Data Engineering Learning 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 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.

This project is a learning curriculum and programming guide for Apache Spark, providing a structured set of educational resources and practical code examples for mastering distributed data processing. It serves as a course for building scalable data workflows and big data engineering pipelines. The repository provides practical source code and project layouts that demonstrate how to connect external data stores, process streaming data, and organize code for distributed environments. It includes implementation examples for scaling machine learning algorithms across clusters to handle large training datasets. The content covers the development of data workflows, the integration of external storage systems, and the process of compiling and packaging source code into executable assemblies for cluster deployment.

hello-sql is a collection of educational resources and practical guides designed for mastering relational database design, SQL query writing, and schema mapping. It provides a set of lessons and exercises for practicing the creation and manipulation of data within relational databases. The project includes a database schema workbook for designing tables and mapping relationships, alongside a dedicated SQL query guide for writing selection, filtering, and aggregation statements. These resources are delivered through a relational database tutorial and a broader SQL learning resource. The material covers core relational database operations, including schema design, record management, and data mapping. It addresses the retrieval of information from relational tables and the integration of complex datasets using joins and unions.

tech-vault is a command-line technical interview bank and knowledge base designed for practicing engineering questions across various technical domains. It functions as a terminal-based application that stores structured study materials and interview questions as markdown files, which are then rendered directly within the system console. The project distinguishes itself through a delivery model that uses command-line argument parsing to filter content by topic or difficulty. It also includes a random selection algorithm to pick individual questions from the collection for spontaneous study sessions. The knowledge base covers a broad surface of engineering disciplines, including software engineering, system design, and backend concepts. It provides detailed materials on DevOps and cloud infrastructure, cybersecurity fundamentals, and data engineering principles such as data modeling and warehousing.

This project is a collection of interactive Python notebooks and educational resources designed for mastering data science, machine learning, and numerical computing. It provides a series of practical guides and tutorials covering deep learning, big data processing, and statistical analysis. The repository features specialized instructional suites for implementing classical machine learning algorithms, building deep learning model architectures, and managing AWS cloud infrastructure. It includes dedicated notebooks for data visualization and numerical computing exercises. The project covers a broad range of analytical capabilities, including tabular data manipulation, statistical inference, and time series analysis. It also encompasses big data processing through distributed computing, as well as the generation of 2D and 3D graphical visualizations and geographic maps.

This project is a professional development repository that provides structured learning paths for individuals pursuing careers in data-centric engineering and artificial intelligence. It functions as a competency benchmarking framework, defining the core knowledge areas and technical milestones required to achieve proficiency in specialized domains. The repository distinguishes itself through hierarchical knowledge graphing, which organizes complex technical subjects into nested tree structures to create clear, progressive learning sequences. By centralizing curated educational resources and industry-standard curricula, it streamlines the process of self-directed study for roles ranging from data engineering to deep learning. The content is maintained using markdown-based storage, allowing for version control and consistent updates across multiple technical roadmaps. These roadmaps cover a broad capability surface, including the design of scalable data systems, the application of statistical models, and the mastery of foundational mathematical and database principles.

Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster. The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big data storage and cluster computing. The system covers large scale data processing and big data infrastructure management. It provides capabilities for distributing data across clusters and executing computational tasks across multiple nodes to handle volumes of information too large for a single computer.

This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, while also examining the architectural patterns for both batch and stream processing pipelines. Beyond foundational theory, the project covers the implementation of event-driven systems, including event sourcing, log-structured storage, and message brokering. It addresses the complexities of maintaining system consistency, enforcing transactional integrity, and managing derived data views in environments prone to network failures and concurrency challenges. The documentation is available in multiple formats, including an exportable digital book version, to support study and reference across various devices.

Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available hardware. The library provides capabilities for out-of-core memory management and partition-based data distribution. These features allow it to process datasets larger than available RAM by loading and computing on data partitions from disk on demand.

Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth. The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps and prepare for professional interviews through targeted learning sequences. Beyond its core mapping capabilities, the platform offers practical project ideas and interactive tutoring to reinforce engineering concepts. It provides a centralized space for the community to share resources, track progressive skill development, and navigate complex technical landscapes.

This project is a comprehensive educational curriculum designed to teach the fundamental concepts, workflows, and tools of data science. It provides a structured learning path that covers the end-to-end data science lifecycle, including data acquisition, maintenance, processing, and pattern discovery, while grounding theoretical knowledge in practical, real-world applications. The curriculum distinguishes itself through a data-driven pedagogical design that utilizes interactive, notebook-based lessons. By combining narrative text with live code blocks, the platform allows learners to experiment with data analysis and visualization techniques in real time. The content is organized into a modular structure that sequences topics by progressive complexity, ensuring that foundational skills are established before moving into more advanced analytical techniques. The material encompasses a broad capability surface, including tutorials on data visualization, relational database querying, and the integration of cloud computing into data science workflows. These resources rely on an established ecosystem of open-source libraries to ensure that the skills acquired are applicable to professional environments. The repository is hosted as a centralized collection of instructional modules and guided exercises. It includes self-contained code samples and assignments that require a standard Python environment to execute.