Transaction Isolation Levels Explained: Anomalies and How Databases Stay Consistent

What happens when multiple transactions hit the same data at the same time? In this episode of Data Engineering Concepts, we dive into transaction isolation levels — a classic interview topic and a must-know for anyone working with transactional systems. We start with the anomalies that isolation levels are designed to prevent: dirty writes dirty reads lost updates non-repeatable reads read and write skews and phantom reads. Then we walk up the "ladder" of the four main isolation levels: Read Uncommitted Read Committed Repeatable Read And Serializable and look at how each one removes more anomalies at the cost of performance. Along the way, we touch on MVCC snapshots in PostgreSQL, why isolation levels can behave differently across database systems (hello, DB2's cursor stability), and how Serializable Snapshot Isolation can even roll back your queries. By the end, you'll have a solid foundation for understanding how parallel transactions interact — and how to keep your data consistent. 🎙️ Data Engineering Concepts — making complex data engineering topics easier to understand, one episode at a time. Podcast website: 🌐 ⁠⁠⁠⁠https://deconcepts.com⁠⁠⁠⁠ Add me on LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/dpolovinkin⁠⁠