Apache Kafka - Spark - Cassandra Real Time Streaming Project
Sonal Saxena
32:54
Description
Real Time Data Streaming Project, that involved building the architecture from scratch and deployment.
What You'll Learn?
- Professionals looking for an end to end kafka, spark and cassandra streaming pipeline.
- Anyone who wants to understand how to use Apache kafka in their current architecture.
- Engineers who are looking to design a Kafka Solution Pipeline.
- A complete case study from start to execution.
Who is this for?
More details
DescriptionThe course is designed to provide a comprehensive understanding of real-time big data processing using Kafka, Spark, and Cassandra. In today's world, data is produced at an unprecedented rate, and the ability to process and analyze this data in real-time is critical for making informed decisions. This course focuses on the fundamental concepts and architecture of Kafka, Spark, and Cassandra, and how they work together to create a robust big data processing pipeline.
Students will learn how to set up Kafka clusters and work with Kafka producers and consumers. Students will also learn about Kafka Streams, a client library for building real-time streaming applications that process data directly within Kafka.
Throughout the course, students will gain hands-on experience through practical exercises and projects that simulate real-world scenarios. By the end of the course, students will have a understanding of how to use Kafka, Spark, and Cassandra to build real-time big data processing systems.
Course Objectives:
Understand the fundamental concepts of real-time big data processing
Learn the architecture setup of Kafka, Spark, and Cassandra
Understand how Kafka, Spark, and Cassandra work together to create a real-time big data processing pipeline
Gain hands-on experience with Kafka, Spark, and Cassandra through practical exercises and projects
Learn how to build a real-time big data processing pipeline from scratch
This course is intended for software engineers, data engineers, and data analysts who have a basic understanding of programming concepts and are familiar with SQL.
Who this course is for:
- Engineers looking for a CASE STUDY on Real Time Data Streaming involving KAKFA and SPARK
The course is designed to provide a comprehensive understanding of real-time big data processing using Kafka, Spark, and Cassandra. In today's world, data is produced at an unprecedented rate, and the ability to process and analyze this data in real-time is critical for making informed decisions. This course focuses on the fundamental concepts and architecture of Kafka, Spark, and Cassandra, and how they work together to create a robust big data processing pipeline.
Students will learn how to set up Kafka clusters and work with Kafka producers and consumers. Students will also learn about Kafka Streams, a client library for building real-time streaming applications that process data directly within Kafka.
Throughout the course, students will gain hands-on experience through practical exercises and projects that simulate real-world scenarios. By the end of the course, students will have a understanding of how to use Kafka, Spark, and Cassandra to build real-time big data processing systems.
Course Objectives:
Understand the fundamental concepts of real-time big data processing
Learn the architecture setup of Kafka, Spark, and Cassandra
Understand how Kafka, Spark, and Cassandra work together to create a real-time big data processing pipeline
Gain hands-on experience with Kafka, Spark, and Cassandra through practical exercises and projects
Learn how to build a real-time big data processing pipeline from scratch
This course is intended for software engineers, data engineers, and data analysts who have a basic understanding of programming concepts and are familiar with SQL.
Who this course is for:
- Engineers looking for a CASE STUDY on Real Time Data Streaming involving KAKFA and SPARK
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Sonal Saxena
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 14
- duration 32:54
- Release Date 2023/04/25