Data EngineeringRoadmaps
Follow structured learning paths designed for freshers, aspiring Data Engineers, and professionals transitioning into modern data platforms and cloud ecosystems.
Core Data Engineering Foundations
Start with the most important foundational skills required to become a modern Data Engineer.
SQL Roadmap
Master SQL from fundamentals to advanced query optimization for Data Engineering interviews and real-world projects.
Python Roadmap
Learn Python for Data Engineering including APIs, Pandas, automation, and data processing workflows.
PySpark Roadmap
Understand distributed processing, Spark optimization, transformations, and real-world big data concepts.
Data Architecture & Modeling
Learn foundational concepts behind scalable analytical systems, warehouse design, and modern data architectures.
Cloud Data Engineering
Learn modern cloud-based Data Engineering platforms and enterprise architectures.
Azure Data Engineering
Structured roadmap covering ADF, Databricks, Synapse, ADLS, Blob Storage, Microsoft Fabric, and enterprise cloud workflows.
AWS Data Engineering
Learn modern AWS Data Engineering tools including S3, Glue, Athena, Redshift, and EMR.
Data Engineering Ecosystem
Explore additional tools, orchestration systems, streaming platforms, cloud warehouses, and engineering workflows used in modern Data Engineering.
Airflow
Learn orchestration concepts including DAGs, scheduling, pipelines, and automation workflows.
Kafka
Understand real-time streaming, producers, consumers, partitions, and event-driven architectures.
dbt
Learn modern data transformation workflows and analytics engineering concepts using dbt.
Databricks
Explore lakehouse architecture, notebooks, Spark clusters, and enterprise analytics workflows.
Snowflake
Learn cloud data warehousing concepts, optimization, and scalable analytics architectures.
Docker & Linux
Understand containers, Linux basics, environments, deployments, and engineering workflows.