Apache Airflow is an open-source workflow management platform designed to programmatically author, schedule, and monitor workflows. Developed by Airbnb and later incubated by the Apache Software Foundation, Airflow allows users to define tasks and dependencies using Python, providing a dynamic, extensible interface for managing complex pipelines of tasks. It uses directed acyclic graphs (DAGs) to manage workflow orchestration, ensuring that tasks are executed in the right order and at the right time. Airflow is highly scalable, supporting both simple conditional workflows across servers and complex data-driven pipelines, making it a favored choice for data engineers and developers involved in building data processing systems and automated job workflows.
- repository: https://github.com/pytzen/airflow
- wiki: https://github.com/pytzen/airflow/wiki
- conda_env: https://github.com/pytzen/airflow/tree/main/conda_env
Linux, a robust open-source operating system, is a cornerstone of modern software development, renowned for its stability and security. It operates on the principles of the Unix architecture, making it ideal for servers, desktops, and embedded systems alike. Bash scripting, an integral part of the Linux ecosystem, allows users to automate tasks, manage system operations, and efficiently handle administrative tasks. Using Bash, a command-line shell, users can write scripts to execute multiple commands, loop through tasks, and conditionally control the execution flow, enhancing productivity and enabling complex systems management. Together, Linux and Bash scripting form a powerful duo, providing developers and system administrators with the tools to customize, automate, and optimize their computing environments.
- repository: https://github.com/pytzen/linux
- wiki: https://github.com/pytzen/linux/wiki
- docker_env: https://github.com/pytzen/linux/tree/main/docker_env
Go, also known as Golang, is a statically typed programming language developed by Google, designed to be simple, efficient, and effective for modern software development. Emphasizing speed and safety, Go features a clean syntax, garbage collection, and native support for concurrent programming. Its comprehensive standard library and robust tools like gofmt and godoc enhance developer productivity and code quality. Popular among developers for building scalable server-side applications, Go powers significant projects like Docker and Kubernetes, showcasing its capability in handling high-performance computing tasks and complex networked systems.
- repository: https://github.com/pytzen/go
- wiki: https://github.com/pytzen/go/wiki
- docker_env: https://github.com/pytzen/go/tree/main/docker_env
- notebooks: https://github.com/pytzen/go/tree/main/notebooks
Python is a versatile and widely-used programming language known for its readability and straightforward syntax, which makes it particularly appealing for beginners, yet powerful enough for professionals. As an interpreted language, Python enables rapid development and iteration, with a vast standard library that supports a variety of programming paradigms including procedural, object-oriented, and functional programming. It is heavily utilized in web development, data analysis, artificial intelligence, scientific computing, and automation, benefiting from an extensive ecosystem of libraries and frameworks such as Django, Flask, Pandas, and TensorFlow. Python’s community support and continuous evolution ensure it remains a top choice for developers looking to solve complex problems and build scalable applications.
- repository: https://github.com/pytzen/python
- wiki: https://github.com/pytzen/python/wiki
- source code: https://github.com/pytzen/python/tree/main/src
- documentation: https://github.com/pytzen/python/tree/main/docs
Apache Iceberg is an open-source table format designed for handling large-scale analytic datasets more efficiently than traditional row-based formats like those used in Hive. It enables fine-grained and high-concurrency reads and writes, supporting complex nested data structures through its columnar storage format. Iceberg is notable for its robust schema evolution capabilities, allowing for additions, deletions, and updates to table schemas without downtime or performance degradation. It also features snapshot isolation, ensuring consistent data views while changes are being made, and simplifies data management tasks with features like version rollback and incremental processing. This table format integrates seamlessly with popular data processing frameworks such as Apache Spark, Trino, and Flink, making it a versatile choice for modern data architectures.
- repository: https://github.com/pytzen/iceberg
- wiki: https://github.com/pytzen/iceberg/wiki
- conda_env: https://github.com/pytzen/iceberg/tree/main/conda_env
- docker_spark_iceberg: https://github.com/pytzen/iceberg/tree/main/docker_spark_iceberg