| 1. Introduction.mp4 | 13.3 MB | ||
| 1. Real Time Streaming Pipeline Architecture Design.mp4 | 24.4 MB | ||
| 1. The architecture Design of the hands-on project.mp4 | 15.5 MB | ||
| 2. Real time Data Source Twitter API from Developer Platform.mp4 | 7.5 MB | ||
| 2. Requirement for this hands on (updated versions ).mp4 | 11.9 MB | ||
| 3. Apache Flink Introduction pyflink.mp4 | 14.3 MB | ||
| 3. Extracting Twitter Data Stream from API in python.mp4 | 50.9 MB | ||
| 4. Configure Flink to consume data from a Kafka topic as a data source pyFlink.mp4 | 70.7 MB | ||
| 4. Create a Tweets Data Kafka Producer.mp4 | 28.4 MB | ||
| 5. Configure Flink to write the processed data to a Elasticsearch sink pyFlink.mp4 | 90.4 MB | ||
| 5. Create a Tweets Data Kafka Consumer.mp4 | 45.5 MB | ||
| 6. Kafka Consumer Store Data in Hadoop HDFS.mp4 | 53.2 MB | ||
| 6. Real Time Tweets Word Count with pyFlink and Kafka.mp4 | 138.5 MB | ||
| 7. Complete Python Code Streaming pipeline.html | 0 B | ||
| 7. Complete Python Code resources (Twitter Producer & Consumer + HDFS Consumer).html | 102.4 B | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Kafka_tweets_consumer.py | 819.2 B | ||
| hdfs_consumer.py | 1.1 KB | ||
| kafka_flink_elasticsearch_streaming.py | 1.9 KB | ||
| kafka_flink_streaming.py | 1.4 KB | ||
| kafka_tweets_producer.py | 1.7 KB | ||
| pyflink_word_count.py | 3.9 KB | ||
| secret.txt | 102.4 B | ||
| tweets_kafka_producer.py | 2 KB | ||
| ▲ 25 total files | |||
Kafka Flink End-to-end Streaming pipeline: python Handson
https://DevCourseWeb.com
Published 5/2023
Created by Big Data Landscape
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 15 Lectures ( 1h 19m ) | Size: 564 MB
Python Big Data Internship Program: Real Time Streaming pipeline with Kafka, Flink, Hadoop HDFS, Elasticsearch & Kibana
What you'll learn
Kafka fundamentals: Master the key concepts and functionalities of Apache Kafka for efficient data ingestion and streaming.
Flink for real-time processing: Learn how to leverage Apache Flink for real-time data processing and analytics in streaming pipelines.
Elasticsearch for indexing and search: Explore the capabilities of Elasticsearch for indexing and querying large volumes of streaming data in real time.
Visualization with Kibana: Use Kibana to create interactive visualizations and dashboards that provide insights into your streaming data pipeline.
Integrating Kafka and HDFS: Learn how to integrate Kafka with HDFS using Python to store streaming data efficiently and reliably.
Hands-on implementation: Get hands-on experience by building a Python-based solution that consumes Kafka data streams and stores them in HDFS
PyFlink for real-time processing: Utilize PyFlink for real-time data processing.
Pydoop for HDFS integration: Integrate HDFS with Python using Pydoop.
Kafka-Python for Kafka integration: Connect to Kafka and process streaming data using Kafka-Python.
Requirements
This course is designed to be beginner-friendly
Basic familiarity with Python programming language would be helpful
You will be guided through practical exercises that focus on building an end-to-end streaming pipeline using Python
Basic Knowledge on Kafka, Flink, Hadoop, Elasticsearch and Kibana
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