International Journal on Science and Technology

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

Author(s) Pritam Roy
Country United States
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.
Keywords Data Streaming, Micro-batch Processing, Real-time Analytics, Cloud-native Computing, Distributed Systems.
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-28
Cite This Understanding Data Processing in Databricks: From Spark Streaming to Structured Streaming - Pritam Roy - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2924
DOI https://doi.org/10.71097/IJSAT.v16.i1.2924
Short DOI https://doi.org/g896ff
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