RESOURCES

Data Engineering & Financial Data Resources

Technical resources spanning data engineering education, financial-data systems, analytics and AI/ML.

01

Python for Data Engineering

Programming patterns, file handling, APIs, error handling, testing and automation.

02

SQL & Databases

Joins, CTEs, window functions, query optimization, data modeling and incremental loading.

03

PySpark & Spark

DataFrames, transformations, partitioning, joins, caching, performance and Spark architecture.

04

Kafka & Streaming

Topics, partitions, consumer groups, schemas, delivery semantics, checkpointing and streaming patterns.

05

Cloud Data Engineering

AWS and Azure patterns covering object storage, orchestration, serverless processing and analytics.

06

Interview Preparation

Project explanations, SQL/Python/PySpark questions, architecture discussions and troubleshooting scenarios.

RESOURCE ROADMAP

What will be added next.

Planned material can include downloadable notes, structured learning paths, practical notebooks, project walkthroughs, architecture diagrams and interview question banks.

Only resources that are actually published should be represented as available content. This page describes the platform direction rather than claiming unpublished material already exists.