data-engineering

Most data engineering teams do not struggle because they lack smart people. They struggle because too much of the delivery process is still repetitive. A source-to-target mapping document comes in. Then someone has to manually create: target table DDL transformation SQL data dictionary technical specification data quality rules reconciliation checks test cases For one or two tables, this is manag…

The best way to actually understand data engineering is to build something that breaks, fix it, and watch it successfully run. In this article, we build an ETL pipeline that pulls data from the News API , cleans it up using pandas , and loads it into a local PostgreSQL database. If you are a beginner Python developer or just getting into data engineering, this one is for you! The Goal & The Archi…

Introduction Data engineering focuses on designing, building, and maintaining systems that collect, process, store, and deliver data for analysis and decision-making. Modern organizations generate enormous amounts of data from websites, applications, sensors, and business systems. Data engineers ensure this information is reliable, accessible, and useful. This article explains some of the most im…

I published a public data engineering project that demonstrates a cloud-based ETL pipeline for analyzing web analytics search keyword revenue. The project uses PySpark, AWS Glue, Amazon S3, and Terraform to process hit-level web analytics data, extract external search engine domains and keywords, parse revenue, and generate a sorted reporting output. Key concepts covered: Batch ETL pipeline desig…

Introduction Data engineering is the practice of designing and building systems for collecting, storing, transforming, and managing data so it can be safely used for reporting, analytics, machine learning, and making business decisions. Think of it as the behind-the-scenes work that makes apps, websites, and businesses function. Every modern company depends on data. If data is like water, data en…

For the past decade, data engineering was synonymous with distributed clusters. If your dataset exceeded a few gigabytes, standard practice dictated spinning up an Apache Spark cluster on AWS EMR or Databricks. This distributed paradigm introduced massive operational complexity: managing JVM configurations, allocating executors, tuning shuffle partitions, and paying a substantial "serialization t…

Every data engineer knows the struggle: finding a project that's both technically impressive and genuinely useful. Today I'll walk you through AfriData Pipeline — a production-grade ETL system that extracts economic data for all 54 African countries, loads it into a DuckDB analytical warehouse, and serves an interactive dashboard. No paid APIs. No cloud services required. Just Python, DuckDB, and…

Vietnam’s IT Market Feels Like It’s Entering A Different Era Over the past few months, I’ve been seeing more and more discussions around salaries, hiring trends and career directions in Vietnam’s tech industry. And honestly, the market feels very different compared to just a few years ago. Back then, the “safe route” usually meant: frontend/backend development mobile engineering fullstack paths c…

Over the next few posts, I’ll break down understanding analytics pipeline using: • Databricks • PySpark • Delta Lake • Azure Data Lake Storage (ADLS) This series is designed for: ✅ Beginners trying to understand ETL practically ✅ Engineers learning Medallion Architecture ✅ Professionals exploring Databricks & Delta Lake ✅ Anyone who wants to understand how real-world data pipelines are built The …

Have you ever looked at a stack of physical medical reports and wished you could just "Ctrl+F" your health history? 📑 We’ve all been there. Every hospital has a different layout, different units, and cryptic abbreviations that make manual data entry a nightmare. In the world of data engineering , turning unstructured "messy" documents into structured data extraction pipelines is a superpower. Tod…

Top 10 Data Engineering Interview Prep Tools (2026 Guide for SQL, ETL & System Design) Distinguishes learning vs simulation tools Hadil Ben Abdallah Hadil Ben Abdallah Hadil Ben Abdallah Follow Apr 28 Top 10 Data Engineering Interview Prep Tools (2026 Guide for SQL, ETL & System Design) # dataengineering # career # datascience # python 72 reactions Comments 8 comments 8 min read

We had a slightly reckless idea: what if we let AI do most of our data engineering work? Not "help with a query here and there," but actually build real pipelines. Azure, Databricks, Delta Lake, the whole thing. Real enterprise data, messy schemas, and stakeholders who will definitely shout if numbers look wrong. I'm a Senior Data Engineer, I work on this stack every day, and I still wanted to se…

Delta Lake for Dummies: ACID Transactions, Time Travel & Delta Tables If there's one concept in this entire series that separates a data engineer who knows Databricks from one who truly gets it — it's Delta Lake . It's the technology that makes your data lake reliable. It's what turns a folder of Parquet files into something that behaves like a proper database. And it's baked into everything you …

- Backend and full-stack engineers are moving into data engineering roles because their work has a more direct impact on business outcomes and offers better long-term career growth. - Data engineers have become the most valuable offshore hire because they build the systems that turn raw data into decision-making power (sitting at the core of AI, analytics, and business strategy). - For companies …

If you’ve ever wondered about the difference between these two roles, you’re not alone. The conversation around data analytics vs data engineering is becoming more common, especially as careers in data continue to grow. While the terms might sound similar, they represent two very different parts of the data process. One focuses on analyzing and […]

In AWS data engineering, Extract, Transform, and Load (ETL) processes are pivotal, as they allow you to prepare raw data sets for analytical purposes. This blog provides a detailed exploration of data engineering best practices specifically geared toward optimising ETL workflows, enhanced with relevant keywords and concepts for AWS Certified Data Engineer Associate Certification (DEA-C01). The ET…

Large-scale data engineering requires structuring, transforming, and analyzing datasets efficiently. The Medallion architecture—a design pattern for a data workflow for organizing and improving data quality through tiered transformations—has been a widely adopted approach for managing complex datasets. Traditionally implemented using tools like Spark and Delta Lake, this workflow ensures that raw…

The data engineering landscape constantly evolves, with new technologies and tools emerging rapidly. As businesses increasingly rely on data-driven insights, the demand for skilled data engineers is soaring.  Earning a relevant data engineering certification can be a powerful way to validate your skills, gain industry recognition, and stand out in a competitive job market. This blog delves into t…

Data engineering, particularly with Amazon Web Services (AWS), has evolved as an appealing and financially rewarding career path. The growing need for data engineers has elevated the salary spectrum within the field. But first, there’s an important question to answer before diving into this field: “What does an AWS Data Engineer salary look like?” No need to fret! Keep reading t…

Are you launching or advancing a career in data science with an eye toward figuring out what type of role within this multifaceted and fast-growing field makes the most sense for you? You are not alone. The post How to Become a Data Engineer [Career Guide] appeared first on University of San Diego Online Degrees .