Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number of devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis Streams enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. Apache Spark’s Structured Streaming API enables real-time decision making for Continuous Applications. In this session, Roshan will perform a live demonstration of how to integrate open source Redis Streams with Apache Spark’s Structured Streaming API using Spark-Redis library. He will also walk through the code and demo a live continuous application.

Talk Description: Continuous applications have 3 things in common:

1) They collect data from sources (ex: IoT devices)

2) process them in real-time (example: ETL)

3) deliver them to machine learning serving layer for decision making

YOU MAY ALSO LIKE:

• Digital Discrimination: Cognitive Bias in Machine Learning (in Online Event on 4th June 2020)
• Using A.I. to Help Humanitarian Causes (SkillsCast recorded in May 2020)
• Flowing Things, Not So Strange In The MVI World (SkillsCast recorded in October 2019)