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A Mindset for Sustainable Code

Warning Signs for Refactoring

Master AWS Migration & Transfer Services using Cornell Note-Taking methodology. Structured study guide covering Application Discovery, MGN, DMS, DataSync, Snow Family, and Transfer Family with key concepts, detailed explanations, and summary sections for effective learning and retention.

Comprehensive guide to AWS database services including DynamoDB, RDS, Aurora, DocumentDB, Neptune, Timestream, and Redshift. Learn features, use cases, and best practices for choosing the right database solution.

Discover the latest AWS storage solutions—object, block, file, and backup. Learn key features, new services, and best practices for optimizing your cloud storage in 2024.

A comprehensive overview of AWS Data Engineering fundamentals, covering data types, big data concepts, data lakes and warehouses, ETL pipelines, data modeling, lineage, schema evolution, optimization, and validation. This post is ideal for beginners and practitioners looking to understand the core principles and AWS services used in modern data engineering.

An in-depth guide to AWS for Data Engineering, covering core concepts, storage, databases, migration, compute, containers, analytics, integration, security, networking, management, machine learning, developer tools, cost management, and APIs. This post is designed for data engineers and cloud practitioners seeking a holistic understanding of AWS services and best practices for building scalable, secure, and efficient data solutions.

Overview Clean Code in Python.