Real Time Tweets Word Count Streaming Pipeline with Python, Kafka, Flink, Elasticsearch and Kibana
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In this video, we'll dive into a practical example that demonstrates how to perform word count on a Kafka stream of tweets using PyFlink and store the results in Elasticsearch. So, let's get started!
Stream processing is a powerful technique that allows us to analyze and extract valuable insights from continuous streams of data in real-time. PyFlink, a Python API for Apache Flink, provides an easy and efficient way to process streaming data. In our example,
In this video, we'll leverage PyFlink to process a stream of tweets and perform word count.
By analyzing the word count, we can identify the most frequently occurring words or phrases in the dataset. This information can help us understand the popular topics or subjects being discussed. For example, if words like "technology," "innovation," and "startups" have high frequencies, it indicates a focus on the tech industry.
Timecodes
0:00 - Intro
0:35 - Word Count Intro
1:31- Pipeline architecture
3:35- Prerequisites
5:49- PyFlink Streaming Code
11:14- Word Cout Processing Pipeline ( Flink + python)
15:30- Running (Flink Streaming + Kafka Producer)
16:13 - Create Kibana Elasticsearch index
16:50- Create Kibana Chart & Dashboard
20:25- Run Real Time Data Analytics