Real Time Tweets Word Count Streaming Pipeline with Python, Kafka, Flink, Elasticsearch and Kibana

Apache Kafka & Flink Course

#flink #pyFlink #kafka #bigdata #streamprocessing #bigdata2023 https://www.udemy.com/course/python-kafka-streaming-handson/?referralCode=0DB1416B58D5527FD168 🚨Ready to level up your Big data skills? Join my Kafka & Flink Hands-on course and become a real-time data guru! Master the latest tools like Kafka, PyFlink, Elasticsearch, and Kibana, and unlock the potential of real-time analytics. 💯Enroll today and embark on a transformative learning journey!👌 https://www.udemy.com/course/python-kafka-streaming-handson/?referralCode=0DB1416B58D5527FD168 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

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