Lecture

Spark Streaming: Fault Tolerance and DStreams

Description

This lecture covers Spark Streaming, which enables real-time analysis of big data by processing data as soon as it arrives. It discusses fault tolerance techniques for streaming platforms, including replication and upstream backup. The concept of DStreams, a sequence of immutable, partitioned datasets, is explained. Examples of streaming word count and sliding window operations are provided, showcasing the inter-mixing of RDD and DStream operations. The lecture also explores fault-tolerance mechanisms such as RDD lineage and fast fault recovery within Spark Streaming, aiming to unify batch and stream processing models.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.