12 Exciting Hadoop Project Ideas & Topics For Beginners [2020]

Hadoop Project Ideas & Topics

Today, big data technologies power diverse sectors, from banking and finance, IT and telecommunication, to manufacturing, operations and logistics. Most of the Hadoop project ideas out there focus on improving data storage and analysis capabilities. With Apache Hadoop frameworks, modern enterprises can minimize hardware requirements and develop high-performance distributed applications.

Read: Apache Spark vs Hadoop Mapreduce

Introducing Hadoop

Hadoop is a software library designed by the Apache Foundation to enable distributed storage and processing of massive volumes of computation and datasets. This open-source service supports local computing and storage can deal with faults or failures at the application layer itself. It uses the MapReduce programming model to bring the benefits of scalability, reliability, and cost-effectiveness to the management of large clusters and computer networks. 

Why Hadoop projects

Apache Hadoop offers a wide range of solutions and standard utilities that deliver high throughput analysis, cluster resource management, and parallel processing of datasets. Here are some of the modules supported by the software:

  • Hadoop MapReduce
  • Hadoop Distributed File System or HDFS
  • Hadoop YARN

Note that technology companies like Amazon Web Services, IBM Research, Microsoft, Hortonworks, and many others deploy Hadoop for a variety of purposes. It is an entire ecosystem replete with features that allow users to acquire, organize, process, analyze, and visualize data. So, let us explore the system tools through a set of exercises. 

Hadoop Project Ideas For Beginners

1. Data migration project

Before we go into the details, let us first understand why you would want to migrate your data to the Hadoop ecosystem.

Present-day managers emphasize on using technological tools that assist and improve decision-making within dynamic market environments. While legacy software like a relational database management system (RDBMS) help store and manage data for business analysis, they pose a limitation when a more substantial amount of data is involved.

It becomes challenging to alter tables and accommodate big data with such traditional competencies, which further affects the performance of the production database. Under such conditions, smart organizations prefer the toolsets offered by Hadoop. Its powerful commodity hardware can significantly capture insights for massive pools of data. This is particularly true for operations like Online Analytical Processing or OLAP. 

Now, let us see how you can migrate RDBMS data to Hadoop HDFS. 

You can use Apache Sqoop as an intermediate layer to import data from a MySQL to the Hadoop system, and also to export data from HDFS to other relational databases. Sqoop comes with Kerberos security integration and Accumulo support. Alternatively, you can use the Apache Spark SQL module if you want to work with structured data. Its fast and unified processing engine can execute interactive queries and streaming data with ease. 

2. Corporate data integration

When organizations first replace centralized data centers with dispersed and decentralized systems, they sometimes end up using separate technologies for different geographical locations. But when it comes to analytics, it makes sense for them to want to consolidate data from multiple heterogeneous systems (often from different vendors). And herein comes the Apache Hadoop enterprise resource with its modular architecture. 

For example, its purpose-built data integration tool, Qlick (Attunity), helps users configure and execute migration jobs via a drag-and-drop GUI. Additionally, you can freshen up your Hadoop data lakes without hindering the source systems. 

Check out: Java Project Ideas & Topics for Beginners

3. A use case for scalability 

Growing data stacks mean slower processing times, which hampers the procedure of information retrieval. So, you can take up an activity-based study to reveal how Hadoop can deal with this issue. 

Apache Spark—running on top of the Hadoop framework to process MapReduce jobs simultaneously—ensures efficient scalability operations. This Spark-based approach can help you to get an interactive stage for processing queries in near real-time. You can also implement the traditional MapReduce function if you are just starting with Hadoop. 

4. Cloud hosting

In addition to hosting data on on-site servers, Hadoop is equally adept at cloud deployment. The Java-based framework can manipulate data stored in the cloud, which is accessible via the internet. Cloud servers cannot manage big data on their own without a Hadoop installation. You can demonstrate this Cloud-Hadoop interaction in your project and discuss the advantages of cloud hosting over physical procurement. 

5. Link prediction for social media sites

The application of Hadoop also extends to dynamic domains like social network analysis. In such advanced scenarios where variables have multiple relationships and interactions, we require algorithms to predict which nodes could be connected. Social media is a storehouse of links and inputs, such as age, location, schools attended, occupation, etc. This information can be used to suggest pages and friends to users via graph analysis. This process would involve the following steps:

  • Storing nodes/edges in HBase
  • Aggregating relevant data 
  • Returning and storing intermediate results back to HBase
  • Collecting and processing parallel data in a distributed system (Hadoop)
  • Network clustering using k-means or MapReduce implementations

You can follow a similar method to create an anomaly predictor for financial services firms. Such an application would be equipped to detect what types of potential fraud particular customers could commit. 

6. Document analysis application

With the help of Hadoop and Mahout, you can get an integrated infrastructure for document analysis. The Apache Pig platform matches the needs, with its language layer, for executing Hadoop jobs in the MapReduce and achieving a higher-level abstraction. You can then use a distance metric to rank the documents in text search operations. 

7. Specialized analytics

You can select a project topic that addresses the unique needs of a specific sector. For instance, you can apply Hadoop in the Banking and Finance industry for the following tasks:

  • Distributed storage for risk mitigation or regulatory compliance
  • Time series analysis
  • Liquidity risk calculation
  • Monte Carlo simulations 

Hadoop facilitates the extraction of relevant data from warehouses so that you can perform a problem-oriented analysis. Earlier, when proprietary packages were the norm, specialized analytics suffered challenges related to scaling and limited feature sets. 

8. Streaming analytics

In the fast-paced digital era, data-driven businesses cannot afford to wait for periodic analytics. Streaming analytics means performing actions in batches or a cyclical manner. Security applications use this technique to track and flag cyber attacks and hacking attempts. 

In the case of a small bank, a simple combination of Oracle and VB code could run a job to report abnormalities and trigger suitable actions. But a statewide financial institution would need more powerful capabilities, such as those catered by Hadoop. We have outlined the step-by-step mechanism as follows:

  • Launching a Hadoop cluster
  • Deploying a Kafka server
  • Connecting Hadoop and Kafka
  • Performing SQL analysis over HDFS and streaming data 

Read: Big Data Project Ideas & Topics 

9. Streaming ETL solution

As the title indicates, this assignment is about building and implementing Extract Transform Load (ETL) tasks and pipelines. The Hadoop environment contains utilities that take care of Source-Sink analytics. These are situations where you need to Capture streaming data and also warehouse it somewhere. Have a look at the tools below.

  • Kudu
  • HDFS
  • HBase
  • Hive

10. Text mining using Hadoop

Hadoop technologies can be deployed for summarizing product reviews and conducting sentiment analysis. The product ratings given by customers can be classified under Good, Neutral, or Bad. Furthermore, you can bring slangs under the purview of your opinion mining project and customize the solution as per client requirements. Here is a brief overview of the modus operandi:

  • Use a shell and command language to retrieve HTML data
  • Store data in HDFS
  • Preprocess data in Hadoop using PySpark
  • Use an SQL assistant (for example, Hue) for initial querying
  • Visualize data using Tableau

11. Speech analysis 

Hadoop paves the way for automated and accurate speech analytics. Through this project, you can showcase the telephone-computer integration employed in a call center application. The call records can be flagged, sorted, and later analyzed to derive valuable insights. A combination of HDFS, MapReduce, and Hive combination works best for large-scale executions. Kisan Call Centers operating across multiple districts in India form a prominent use case.  

12. Trend analysis of weblogs

You can design a log analysis system capable of handling colossal quantities of log files dependably. A program like this would minimize the response time for queries. It would work by presenting users’ activity trends based on browsing sessions, most visited web pages, trending keywords, and so on. 

Also read: How to Become a Hadoop Administrator

Conclusion

With this, we have covered the top Hadoop project ideas. You can adopt a hands-on approach to learn about the different aspects of the Hadoop platform and become a pro at crunching big data!

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