Optimizing Real-Time Data Pipelines: Streaming Tables vs. Delta Live Tables
Hello, my readers! Recently, while diving into Databricks, I came across some terminology that piqued my interest and, to be honest, left me a bit puzzled. This article aims to clear up one of these intriguing terms in detail.
In this article, we’ll explore the key differences between Streaming Tables and Delta Live Tables, providing insights into how each approach handles real-time data processing.
Traditional data warehouses are not designed for streaming ingestion and transformation. Ingesting large volumes of data with low latency in a traditional data warehouse is expensive and complex because legacy data warehouses were designed for batch processing. As a result, databricks implemented clumsy solutions that required configurations outside of the warehouse and needed to use cloud storage as an intermediate staging location. Managing these systems is costly, prone to errors, and complex to maintain.
Delta Live Tables (DLT):
- Managed service: DLT is a fully managed framework for building reliable data pipelines.
- Declarative syntax: DLT allows you to define ETL pipelines declaratively. It automatically handles tasks like ensuring data quality, retrying failed jobs, and maintaining table lineage.
- Automated monitoring: DLT provides built-in…
