You've likely heard that "Data is the new oil". But raw oil is useless without a refinery. In the world of Big Data, Apache Spark is that refinery. Whether it's millisecond-level fraud detection or processing terabytes of logs, Spark's ability to handle massive scale with in-memory speed is why it remains a core skill for every ML & Data Engineer. Here are 5 real-world problems and exactly how Spa
Data is no longer treated as a byproduct of business operations and has become one of the most valuable organizational assets. Every interaction on a banking application, e-commerce platform, hospital system, logistics network or social media service generates data continuously. As organizations increasingly adopt digital workflows, cloud platforms, machine learning systems and real-time applicati
In modern data-driven organizations, managing and analyzing data efficiently is critical. OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are both integral parts of data management, but they have different functionalities. Understanding how they differ, and how they complement each other is essential for anyone working with data systems. Online Transaction Processing (
🚀 The Complete Guide to Pass the DP-750 Beta Certification Exam — Azure Databricks Data Engineer Associate Today I have something important for you. I've created a specific guide to help you pass your DP-750 beta certification. How to master Azure Databricks, Unity Catalog governance, and Apache Spark to confidently pass the Microsoft DP-750 certification — the most complete study roadmap for d
SQL is widely known for data querying and manipulation but systems do grow; data becomes larger; processes become repetitive and operations become sensitive. SQL has some features which enables it to be considered a fully fledged programming language. Some of the features which I discuss in this article are procedures, functions and transactions. Each of these concepts serve distinct purposes. Sto
Hi 👋, In this post we shall explore Bedrock's structured KB with this architecture: Upload CSVs to S3 > SNS Queue > Crawl data with Glue > Query with Redshift > Bedrock KB > Query with LLM. Let's do some of this with code. Let's get started. Clone the repo and switch to the project directory. git clone [email protected]:networkandcode/networkandcode.github.io.git cd structured-kb-demo/ Do a uv sync
Subqueries vs. CTEs in SQL: A Practical Guide to Writing Cleaner, Smarter Queries Whether you're just getting comfortable with SQL or leveling up your data skills, two tools will come up again and again when working with complex queries: subqueries and Common Table Expressions (CTEs). They solve similar problems — breaking a complex query into manageable pieces — but they do it in different ways
In a previous post, I explored Codd's connection trap in PostgreSQL and MongoDB — the classic pitfall where joining two independent many-to-many relationships through a shared attribute produces spurious combinations that look like facts but aren't. The example followed Codd's 1970 suppliers–parts–projects model: we know which suppliers supply which parts, and which projects use which parts, but j