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One must know the tools, techniques, and the best practices adopted when dealing with this data-oriented position since changes are constantly taking place within this field. GitHub due to being a collaboration and innovation hub provides many repositories that can suit the needs of data engineers. Here, you will find the list of ten GitHub repositories, which every data engineer should subscribe to in order to update their knowledge and become even more proficient in their field.
Apache Spark is a popular tool for working with large amounts of data and is currently being actively utilized. This repository provides the download of the source code, documentation, and other things submitted by members of the Spark community. Thus, subscribing to this repository will help you be aware of the recent changes that have occurred in Spark and any improvements made to it.
Apache Airflow is an open-source platform for managing pipelines to process data more efficiently. By following the Airflow repository, one is updated on new features, bug fixes, and improvements made by the community to data workflows.
Pandas is a fast and efficient open-source library for data analysis commonly used in Python. The official site offers sources that contain information about the development of this tool, including documentation and discussions. Subscribing to this repository is helpful for following new features, optimizations, and recommendations when it comes to comprehending tabular data.
Docker as technology has also upended the traditional packaging and deployment of software and data engineering applications. The Docker repository is helpful in indicating the latest released Docker updates, improvements, and other resources contributed by the Docker community, thus allowing for the efficient containerization of data workflows.
Kubernetes is known as the container orchestration layer, which helps manage containers and the applications that run on them. Subscribing to the official Kubernetes repository enables one to learn when new features are added to Kubernetes, how bugs are fixed, and the recommended approaches to using Kubernetes to host data-intensive applications.
Apache Kafka is a distributed message streaming platform for building real-time applications to stream data on distributed systems. With the Kafka repository tracked, a user can keep abreast of its continued evolution and optimization for improved performance, as well as various features developed by the community. Thus, the user can assist in using Kafka in data engineering projects whenever necessary.
Unlike other comprehensive high-level libraries for numerical and scientific computing, scikit-learn is designed explicitly for large-scale modelings and analyses in machine learning. Suppose you aim to use the Scikit-learn library to develop your new machine-learning models, algorithms, or optimizations. In that case, you can follow the Scikit-learn repository to learn about new features in detail.
TensorFlow is an open-source platform utilized in applying machine learning options engineered by Google. The TensorFlow GitHub page offers further access to TensorFlow source codes and documents together with resources developed by the community for the TensorFlow improvement. Subscribing to this repository will enable you as a user to easily track a new feature, improve performance, and learn lessons on how to architect and deploy scalable machine learning models.
Data Engineering Weekly is a weekly collection of articles, tutorials, and resources related to data Engineering. By subscribing to this repository, one will always be in touch with the new trends, methods, and standards within the data engineering specialism, allowing for constant growth.
This is a list of big data tools, frameworks, and resources for use in big data applications, which are categorized as excellent. FOLLOWING this repository will let you get all the information regarding open source big data engineering projects, libraries, and tutorials that can keep you updated about new tools and techniques for enhancing your data engineer skill set.
In conclusion, it is therefore recommended that Data Engineers conform to these 10 GitHub repositories to enhance their understanding of the current data engineering tools, methods, and standards. Regardless of the areas you are going to engage yourself in – big data processing, workflows, ML, or even containerization, you are to get access to the actually priceless materials and resources to become ready for your professional career and solve the data engineering tasks in the best way possible.
