Data Engineering

Roadmap to becoming a data engineer in 2021

This project is an open-source educational curriculum designed to provide comprehensive training in data engineering. It focuses on building scalable data pipelines and managing cloud-native infrastructure through a structured, self-paced program that combines technical explanations with hands-on practical exercises. The curriculum distinguishes itself by emphasizing industry-standard methodologies, specifically teaching students how to implement infrastructure as code and manage data workflows through orchestration tools. By utilizing container-based environment isolation and declarative configuration, the program ensures that learners gain experience with reproducible deployments and consistent development environments across distributed systems. The training covers a broad range of technical topics, including the design of automated data processing tasks and the configuration of cloud resources. The materials are organized into modular, progressive units that build foundational knowledge before advancing to complex engineering workflows. The course materials are hosted in a centralized repository, which facilitates community-supported updates and collaborative improvements to the educational assets.

Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It orchestrates complex sequences of containerized tasks by executing them as directed acyclic graphs, allowing for dependency management and parallel processing within a cluster. The system extends the native Kubernetes control plane to manage the full lifecycle of automated processes, from initial triggering to final resource cleanup. The platform distinguishes itself through its controller-pattern reconciliation, which continuously monitors workflow states to align them with desired configurations. It supports event-driven execution, enabling workflows to trigger based on external signals or time-based schedules. Users can define reusable operational patterns through a centralized template management system, ensuring consistency across distributed environments. The engine provides a comprehensive suite of tools for managing multi-step pipelines, including sidecar-based artifact management for data transfer between steps and external storage providers. It includes built-in administrative interfaces for visualizing execution progress, monitoring performance metrics, and enforcing security through standard authentication and authorization protocols. The system is designed to handle diverse operational requirements, ranging from automated batch processing and data engineering to infrastructure maintenance and software delivery pipelines.