Top 4 Interesting Big Data Projects In GitHub For Beginners [2024]

For years, GitHub has been a hands-down online community of developers and technicians who come up with out-of-the-box projects across all verticals, provide roadmaps to multiple issues, etc. Today, GitHub has become this massive online repository for the big data community; that’s a great way to hone technical skills. Currently, the big data industry’s biggest challenge is the sheer dynamism of the market and its requirements.

Therefore, if you want to get a good headstart into setting yourself as a differentiator, there are multiple big data projects on GitHub that can work just right. These projects are known for their signature usage of open-source data and implementation in real-life that can be taken as it is or tweaked according to your project objectives. If NoSQL databases like MongoDB, Cassandra have been your forte, work on Hadoop Cluster management’s fundamentals, stream-processing techniques, and distributed computing. 

The point is that Big Data is one of the most promising industries of the current times as people are waking up to the fact that data analysis can promote sustainability in the coming years when done right. As demanding as it gets, for a big data/data science professional, starting with Hadoop projects on GitHub can be an excellent way to grow along with the industry requirements and develop a stronghold over the basics. In this post, we’d be covering such big data projects on GitHub so far:

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Big Data Projects in GitHub

1. Pandas Profiling

The pandas profiling project aims to create HTML profiling reports and extend the pandas DataFrame objects, as the primary function df.describe() isn’t adequate for deep-rooted data analysis. It uses machine learning and pandas data frame to find the unique, correlated variables and quick data analysis. 

The report generated would be in HTML format, and here it would compute data using Histogram, Spearman, Pearson, and Kendall matrices to break down the massive datasets into meaningful units. It supports Boolean, Numerical, Date, Categorical, URL, Path, File, and Image types of abstraction as an effective data analysis method.

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2. NiFi Rule Engine Processor 

The Apache NiFi, also known as NiagraFiles, is known for automating the data stream between various software systems. This project is designed to apply predefined rules on data to streamline the data flow.

It makes use of Drools – a Business Rules Management System (BRMS) solution that is known to provide a core Business Rules Engine (BRE), a web authoring-cum-rules management platform (Drools Workbench), and an Eclipse IDE plugin. The contributors – Matrix BI Limited, have come up with unique rules written entirely in Java, making it a handy big data project on GitHub.

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3. TDengine

This project is one of those that is entirely about the Internet of Things (IoT) and IoT-based applications. It revolves around creating an open-source big data interface programmed for the overall IT infrastructure to track it 10x faster than any other consortium. It would also be equipped with data caching, data stream processing, message queuing for decreasing the data complexity, and more. 

A promising breakthrough in the field of databases, this platform can retrieve more than ten million data points in just a second – without any integration of any other software like Kafka, Spark, or Redis. The data collected can also be analyzed in terms of time, multiple time streams, or a bit of both. Frameworks like Python, R, Matlab powers this heavy-duty database that’s otherwise pretty easy to install with the set of a few tools like Ubuntu, Centos 7, Fedora, etc.

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4. Building Apache Hudi from Source

This project can be a blessing for those looking for faster data indexing, publishing, and data management without any limitations. Apache Hudi (meaning Hadoop Upserts Deletes and Incrementals) can save you a lot of time, worry, and work as it looks after storing and handling bulk analytical datasets on the DFS. 

In general, Hudi is compatible with three different types of queries:

• Snapshot queries can supply snapshot queries based on real-time data with column and row-based data arrangement. 
• An incremental query can help allocate a change stream if the data is inserted or updated past period. 
• Read optimized query may give you all the details on the snapshot query performance with any column-based storage like Parquet.

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Also Read: Difference Between Data Science & Big Data

Conclusion

You can build Apache Hudi with Scala both with and without the spark-avo module as long as you use a spark-shade-unbundle-avro profile. You’d also need a Unix-like system like Linux or Mac OS X, Java 8, Git, and Maven.

As we have discussed in this article, the vision for big data has come a long way, and there is still a vast ground left to cover, going forward. With this progression rate, we can hope that big data would make major developments across all verticals in the coming years.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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