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Understanding Data Processing in Databricks: From Spark Streaming to Structured Streaming

Year: 2025

Type: article

Abstract: The evolution of data processing has transformed significantly, particularly in streaming data handling capabilities. From traditional Spark Streaming to advanced Structured Streaming in Databricks, the technology has matured to handle complex real-time processing needs. This article explores the progression from micro-batch processing to continuous streaming, highlighting key improvements in latency, throughput, and reliability. The introduction of Auto Loader and Project Lightspeed represents further advancements in cloud-native data ingestion and processing capabilities. Through real-world implementations across financial services, manufacturing, healthcare, and automotive sectors, the article demonstrates how modern streaming solutions enable sophisticated data processing while maintaining performance and scalability.

Source: International Journal on Science and Technology

DOI: 10.71097/ijsat.v16.i1.2924

Author: Pritam Roy -

Language: English

Topic: Advanced Database Systems and Queries

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4408943161

FWCI:

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