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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 370 / 782
This advanced path equips data engineers with the skills to plan and execute large-scale data migrations. It covers migration planning, assessment, tooling, validation, cutover strategies, and rollback planning, with a focus on cloud migrations and legacy data systems. The path emphasizes practical, hands-on application and thorough validation to ensure successful, low-risk migrations.
This learning path equips data engineers with advanced knowledge to optimize data storage using efficient file formats and compression techniques. It covers columnar storage fundamentals, deep dives into Parquet, ORC, and Avro, and practical strategies for partitioning and compression to reduce storage costs.
This advanced learning path equips IoT data engineers with the knowledge to design and implement robust data pipelines for Internet of Things data streams. It covers IoT protocols, sensor data processing, edge computing, time-series databases, stream processing, anomaly detection, and IoT platforms, with a focus on real-world engineering challenges.
This path equips data engineers with the knowledge to select and use NoSQL databases for diverse use cases. It covers core concepts, major NoSQL types (document, key-value, columnar, graph), and practical tradeoffs, culminating in a capstone project.
This learning path guides data engineers through the principles and practices of designing data models that are optimized for performance and scalability. It covers foundational database concepts, SQL, normalization, denormalization, dimensional modeling, data vault, schema design, and performance optimization, culminating in practical application with data modeling tools.
A beginner-friendly learning path for data engineers to master Git for versioning code and configuration files. It covers essential Git concepts, branching strategies, code review, collaboration, and integration with CI/CD pipelines, tailored to data pipeline code management.
This learning path equips data engineers with the knowledge to implement data catalogs for discovery and governance. It covers metadata concepts, metadata extraction, search, data discovery, and data lineage visualization, with practical applications using tools like Amundsen and DataHub.
This path equips data engineers with the knowledge to apply integration patterns for moving and transforming data across systems. It covers batch and event-driven integration, CDC, API integration, EDI, and integration tools, building from ETL fundamentals to advanced real-time patterns.
This advanced learning path equips ML platform engineers with the knowledge to design, implement, and maintain robust data pipelines that feed machine learning workflows. It covers feature engineering pipelines, feature stores, data versioning, and model monitoring, with a strong foundation in data engineering principles.
This advanced learning path equips data engineers with the knowledge to design, implement, and optimize systems for real-time data analysis and querying. It covers streaming fundamentals, specialized databases like ClickHouse, Druid, and Pinot, and techniques for low-latency dashboards and query engines.