This repository demonstrates workflow orchestration for data engineering pipelines using Kestra. It guides users through building, running, and scheduling data pipelines that extract, transform, and load (ETL) data both locally (with PostgreSQL) and in the cloud (with Google Cloud Platform). The project is hands-on and includes conceptual explanations, infrastructure setup, and several example pipeline flows.
This repository provides a comprehensive, step-by-step guide to building a simple data engineering pipeline using containerization (Docker), orchestration (Docker Compose), and Infrastructure as Code (Terraform), with a focus on ingesting and processing NYC taxi data. The project is hands-on and includes conceptual explanations, infrastructure setup, and several example pipeline flows.
This project is a practical template for data engineers to learn and implement containerized data pipelines, local and cloud database management, and automated cloud infrastructure provisioning using modern tools like Docker, Docker Compose, and Terraform. It is especially useful for those looking to understand the end-to-end workflow from local prototyping to cloud deployment in a reproducible, automated way.
Artificial intelligence isn’t just a consumer of data—it’s increasingly becoming an integral part of how we design and operate our data systems. This post explores the evolving relationship between AI and data architecture.
Modern data architectures are incorporating AI at various levels:
AI algorithms help determine which data is most relevant to different users and use cases, optimizing data discovery and access.
Creating robust machine learning pipelines is essential for deploying AI solutions at scale. This post covers key considerations and best practices.
A well-designed ML pipeline includes these key stages:
Solution: Implement robust data validation and cleaning processes early in the pipeline.
Data engineering is the backbone of any data-driven organization. In this post, we will explore the fundamental concepts that every aspiring data engineer should understand.
Data engineering focuses on designing, building, and maintaining the infrastructure and architecture for data generation, storage, and analysis. Data engineers develop the systems that collect, manage, and convert raw data into usable information for data scientists and business analysts.
Welcome to my professional website! I’m an AI-Enabled Data Engineer passionate about leveraging artificial intelligence and data solutions to solve complex business problems.
With expertise in data engineering, machine learning, and AI integration, I help organizations transform their data into actionable insights. I specialize in designing and implementing data pipelines, creating machine learning models, and developing AI-powered applications that drive business value.
On this website, you can explore:
