Introduction
Delta Live Tables (DLT) in Azure Databricks provides a robust framework for streaming architecture patterns, enabling efficient and reliable data processing. In this architecture, raw data streams from various sources into the system, where Delta Live Tables orchestrate the data flow. With the use of declarative pipeline syntax, DLT automates the complex tasks of data ingestion, transformation, and storage. This approach allows real-time data processing with fault tolerance and scalability. Key patterns include event-driven processing, where data is processed as it arrives, and micro-batch processing, which handles data in small, time-defined chunks. DLT ensures data quality and consistency through built-in error handling and monitoring, making it appropriate for, data-driven applications requiring low-latency data insights.