- Course
Handling Streaming Data with Apache Pulsar 2
Apache Pulsar is a highly scalable, high throughput system that handles both queuing as well as streaming data with incredible ease. This course will teach you all the necessary concepts and tools to adopt Apache Pulsar to your projects.
Intermediate
- Course
Handling Streaming Data with Apache Pulsar 2
Apache Pulsar is a highly scalable, high throughput system that handles both queuing as well as streaming data with incredible ease. This course will teach you all the necessary concepts and tools to adopt Apache Pulsar to your projects.
Intermediate
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This course is included in the libraries shown below:
- Data
What you'll learn
Real-time applications are hard to scale! They can get high volumes of data in an instant and need to route messages correctly. Apache Pulsar is a highly scalable, low latency, high throughput pub-sub system to attack this problem. In this course, Handling Streaming Data with Apache Pulsar 2, you’ll learn how to tame them adopting Apache Pulsar. First, you’ll explore Pulsar Functions for serverless ETL. Next, you’ll discover how to connect your Pulsar deployment to Kafka and databases with Pulsar IO. Finally, you’ll learn how to migrate from Kafka to Pulsar with the client wrapper. When you’re finished with this course, you’ll have the skills and knowledge of Apache Pulsar needed to handle high volume streaming data in your applications with ease.
Handling Streaming Data with Apache Pulsar 2
Intermediate
Table of contents
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Version Check | 15s
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Advantages of Apache Pulsar | 4m 49s
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Architecture of Apache Pulsar | 5m 1s
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Installing Apache Pulsar with Docker | 4m 16s
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Installing Apache Pulsar in Our Instances | 2m 29s
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Introducing Pulsar Functions | 4m 55s
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Transforming Messages with Pulsar Functions | 7m 26s
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Develop and Debug Pulsar Functions | 7m 59s
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Deploying Pulsar Functions | 5m 14s
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Summary | 1m 10s
Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Machine Learning Operations. After researching in Probability, Statistics and Machine Learning optimization, he is currently working at JAMPP as a Machine Learning Research Engineer leveraging customer data for making accurate predictions at Real Time Bidding.