Top Hadoop Projects and Spark Projects for Beginners 2024

Hadoop and Spark have made big data analysis easier and faster through Hadoop based real-time projects and Spark real-time projects.

Top Hadoop Projects and Spark Projects for Beginners 2024
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Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. Apache Hadoop and Apache Spark fulfill this need as is quite evident from the various projects that these two frameworks are getting better at faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. These Apache Spark projects are mostly into link prediction, cloud hosting, data analysis, and speech analysis. These projects are proof of how far Apache Hadoop and Apache Spark have come and how they are making big data analysis a profitable enterprise.


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As we step into the latter half of the present decade, we can’t help but notice the way Big Data has entered all crucial technology-powered domains such as banking and financial services, telecom, manufacturing, information technology, operations, and logistics.

With Big Data came a need for programming languages and platforms that could provide fast computing and processing capabilities. The parallel emergence of Cloud Computing emphasized distributed computing and there was a need for programming languages and software libraries that could store and process data locally (minimizing the hardware required to maintain high availability). Apache™, an open-source software development project, came up with open-source software for reliable computing that was distributed and scalable.

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Hadoop and Spark are two solutions from the stable of Apache that aim to provide developers around the world a fast, reliable computing solution that is easily scalable. Built to support local computing and storage, these platforms do not demand massive hardware infrastructure to deliver high uptime. At the bottom lies a library that is designed to treat failures at the Application layer itself, which results in highly reliable service on top of a distributed set of computers, each of which is capable of functioning as a local storage point.

Why Apache Spark?

Owned by Apache Software Foundation, Apache Spark is an open-source data processing framework. It sits within the Apache Hadoop umbrella of solutions and facilitates the fast development of end-to-end Big Data applications.  It plays a key role in streaming in the form of Spark Streaming libraries,  interactive analytics in the form of SparkSQL and also provides libraries for machine learning that can be imported using Python or Scala.

It is an improvement over Hadoop’s two-stage MapReduce paradigm. By providing multi-stage in-memory primitives, Apache Spark improves performance multi-fold, at times by a factor of 100! It can interface with a wide variety of solutions both within and outside the Hadoop ecosystem.

Apache Spark is the current buzzword in the field of analytics and, more specifically, Big Data Analytics. Spark technology is 100 times faster than the traditional Hadoop technology for processing large amounts of data. Apache Spark uses in-memory storage and computing capabilities as its niche to give users the power to handle petabytes of complex data.

Hadoop Projects and Spark Projects

Apache has gained popularity around the world and there is a very active community that is continuously building new solutions, sharing knowledge, and innovating to support the movement. A number of times developers feel they are working on a really cool project but in reality, they are doing something that thousands of developers around the world are already doing. The aim of this article is to mention some very common projects involving Apache Hadoop and Apache Spark.

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1. Data Migration

RDBMSs were inefficient and failed to manage the growing demand for current data. This failure of relational database management systems triggered organizations to move their data from RDBMS to Hadoop. Data migration from legacy systems to the cloud is a major use case in organizations that have been into relational databases. Being open-source Apache Hadoop and Apache Spark has been the preferred choice of a number of organizations to replace the old, legacy software tools which demanded a heavy license fee to procure and a considerable fraction of it for maintenance. Unlike years ago, open-source platforms have a large talent pool available for managers to choose from who can help design better, more accurate, and faster solutions. Hadoop ecosystem has a very desirable ability to blend with popular programming and scripting platforms such as SQL, Java, Python, and the like which makes migration projects easier to execute.

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2. Data Integration

Businesses seldom start big. Most of them start as isolated, individual entities and grow over a period of time. The Digital explosion of the present century has seen businesses undergo exponential growth curves. Given the operation and maintenance costs of centralized data centers, they often choose to expand in a decentralized, dispersed manner. Given the constraints imposed by time, technology, resources, and talent pool, they end up choosing different technologies for different geographies and when it comes to integration, they find going tough.

That is where Apache Hadoop and Apache Spark come in. Given their ability to transfer, process, and store data from heterogeneous sources in a fast, reliable, and cost-effective manner, they have been the preferred choice for integrating systems across organizations.

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3.Scalability

As mentioned earlier, scalability is a huge plus with Apache Spark. Its ability to expand systems and build scalable solutions in a fast, efficient, and cost-effective manner outsmart a number of other alternatives. Apache Spark has been built in a way that it runs on top of the Hadoop framework (for parallel processing of MapReduce jobs). As the data volumes grow, processing times noticeably go on increasing which adversely affects performance. Hadoop can be used to carry out data processing using either the traditional (map/reduce) or Spark-based (providing an interactive platform to process queries in real-time) approach.

4.Link Prediction

Link prediction is a recently recognized project that finds its application across a variety of domains – the most attractive of them being social media. Given a graphical relation between variables, an algorithm needs to be developed which predicts which two nodes are most likely to be connected? This can be applied in the financial services industry – where an analyst is required to find out which are the kinds of frauds a potential customer is most likely to commit? It can also be applied to social media where the need is to develop an algorithm which would take in a number of inputs such as age, location, schools, and colleges attended, workplace and pages liked friends can be suggested to users.

Given Spark’s ability to process real-time data at a greater pace than conventional platforms, it is used to power a number of critical, time-sensitive calculations and can serve as a global standard for advanced analytics.

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5.Cloud Hosting

Apache Hadoop is equally adept at hosting data at on-site, customer-owned servers, or in the Cloud. Cloud deployment saves a lot of time, cost, and resources. Organizations are no longer required to spend over the top for procurement of servers and associated hardware infrastructure and then hire staff to maintain it. Instead, cloud service providers such as Google, Amazon, and Microsoft provide hosting and maintenance services at a fraction of the cost. Cloud hosting also allows organizations to pay for actual space utilized whereas in procuring physical storage, companies have to keep in mind the growth rate and procure more space than required.

6.Specialized Data Analytics

Organizations often choose to store data in separate locations in a distributed manner rather than at one central location. Besides risk mitigation (which is the primary objective on most occasions) there can be other factors behind it such as audit, regulatory, advantages of localization, etc.

It is only logical to extract only the relevant data from warehouses to reduce the time and resources required for transmission and hosting. For example, in financial services, there are a number of categories that require fast data processing (time series analysis, risk analysis, liquidity risk calculation, Monte Carlo simulations, etc.).

Hadoop and Spark facilitate faster data extraction and processing to give actionable insights to users. Separate systems are built to carry out problem-specific analysis and are programmed to use resources judiciously.

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7.Streaming Analytics

To set the context, streaming analytics is a lot different from streaming. Streaming analytics is a real-time analysis of data streams that must (almost instantaneously) report abnormalities and trigger suitable actions. For example, when an attempted password hack is attempted on a bank’s server, it would be better served by acting instantly rather than detecting it hours after the attempt by going through gigabytes of server log!

Streaming analytics requires high-speed data processing which can be facilitated by Apache Spark or Storm systems in place over a data store using HBase. Streaming analytics is not a one-stop analytics solution, as organizations would still need to go through historical data for trend analysis, time series analysis, predictive analysis, etc.

8.Speech Analysis

Computer Telephone Integration has revolutionized the call center industry. Speech analytics is still in a niche stage but is gaining popularity owing to its huge potential. Consider a situation where a customer uses foul language, words associated with emotions such as anger, happiness, frustration, and so on are used by a customer over a call. Instead of someone having to go through huge volumes of audio files or relying on the call handling executive to flag the calls accordingly, why not have an automated solution? Hadoop and Spark excel in conditions where such fast-paced solutions are required. This reduces manual effort multi-fold and when an analysis is required, calls can be sorted based on the flags assigned to them for better, more accurate, and efficient analysis.

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Big Data Sample Apache Spark Projects with Source Code

With the growing demand for Apache Spark and its associated technologies, it becomes quite essential to know and understand the different types of real-world projects that have been solved by using them. In this section we will walk you through 10 Big Data Analytics Projects using Apache Spark. The spark project ideas stated here are business use cases that have been solved by various companies spanning across industries like Airline, E-Commerce, and Fintechs, to name a few. The readers here will also get to know the tech stack used for the implementation of these big data projects using Spark.

Apache Big Data Project Using Spark #1:

Job and Server Management

Job and Server Management

   Image Source:manageengine.com

Business Use Case: The business case here is to enable easy Spark Server and Job Management.

Objective and Summary of the Apache Spark Project Idea: As and when applications are used they start deploying never-ending jobs which include jars, logs, contexts, and job results. Each job here has its own set of parameters and interface which makes the server management even more complex. With the introduction of Apache Spark technology, this entire process has been simplified using its Restful API and open-source environment. It also allows job submissions from multiple environments and languages within no time.

Tools/Tech stack used: The tools and technologies used for efficient Job and Server Management using Apache Spark are Rest API, Ruby scripting, Scala, and Java.

Apache Big Data Project Using Spark #2:

Predicting Flight Delays

Predicting Flight Delays

   Image Source:medium.com

Business Use Case: The business case here is to forecast delays of various airlines.

Objective and Summary of the project: This is one of the most known problems that need to be solved for the airline industry. While this problem can be solved using various machine learning algorithms as well but with an increase of data, there might be limitations for the typical models in use. To solve the problem, Spark is used for doing descriptive and predictive analysis on huge datasets. This method is also capable of handling the ever-growing increase in data from the airline industry with better accuracy. 

Tools/Tech stack used: The tools and technologies used for such analytics and predictions using Apache Spark are Spark MLib, Kylin, RESTful API, and Cube.

Apache Big Data Project Using Spark #3:

Data Pipeline Management

Data Pipeline Management

   Image Source:sarasanalytics.com

Business Use Case: The business case here is to streamline data pipeline management for various industries with huge datasets.

Objective and Summary of the project: Data Pipeline management involves various activities which include ingestion and the entire ETL process. ETL here stands for Extraction of data from the source, Transformation of data into a readable and understandable format, and Loading of data into the data warehouse. With Apache Spark, one can easily simulate all these activities, creating various events for the entire process. It is also very easy to test and troubleshoot with Spark at each step.

Tools/Tech stack used:  The tools and technologies used for such data pipeline management using Apache Spark are NoSQL, API, ETL, and Python.

Apache Big Data Project Using Spark #4:

Data Hub Creation

Data Hub Creation

 Image Source:actian.com

Business Use Case: The business case here is to create a data hub for easy consolidation of data.

Objective and Summary of the project: With so much use of online applications the inflow of data has increased exponentially. To manage all the information it is quite essential to have something like a data hub or data lake for easy access of the same. This problem has been easily addressed by Apache Spark and the application of MapReduce where data from various sources can easily be integrated and used widely.

Tools/Tech stack used: The tools and technologies used for data hub creation using Apache Spark are MapReduce, Hive, HDFS, and Ipython.

Apache Big Data Project Using Spark #5:

E-commerce analytics

Image Source:ecommerce-platforms.com

Business Use Case: The business case here is to handle the complexity of Ecommerce Analytics.

Objective and Summary of the project: This is one industry where Big Data has taken a lot of prominence over the past few years. With the dynamic environment of the E-commerce industry where there are real-time transactions and product reviews, it becomes quite challenging to manage data and analytics for them. With Apache Spark and Machine Learning algorithms, this use case of unstructured data has been solved easily.

Tools/Tech stack used: The tools and technologies used for such e-commerce analytics using Apache Spark are Spark MLib, K-Means, Text Analytics, and other Clustering algorithms.

Apache Big Data Project Using Spark #6:

Build a Real-Time Dashboard with Spark, Grafana, and InfluxDB

Time-series based dashboards are a creative tool for analysing business performance. They utilise time-series data to inspect website traffic, demographic data, IT operations, pricing fluctuations, and the user clicks since all these parameters depend on time. Values for all these parameters are collected and stored every short interval of time. The database thus keeps on increasing in size rapidly and needs to be analysed for drawing insightful conclusions that can assist in accelerating a business’ growth. In this project, you will work on preparing a real-time analytics dashboard using popular Big Data tools.

Data Description

The dataset for this project is of two types: batch data and stream data.

The batch data has 100,000 auto-generated user demographic data points, including the following columns:

  • Id
  • Age
  • Gender
  • State
  • Country

While the stream data is based on user purchase events and is produced every 1 second along with a timestamp when joined with batch data. This data includes the following details:

  • Id
  • campaignID
  • orderID
  • total_amount
  • units
  • tags- click/purchased

Language Used - Java8, SQL

Services- Kafka, Spark Streaming, MySQL, influxDB, Grafana, Docker, Maven

Source Code-  Build a Real-Time Dashboard with Spark, Grafana, and InfluxDB

 

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