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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
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A comprehensive learning path for data engineers to understand and implement stream processing systems. It covers core concepts, event time semantics, state management, fault tolerance, and practical implementation using frameworks like Apache Flink.
A structured learning path for data engineers to design, implement, and maintain reliable batch data pipelines. Covers core concepts, scheduling, data consistency, validation, and monitoring, with a focus on practical, robust engineering practices.
This advanced learning path equips senior data engineers with the knowledge and skills to design and implement modern data lake and lakehouse architectures. It covers core concepts, cloud storage, open table formats, governance, and querying, with a foundation in data warehousing principles.
This learning path guides data engineers through Prefect's core concepts—flows, tasks, states, and scheduling—and extends into Dask integration, Prefect UI, deployments, notifications, and hybrid execution. It emphasizes hands-on practice and real-world deployment patterns.
This learning path guides data engineers through building and managing data pipelines with Apache Airflow. It covers core concepts like DAGs, tasks, operators, and sensors, along with architecture, scheduling, monitoring, error handling, and best practices. The path emphasizes hands-on application and assumes Python proficiency.
This path guides data engineers through designing and implementing ETL and ELT pipelines for data integration. It covers extraction strategies, transformation types, loading strategies, ETL tools, ELT with dbt, pipeline orchestration, and data lineage, with SQL and Python as foundational skills.
A structured learning path for data engineers to understand data warehouse architecture and design principles, covering dimensional modeling, schema design, slowly changing dimensions, OLAP, and data marts. The path progresses from foundational database concepts to advanced warehousing topics, ensuring a solid understanding of how to model and architect data warehouses.
This learning path equips data engineers with essential Linux command-line skills and shell scripting capabilities. It covers file system navigation, file permissions, process management, environment variables, and cron jobs, culminating in scripting tasks relevant to data engineering workflows.
This learning path guides data engineers through advanced SQL techniques essential for complex data extraction and transformation. Starting with database fundamentals, it progresses through joins, subqueries, CTEs, window functions, and query optimization, culminating in stored procedures and performance tuning. The path emphasizes practical application and systematic skill building.
This learning path guides data engineering students from Python basics to building robust data pipelines. It covers essential syntax, data structures, file handling, and libraries like NumPy and pandas, along with error handling and logging for production-ready automation.