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In this comprehensive tutorial, you'll join us on the journey to create a robust real-time data streaming pipeline from scratch! We dive deep into the world of Apache Kafka for efficient event recording, Apache Flink for powerful stream processing, and Postgres for storing and analyzing real-time insights.
What you learn:
Set up Apache Kafka for event streaming.
Building Apache Flink jobs for real-time data processing.
Integration of Flink with Kafka for seamless data flow.
Designing a schema and connecting Postgres as a data sink.
Optimizing the pipeline for scalability and performance.
Why real-time data streaming is important:
Real-time data processing is a game changer for industries looking for instant insights from their data. Whether you work in finance, e-commerce, or IoT, understanding how to design a robust streaming pipeline is a valuable skill in today's data-driven world.
Who should watch:
Data engineers
Software developers
Data scientists
Anyone interested in real-time data processing
Timestamps:
00:00 – Pipeline architecture
06:50 – Set up Docker Compose file
12:50 – Set up Postgres DB
4:00 PM – Set up Generate Data Python service
22:35 – Set up Python Kafka Producer service
29:10 – Set up Flink Kafka Consumer Service
51:20 – End-to-end testing
Requirements:
Basic knowledge of the concepts of Kafka, Flink and Postgres.
Knowledge of Java and SQL is a plus.
Remark:
This tutorial assumes a working knowledge of the basics of Apache Kafka, Apache Flink, and Postgres. If you're new to these technologies, check out our beginner-friendly guides listed in the description.
Code and resources:
https://www.buymeacoffee.com/bytecoach/e/213878
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#ApacheKafka #ApacheFlink #Postgres #RealTimeData #DataStreaming #datapipeline
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