Technical Blog
Deep dives into data engineering, cloud architecture, AI/ML, and the technologies that power modern data platforms.
Comprehensive Kafka architecture guide covering event streaming, topics, partitions, brokers, replication, producers/consumers, Kafka Connect, Schema Registry, Kafka Streams, and ksqlDB for building real-time data pipelines.
Quick setup guide for running Apache Spark with Jupyter Notebook in Docker containers. Learn volume mounting, container permission management, and local development workflows for data engineering and analytics projects.
Hands-on Docker tutorial: clone repositories, build images, run containers, and share on Docker Hub. Includes multi-stage Dockerfile examples, docker-compose configurations for MySQL and Node.js, and production deployment patterns.
Mkdocs for quick static website generator
Essential Docker CLI reference for data engineers and DevOps professionals: container management, debugging commands, image handling, and production-ready workflows for building scalable data pipelines.
Comprehensive guide to Docker and Kubernetes for data engineers. Understand container vs VM architecture, Docker daemon, images, registries, Dockerfile best practices, and Kubernetes orchestration for scalable data pipeline deployments.
PoC for sending alerts and notifications to slack channel using python
Interesting scenario while working on the UPSERT Logic and Star Schema - ETL Batch Load?
Proof of Concept to demo the encryption and decryption of AWS S3 files using AWS KMS
The email template gives us an idea about the error before moving to the cloud watch logs and reduces our time in searching error log stream.
data types, data platforms that stores, manages, process and deliver.
Learn Python context managers for bulletproof resource handling in data pipelines. Master the with statement, __enter__/__exit__ protocols, contextlib decorators, and patterns for database connections, file I/O, and subprocess management.
Deep dive into Python coroutines for building efficient data processing pipelines. Learn generator-based coroutines, the send method, coroutine decorators, and patterns for streaming data transformations.
Master Python decorators for building reusable, maintainable data pipelines. Learn closures, higher-order functions, functools.wraps, and practical patterns for input validation and logging in production code.
Deep dive into Python descriptors for creating managed attributes and data models. Learn __get__, __set__, __delete__ protocols, property decorators, and patterns for validation, type checking, and read-only attributes in production code.
