Data engineering roadmap

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 con

This project is a collection of specialized study guides and roadmaps centered on computer science, data engineering, and machine learning fundamentals. It provides a structured curriculum of technical competencies, tools, and skills required to transition into professional data engineering roles. The project features a data engineering skill map that visually organizes databases, processing architectures, and infrastructure tools. It also includes a machine learning learning path covering supervised and unsupervised learning techniques alongside model operations. The curriculum covers broad

The data engineering wiki is a crowdsourced knowledge base and reference guide assembled through collaborative contributions from practitioners. It functions as a structured repository of learning paths, architectural decision guides, and software evaluations for data systems, compiled from plain-text source markup files into a searchable static documentation site. The content is organized into strict conceptual hierarchies covering core engineering concepts, security and governance, and infrastructure tools. Contributors and readers can explore foundational architectural patterns, storage s

This project is a comprehensive, community-driven knowledge base designed to support individuals pursuing careers in data engineering. It functions as a centralized learning hub that aggregates industry best practices, technical documentation, and educational resources to assist with both professional development and the design of robust data pipeline architectures. The repository distinguishes itself by providing a structured technical career roadmap that includes curated learning paths, interview preparation strategies, and practical project examples. By indexing a diverse range of media—in

Cookbook is a comprehensive knowledge base and reference repository for data engineering. It serves as a centralized directory for data architecture patterns, professional career roadmaps, and a curated collection of public datasets. The project provides a structured guide for transitioning into specialized data engineering roles through skill-matrix mapping and technical interview preparation. It further distinguishes itself by documenting real-world industry case studies and decomposing large-scale industrial implementations into repeatable architectural patterns. The repository covers a b

Data warehouse learning is a reference implementation of a real-time stream processing system and open-source data lakehouse architecture. It combines stream processing engines, open lakehouse formats, and analytical data warehouses into a complete e-commerce data warehouse system built for both offline and real-time analytics pipelines. The project implements hybrid data warehouse architectures utilizing multi-layer storage models and stream-batch processing pipelines. It features change data capture pipelines that stream database transaction logs into messaging systems, progressive data tra

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.

This project is an educational resource and technical manual for Apache Spark, focused on the architecture and practical application of large-scale data processing. It serves as a guide for big data engineering and distributed computing, covering the principles of parallel processing and fault-tolerant data distribution. The material provides instructional content on designing distributed ETL pipelines and implementing data analysis workflows. It includes tutorials for polyglot data processing, offering patterns and examples for using Python, Scala, and Java within a unified environment. The

Orchest is a data pipeline orchestrator and containerized workflow manager. It provides a platform for designing, scheduling, and executing complex data processing sequences through a combination of a graphical interface and scripting. The platform distinguishes itself by using containers to manage software dependencies, ensuring consistent execution across different environments. It features a polyglot task scheduler capable of triggering jobs written in multiple programming languages and includes a version control system that tracks historical snapshots of project configurations and code.

Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro