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Guided learning journeys that build knowledge step by step.
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This advanced learning path equips data engineers with the knowledge and skills to implement comprehensive monitoring and observability for data pipelines. It covers foundational observability concepts, metrics collection, logging, alerting, and advanced data quality monitoring including freshness, volume, drift, and lineage. The path emphasizes practical application and systematic learning.
A comprehensive learning path for data engineers to systematically test data pipelines. It covers foundational testing concepts, Python-specific skills, data validation, schema testing, performance testing, and CI/CD integration, culminating in a capstone project.
This path equips data engineers with the skills to containerize data applications and pipelines using Docker. It covers Docker fundamentals, image creation, data persistence, networking, orchestration basics, and practical application to data engineering workflows.
This advanced learning path equips senior data engineers with the knowledge and skills to provision data infrastructure using Infrastructure as Code (IaC) tools like Terraform and CloudFormation. It covers fundamental IaC concepts, cloud resource provisioning, version-controlled infrastructure, and CI/CD pipelines, enabling systematic and reproducible data infrastructure management.
This learning path equips data engineers with the knowledge and skills to implement robust security and compliance measures in data pipelines. It covers core security concepts, data protection techniques, access control, auditing, and major compliance frameworks, culminating in a capstone project.
This learning path equips data engineers with the knowledge and skills to implement data governance frameworks and manage data quality in modern data platforms. It covers governance principles, data quality dimensions, monitoring, lineage, metadata management, and data catalogs, with a focus on practical application in engineering workflows.
This learning path equips data engineers with the knowledge to design, implement, and manage cloud-native data warehouses using Snowflake, BigQuery, and Redshift. It covers essential cloud storage concepts, SQL optimization, cost management, security, and data sharing, culminating in practical application scenarios.
A systematic learning path for analytics engineers to master dbt for transformation workflows in modern data stacks. Covers core dbt concepts, models, materializations, testing, documentation, macros, packages, and dbt Cloud, with a foundation in SQL and data warehousing.
This learning path equips data engineers with the skills to use Apache Spark for large-scale data processing. Starting from programming fundamentals and distributed systems concepts, it progresses through Spark's core abstractions (RDDs, DataFrames, SQL) and optimization techniques, culminating in structured streaming. The path emphasizes hands-on practice and performance tuning, ensuring learners can design and implement efficient data pipelines.
This advanced learning path equips data engineers with the knowledge and skills to design, deploy, and manage Apache Kafka for real-time data ingestion and processing. It covers core architecture, producer/consumer APIs, stream processing, integration via Kafka Connect, schema management, and cluster operations.