies that will help you land a top gig as a big data professional making you ready to transition into big data job roles like data engineer, data analyst, business analyst, Hadoop developer, spark developer, Hadoop architect, data architect, and other. Each end-to-end big data project will help you gain some hands-on experience with a novel big data tool making it a worthy mention in your project portfolio. Through these projects, you will be able to maximize your potential and build up your skill set to increase your chances of landing a top big data job.
Can I get assistance from the industry expert who has developed the big data project for practice?
Whenever you are stuck while practicing any big data project or are finding it difficult to understand the workflow of the big data analytics projects, you can reach out to the 24x7 technical support accessible from your dashboard. We also connect you with an industry expert or mentor to provide you guidance for practicing the big data project. You can reach out to our Project Advisors to know more about the one-to-one mentorship to get live help from an experienced industry expert.
What kind of big data skills will I master by working on these data analytics projects?
ProjectPro’s solved end-to-end big data analytics projects cover real-world business use cases across multiple business domains like healthcare, finance, eCommerce, media, and others with solutions implemented using diverse latest big data tools and technologies like Hadoop, Kafka, Spark, PySpark, Kylin, Redis, Zeppelin, Pig, Hive and other novel tools in the industry. The exposure to diverse big data tools through hands-on projects help you build a job-winning data analytics portfolio that any hiring manager will not be able to overlook.