• Mrs.Sophia G
• Ranjith V
• Roshan A
• Tharun S

Twitter is an online social networking service with more than 300 million users, generating a huge amount of information every day. Twitter's most important characteristic is its ability for users to tweet about events, situations, feelings, opinions, or even something totally new, in real time. Currently there are different workflows offering real-time data analysis for Twitter, presenting general processing over streaming data. This study will attempt to develop an analytical framework with the ability of in-memory processing to extract and analyze structured and unstructured Twitter data. The proposed framework includes data ingestion, stream processing, and data visualization components with the Apache Kafka messaging system that is used to perform data ingestion task. Furthermore, Spark makes it possible to perform sophisticated data processing and machine learning algorithms in real time. We have conducted a case study on tweets about the earthquake in Japan and the reactions of people around the world with analysis on the time and origin of the tweets.

Streaming processing, Big Data, Kafka, Spark Twitter, Real time analysis, Data analytics.

"A Survey on Twitter Data Analysis Using Kafka", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.c135-c140, March-2023, Available :http://www.jetir.org/papers/JETIR2303220.pdf

"A Survey on Twitter Data Analysis Using Kafka", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppc135-c140, March-2023, Available at : http://www.jetir.org/papers/JETIR2303220.pdf