Schema-on-Write vs Schema-on-Read | Codebasics

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Contents

1.1: What is Kafka?
1.2: Kafka vs Message Queues
1.3: Kafka Architecture: Broker, Topic, Partition
1.4: What is Confluent? Setting up your Confluent Account
1.5: Confluent UI Walkthrough
1.6: Create Topic & Produce Messages
1.7: Python Producer Demo
1.8: Topics, Partitions: Deep Dive

2.1: Producers Deep Dive
2.2: Python Consumers Demo
2.3: Consumer Groups
2.4: Serialization
2.5: Schema Evolution

3.1: Batch vs Stream Processing
3.2: What is Apache Flink?
3.3: ksqlDB vs Flink
3.4: Hands on Flink in Confluent
3.5: Watermarks
3.6: Flink: Real World Use-cases
3.7: Flink Architecture
3.8: Checkpoint and Fault Tolerance

4.1: Schema-on-Write vs Schema-on-Read
4.2: Schema Validation in Python Producer
4.3: Flink SQL: Creating Sink Tables

5.1: Final Quiz

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Schema-on-Write vs Schema-on-Read

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