20 Best Open Source Big Data Projects to Contribute on GitHub

Explore some of the best open source big data projects you can contribute to on Github and add value to your portfolio with open source contributions.

20 Best Open Source Big Data Projects to Contribute on GitHub
 |  BY ProjectPro

If you are associated with the tech world, you would have unquestionably come across the term “Open-source.” From month-long open-source contribution programs for students to recruiters preferring candidates based on their contribution to open-source projects or tech-giants deploying open-source software in their organization, open-source projects have successfully set their mark in the industry. When any particular project is open-sourced, it makes the source code accessible to anyone. Anyone can freely use, study, modify and improve the project, enhancing it for good. The adaptability and technical superiority of such open-source big data projects make them stand out for community use.

Contributing to an open-source big data project has numerous potential benefits for developers and data scientists, including acquiring new skills, interacting with the community, developing a solid network, and sharpening skillset. Furthermore, excellent open-source contributions can elevate your portfolio and resume to the next level, empowering you to pursue new and promising career avenues in the future.


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According to the 9th annual Future of Open Source Survey, 72-78% percent of the companies participate in open source projects. As per the surveyors, Big data (35 percent), Cloud computing (39 percent), operating systems (33 percent), and the Internet of Things (31 percent) are all expected to be impacted by open source shortly. Following these statistics, big data is set to get bigger with the evolution of open-source projects. 

 

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20 Open Source Big Data Projects To Contribute

There are thousands of open-source projects in action today. This blog will walk through the most popular and fascinating open source big data projects.

open source big data projects

1. Apache Beam

Apache Beam

Source: Google Cloud Platform

Apache Beam is an advanced unified programming open-source model launched in 2016. It derives its name  “Beam” which is from “Batch” + “Stream” from its functionalities for both batch and streaming the parallel processing pipelines for data. To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark, Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc. It even allows you to build a program that defines the data pipeline using open-source Beam SDKs (Software Development Kits) in any three programming languages: Java, Python, and Go.

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Apache Beam has certain features that give an advantage to the user, the primary one being unified batch and streaming APIs with an increased level of abstraction and portability across runtimes. The only pitfalls are lesser transparency and control, the limited scope of performance improvement tricks compared to other Apache APIs, and open bugs.

You can contribute to Apache Beam open-source big data project here: https://github.com/apache/beam

 2. Clickhouse

Clickhouse

Source: Github

Clickhouse is a column-oriented database management system used for the online analytical processing of queries ( also known as OLAP). It allows the creation of tables and databases in runtime, loading data, and running queries without reconfiguring or restarting the server. With reduced disk IO, data locality, and compression, clickhouse is 100-1000x faster than traditional approaches. 

Some of its distinct features include data compression with specialized codecs for excellent performance, disk storage of data, parallel processing on multiple cores, distributed processing on various servers, SQL support, vector computation engine, real-time data updates, adaptive join algorithm, data replication, and data integrity support, role-based access control, etc. 

Companies like Yandex, CloudFare, Uber, eBay, Spotify have preferred Clickhouse owing to its performance, scalability, reliability, and security. On the contrary, the absence of developed transactions, lack of capacity to switch or delete inserted data with a high rate, low latency, and sparse index are the aspects that create a slight backlash for ClickHouse.

Contribute to Clickhouse Open Source project here:https://github.com/ClickHouse/ClickHouse

3. Apache Flink

  Apache Flink

Source: nsfocusglobal.com

Apache Flink is a stateful computation framework. It serves as a distributed processing engine for both categories of data streams: unbounded and bounded. Flink can run in all typical cluster environments, with in-memory speed computations at any scale. Support for stream and batch processing, comprehensive state management, event-time processing semantics, and consistency guarantee for the state are just a few of Flink's capabilities. 

Dynamic messaging, consistent state, multi-language support, cloud-native, no database requirements, and “stateless” operations are some of the fringe benefits provided by Flink. Less community and forums for discussion, lack of excellent API support, and difficult to program data representation are some common drawbacks reported by Flink users. 

To contribute to the Apache Flink open source project, visit: https://github.com/apache/flink

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4. Nvidia RAPIDS

 Nvidia RAPIDS

Source: developer.nvidia.com

The RAPIDS software is built upon the CUDA-X AI. This library package allows you to run end-to-end data science and analytics pipelines exclusively on GPUs. It uses NVIDIA CUDA primitives for basic compute optimization, while user-friendly Python interfaces exhibit GPU parallelism and great bandwidth memory speed. In addition to analytics and data science, RAPIDS focuses on everyday data preparation tasks. This features a familiar DataFrame API that connects with various machine learning algorithms to accelerate end-to-end pipelines without incurring the usual serialization overhead. Multi-node, multi-GPU deployments are also supported by RAPIDS, allowing for substantially faster processing and training on much bigger datasets. Hassle-free integration, top model accuracy, open-source support, and reduced training time are some of the perks offered by RAPIDS.

To contribute to this software library package, visit: https://github.com/rapidsai

5.TDengine

  TDengine

Source: www.taosdata.com

TDengine is an open-source big data platform tailored for IoT, linked automobiles, and industrial IoT. It finds its applications in IT infrastructure, robotics, elevators,  oil/gas extraction, smart homes, the internet of cars, power grids, records of internet access, phone usage, and financial transactions, water, air, and other forms of environmental monitoring, etc. It incorporates caching, stream computing, message queuing, and other functionalities to decrease the complexity and expenses of development and operations, in addition to the 10x quicker time-series database. 1/5 hardware/cloud service costs, full-stack for time-series data, robust data analysis, seamless integration with other tools, zero management, and no learning curve are the significant highlights of TDengine.

To contribute, proceed to: https://github.com/taosdata/TDengine

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6. Apache Spark

 Apache Spark

Source: www.oreilly.com

Apache Spark is an open-source cluster computing framework. It comes with programming interfaces for entire clusters. With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. The bedrock of Apache Spark is Spark Core, which is built on RDD abstraction. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Apache Spark is also quite versatile, and it can run on a standalone cluster mode or Hadoop YARN, EC2, Mesos, Kubernetes, etc. You can also access data through non-relational databases such as Apache Cassandra, Apache HBase, Apache Hive, and others like the Hadoop Distributed File System. Apache Spark can also combine historical and live data to create real-time judgments, ideal for applications like predictive analytics, fraud detection, sentiment analysis, etc.

Hop onto the repository here: https://github.com/apache/spark

7. Presto

   Presto

Source: www.crunchbase.com

Presto is an open-source distributed SQL query engine. It enables the users to run interactive analytic queries for data sources of varied sizes ranging from gigabytes to petabytes. It was built from the ground up for interactive analytics and can scale to the size of Facebook while approaching the speed of commercial data warehouses. Presto allows you to query data stored in Hive, Cassandra, relational databases, and even bespoke data storage. Presto can aggregate data from numerous sources in a single query, allowing you to do analytics across your whole enterprise. It eliminates the false option of adopting an expensive commercial solution for quick analytics or a sluggish "free" alternative that requires a lot of hardware.

Project Aria, Project Presto Unlimited, User Defined Functions, Apache Pinot and Druid Connectors, RaptorX, Presto-on-Spark, Disaggregated Coordinator (a.k.a. Fireball) are some latest innovations in Presto. Some of the disadvantages that the user may face while using Presto maybe its unsuitability for large fact joins and the absence of UDF (User-Defined Functions) Support. 

To learn more about the recent updates and contribute: https://github.com/prestodb/presto

8. Apache Zeppelin

                                                   Apache Zeppelin

Source: Github

Apache Zeppelin is a multi-purpose notebook that supports Data Ingestion, Data Discovery, Data Analytics, Data Visualization, and Data Collaboration. It was built to serve as the front-end web infrastructure for Apache Spark, allowing it to connect with Spark apps without the need for additional modules or plugins. The Zeppelin Interpreter is an excellent feature since it allows you to plug in any data-processing backend to Zeppelin. Spark, Markdown, Python, Shell, and JDBC are all supported by the Zeppelin interpreter. It provides two types of deployments for single and multi-users. The latest novelties in Zeppelin include: Zeppelin SDK, improved Spark Interpreter, improved Flink Interpreter, Yarn Interpreter Mode, Inline Configuration, Interpreter Lifecycle Management

Some of the cons faced by the users include buggy UI, absence of support for some libraries, unfit for production usages, and limited options for visualizations.

To embark on the repository: https://github.com/apache/zeppelin

9. CMAK

CMAK

Source: Github

CMAK stands for Cluster Manager for Apache Kafka, previously known as Kafka Manager, is a tool for managing Apache Kafka clusters. CMAK is developed to help the Kafka community. This project is currently being administered by Verizon Media personnel as well as the community. Some of the attributes of CMAK include management of multiple clusters, easy inspection of the cluster state, running preferred replica election, generating partition assignments with the option to select brokers. It also incorporates key features like running reassignment of partition (based on generated assignments), deleting topic, batch generating partition assignments, batch running reassignment of partition for multiple topics, adding partitions, or updating config for an existing topic. The most significant benefit of using CMAK is that it is an excellent tool for partition reassignment, while on the other hand, its limitation towards Ops tasks can prove to be a drawback. 

Head onto to the repository here: https://github.com/yahoo/CMAK

10. Cython

Cython

Source: Wikipedia

Cython is a static optimizer for the Python programming language. It also works well for the expanded Cython programming language that is based on Pyrex. It makes building  C extensions for Python as trivial as writing Python itself. Cython combines the power of Python with C to allow you to write Python code that can switch back and forth between native C and C++ code at any time. By introducing static type declarations in Python syntax, you can quickly optimize understandable Python code into plain C for performance. Using integrated source code-level debugging, you can identify your Python, Cython, and C code issues. You can build your apps quickly in the CPython ecosystem, which is broad, mature, and extensively used. The Cython programming language can also be referred to as a Python superset that allows you to run C functions and declare C types on variables and class attributes. This enables the compiler to build C code from Cython code that is very efficient. The prime drawback of using Cython is that Cython code can’t be reused independently, unlike pure C and C++ codes that can be used and reused separately from Python. Adding to that, the compiled output provided by Cython will never match the speed as high as hand-tuned C in all cases or even in most cases.

You may advance to the repository here:https://github.com/cython/cython

11.CatBoost

CatBoost

Source: Github

CatBoost is a decision tree gradient boosting machine learning algorithm. Currently, it is available as an open-source library. It was developed by Yandex researchers and engineers and used by Yandex and other organizations such as CERN, Cloudflare, and Careem taxi for search, recommendation systems, personal assistant, self-driving cars, weather prediction, and many other activities. Anyone can use it because it is open-source. Its distinctive features and latest advancements include great quality without parameter tuning, categorical features support, implementation of ordered boosting, fast and scalable GPU version, missing value support, great visualization, improved accuracy and quick predictions. CatBoost is an excellent solution for heterogeneous data problems, but it might not be the best learner for cases that deal with homogenous data. Preprocessing, prediction time, and model analysis are among Catboost's strengths, whereas its training and optimization times constitute its weaknesses.

For documentation and issues, visit: https://github.com/catboost/catboost

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12. Apache CouchDB

Apache CouchDB

Source: idroot.us

The Apache CouchDB database was first released in 2005 by the Apache Software Foundation. Erlang is used to create CouchDB. It's an open-source database that stores, transfers, and processes data in various formats and protocols. It stores data in JSON, executes queries in JavaScript using MapReduce, and provides an API through HTTP. CouchDB is well-suited to current online and mobile applications. Using CouchDB's incremental replication, you may efficiently disseminate your data. CouchDB allows for master-master configurations with automatic conflict detection. CouchDB has several capabilities that make web development more straightforward, such as on-the-fly document transformation and real-time change notifications. It even has a simple web administration panel that is supplied directly from CouchDB! It is a high-availability, partition-tolerant database that is also eventually consistent. It offers a fault-tolerant storage engine that prioritizes data security. Excellent tolerance to overhead, expensive arbitrary queries, prolonged temporary views on large datasets, absence of support for transactions, occasional failure of replication of large databases are some of the shortcomings of CouchDB. 

For more details and contribution guidelines, check out the CouchDB open source repo here: https://github.com/apache/couchdb

13. Apache Airflow

Apache Airflow

Source: airflow.apache.org

Apache Airflow is a programming-based framework for automating authoring, scheduling, and monitoring Beam data pipelines. These Beam data pipelines are dynamic as they are built via programming, and you can use Airflow to create workflows as visualized graphics or directed acyclic graphs (DAGs) of tasks. Airflow also offers a user interface that makes it easy to visualize the pipelines in production, debug any issues that arise, and even track the pipelines' progress. Another benefit of Airflow is that it is extensible, meaning you can build your own operators and extend the library to the desired level of abstraction for your environment. Airflow is also highly scalable, with the company's official website boasting that it can scale indefinitely! No versioning of data pipelines,  unintuitive for new users, configuration overload right from the start, hard to use locally, and its scheduler are some of the bottlenecks faced by the users of Apache Airflow.

To contribute to this open-source project, head onto the link: https://github.com/apache/airflow

14. Trino

Trino

Source: trino.io

Trino is a distributed SQL query engine. It has the potential to query large datasets from numerous heterogeneous data sources. Trino was developed to address data warehousing and analytics, including data analysis, aggregation, and report generation. Online Analytical Processing(OLAP) is a term used to describe these workloads. Trino is a distributed query tool for effectively querying large volumes of data. You're probably utilizing technologies that interact with Hadoop and HDFS if you work with terabytes or petabytes of data. Trino was created as a replacement for tools that query HDFS using MapReduce pipelines, for example, Hive or Pig.  However, Trino is not limited to HDFS access. It can be developed to work with various data sources, including conventional relational databases and alternative data sources like Cassandra. One of Trino's current significant flaws is that if a query uses more memory than the cluster has available, the query will fail. Thanks to the granular fault tolerance capability, the query engine will retry a query to run it successfully rather than failing.

Refer to the Trino Open Source Repository Here: https://github.com/trinodb/trino

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15. Delta Lake

 Delta Lake

Source: Github

Delta Lake is an open-source project that allows you to create a Lakehouse design based on data lakes. On top of existing data lakes like S3, ADLS, GCS, and HDFS, Delta Lake enables ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. The key features of Delta Lake incorporate ACID transactions, scalable metadata handling, time travel (data versioning), open format, unified batch, and streaming source and sink, schema enforcement, schema evolution, audit history, updates and deletes, 100% compatibility with Apache Spark API and, delta Sharing. A considerable number of companies use Delta Lake to process exabytes of data every month. These include Databricks, Viacom, Alibaba group, McAfee, Upwork, eBay, Informatica, and many more. 

To contribute to the project, visit the repository: https://github.com/delta-io/delta

16. Apache Cassandra

Apache Cassandra

Source: Wikipedia

Apache Cassandra is a scalable and high-performance database that can run on commodity hardware or cloud infrastructure and is provably fault-tolerant. It can even replace broken nodes without shutting down the system, and it can automatically replicate data across numerous nodes. Furthermore, Cassandra is a NoSQL database in which all nodes are peers, rather than master-slave architecture. This makes it highly scalable and fault-tolerant, and it allows you to add more new machines without disrupting existing applications. For each update, you can choose between synchronous and asynchronous replication. Cassandra owes its popularity to its distinct features. Big firms like Apple, Netflix, Instagram, Spotify, and Uber deploy it. No Support for ACID Properties, no support for aggregates, latency, joins, data duplication, slow reads, VM memory management are some drawbacks of using Apache Cassandra.

For documentation and contribution insights: https://github.com/apache/cassandra

17. Vespa

Vespa

Source: cloud.vespa.ai

Vespa is a low-latency computing engine for massive data sets. It indexes and stores your data so that queries, selection, and processing can be done on it at serving time. With application components housed within Vespa, the functionality may be customized and enhanced. Vespa empowers application developers to build backend and middleware systems that scale to handle large volumes of data and high loads without compromising latency or reliability. A Vespa instance is made up of several stateless Java container clusters and one or more data-storing content clusters. It allows us to create functionally rich and highly available apps that scale and perform to high standards without burdening with the significant low-level complexity. It enables developers to evolve and grow their applications over time without taking the system offline and avoid complex data and precomputing page schemes that result in stale data that cannot be personalized. This frequently necessitates complex queries to complete in real-time over data that is constantly changing. Vespa finds its applications in many use cases such as Text search, Recommendation, Personalization, Question answering, Semi-structured Navigation, etc.

Checkout the Github repository: https://github.com/vespa-engine/vespa

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18. Apache Calcite

 

Apache Calcite

Source: commons.wikimedia.org

Apache Calcite is a full-stack category tool used for managing dynamic data. It's an open-source database and data management framework. It comes with a SQL parser, an API for creating relational algebra expressions, and a query planning engine. Although it includes many of the components that make up a standard database management system, it still lacks several crucial features, such as data storage, data processing methods, and a metadata repository.  Calcite has chosen to stay out of the data storage and processing business.  This makes it a great candidate for bridging the gap between applications and one or more data storage and processing engines. It's also a great starting point for creating a database: all you have to do is add data. Pros of using Calcite include query parser, validator, optimizer, aid for reading models in JSON format, numerous standard functions, aggregate functions, JDBC queries for Linq4j, JDBC back-ends, Linq4j front-end and SQL features.

To contribute to this project, hop onto: https://github.com/apache/calcite

19.DataHub

DataHub

Source: Github

DataHub is a modern data stack's open-source metadata platform of the third generation. It handles upwards of ten million entity-relationship change events per day and indexes over five million entities and relationships in aggregate. This is done alongside serving operational metadata queries with millisecond-level SLAs thus, enabling data efficiency, compliance, and governance workflows. DataHub is a modern data catalog that allows end-to-end data discovery, data observability, and data governance. This extensible metadata platform is designed to assist developers in taming the complexity of their rapidly evolving data ecosystem and for data practitioners to leverage the maximum value of data within their organizations. 

LinkedIn uses DataHub to deploy datasets, schemas, streams, compliance annotations, GraphQL endpoints, metrics, dashboards, features, and AI models, making it genuinely third-generation in terms of proven scale and battle readiness.

Walkthrough the repository here: https://github.com/linkedin/datahub

20. Koalas

Koalas

Source: Github

The Koalas project implements the pandas DataFrame API on top of Apache Spark, making data scientists more productive when dealing with huge data. Spark is the de facto standard for large data processing, while pandas is the de facto standard (single-node) DataFrame implementation in Python. If you're already familiar with pandas, you can use Spark right away with no learning curve. Using Koalas gives the users the option of having a single codebase that one can use with pandas to test smaller datasets and Spark for larger and distributed datasets. The Koalas open-source project has progressed significantly. The pandas API coverage in Koalas was roughly 10%–20% at the time of launch. Thanks to community contributions over several frequent releases, the pandas API coverage rapidly increased and is now close to 80% in Koalas 1.0. Better pandas API coverage, spark accessor, better type hint support, broader plotting support, more comprehensive support of in-place update, better support of missing values, NaN, and NA are some of the latest advancements of Koalas 1.0.

Access the repository here: https://github.com/databricks/koalas

How to Contribute to Open Source Big Data Projects?

So, finally, now you have thought about contributing to an open-source big data project on GitHub but are confused by the process to give it a try. ProjectPro experts suggest that you begin contributing to an open-source big data repository with an attempt to first make a contribution to the documentation followed by code. To contribute to a big data project documentation you can try to find a broken link or a typo to fix. As such documentation contributions are pretty simple, you will be able to understand and learn the entire workflow of contributing to an open-source big data project through this. You can also browse through existing issues of various open-source projects on big data and see if you can solve any issues and make contributions towards the same. Or you contribute by opening a new issue with your proposal.

All of these open source projects together contribute to making significant advancements in the development of big data. The impact of the contributions made to these open-source projects on the tech community is incredible. However, it also drives another remarkable aspect: these open-source projects collectively shift the industry away from proprietary software towards open-source software. Consequently, any company, business, or organization, irrespective of its size, whether big or small, leverages these open source projects to enhance their day-to-day operations using big data. 

 

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