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Apache Kafka

Information Technology > Continuous Integration/Continuous Deployment

Description

Apache Kafka is a distributed streaming platform that allows you to publish and subscribe to streams of records, similar to a message queue or enterprise messaging system. It's designed to handle real-time data feeds with low latency and high reliability. With Kafka, you can also process streams of records as they occur. Kafka is widely used for real-time analytics, transforming and reacting to streams of data, event sourcing, and for the decoupling of system dependencies. Advanced users can leverage its replication model for fault tolerance, durability, and failover, making it suitable for critical business applications.

Stack

SMACK

Expected Behaviors

✎
LEVEL 1

Fundamental Awareness

At this level, individuals have a basic understanding of Apache Kafka. They are familiar with the concept of publish-subscribe messaging system and understand Kafka's role in real-time data processing. They also have a basic knowledge of Kafka architecture.

🌱
LEVEL 2

Novice

Novices can install and configure Apache Kafka. They have knowledge of Kafka Producers and Consumers and understand Kafka Topics and Partitions. They can create simple Kafka Producers and Consumers in Java.

🌍
LEVEL 3

Intermediate

Intermediate users have an understanding of Kafka Streams and can work with Kafka Connect. They know about Kafka Cluster and can handle failures in Kafka. They also have an understanding of Kafka Security (SSL, SASL, ACL).

⭐
LEVEL 4

Advanced

Advanced users can set up multi-node Kafka cluster and optimize Kafka for better performance. They can integrate Kafka with other systems like Hadoop, Spark, Storm etc. They have an understanding of KSQL and experience with Schema Registry and Avro.

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LEVEL 5

Expert

Experts have a deep understanding of Kafka internals and can troubleshoot complex Kafka issues. They have experience in designing large scale Kafka systems and are proficient in advanced Kafka Streams operations. They have expertise in managing and monitoring Kafka using tools like Confluent Control Center, Prometheus, Grafana etc.

Micro Skills

✎
LEVEL 1

Fundamental Awareness

Familiarity with the concept of event streaming
Knowledge of the basic components of Kafka
Understanding of how Kafka stores data
Understanding of the principles of pub-sub model
Awareness of the differences between pub-sub and other messaging models
Basic knowledge of how Kafka implements pub-sub model
Understanding of the role of Producers, Consumers, Brokers in Kafka
Familiarity with the concept of Topics and Partitions
Awareness of how data flows in Kafka
Awareness of the use cases of Kafka in real-time data processing
Understanding of how Kafka can be integrated with other systems for data processing
Basic knowledge of the benefits of using Kafka for real-time data processing
🌱
LEVEL 2

Novice

Understanding of system requirements for Kafka installation
Knowledge of how to download and install Kafka
Familiarity with Kafka configuration files
Ability to start and stop Kafka server
Understanding of the role of producers and consumers in Kafka
Familiarity with producer API
Familiarity with consumer API
Understanding of how to send and receive messages using producers and consumers
Knowledge of what topics and partitions are
Understanding of how to create, list, and delete topics
Familiarity with topic configuration parameters
Understanding of how data is distributed across partitions
Understanding of how to set up a Java project for Kafka
Knowledge of Kafka producer API in Java
Ability to write a simple Java program to send messages to a Kafka topic
Understanding of how to handle exceptions and errors in Kafka producer code
Understanding of Kafka consumer API in Java
Ability to write a simple Java program to consume messages from a Kafka topic
Knowledge of how to handle offsets in Kafka consumer
Understanding of consumer groups and their usage
🌍
LEVEL 3

Intermediate

Knowledge of Stream Processing Topology
Understanding of KStream and KTable
Familiarity with Windowing Operations
Ability to implement simple stream processing applications
Understanding of Source and Sink Connectors
Ability to configure Kafka Connect
Experience with standalone and distributed modes
Knowledge of common connectors like JDBC, HDFS, S3 etc.
Understanding of Broker, Zookeeper and their roles in a cluster
Familiarity with Replication and Partitioning in Kafka
Basic knowledge of In-sync replica set (ISR)
Understanding of Leader Election for partitions
Understanding of failure scenarios in Kafka
Knowledge of Kafka's fault-tolerance capabilities
Ability to recover from broker failures
Experience with data replication for fault tolerance
Basic knowledge of SSL/TLS for Kafka
Understanding of SASL authentication mechanisms
Familiarity with Access Control Lists (ACLs) for authorization
Ability to secure Kafka clusters
⭐
LEVEL 4

Advanced

Understanding of distributed systems
Knowledge of Zookeeper
Experience with Kafka broker configuration
Ability to handle replication and partitioning in Kafka
Understanding of Kafka's throughput, latency, and durability
Knowledge of Kafka's I/O operations
Experience with tuning Kafka producers, consumers, and brokers
Ability to optimize Kafka's network usage
Understanding of Hadoop ecosystem
Experience with Spark Streaming
Knowledge of Storm topology
Ability to use Kafka Connect for data integration
Knowledge of SQL language
Understanding of stream processing concepts
Experience with creating and executing KSQL queries
Ability to perform real-time data analysis with KSQL
Understanding of data serialization and deserialization
Knowledge of Avro schemas
Experience with using Schema Registry in Kafka
Ability to handle schema evolution
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LEVEL 5

Expert

Understanding of Kafka's storage system
Knowledge of Kafka's replication protocol
Familiarity with Kafka's request processing flow
Understanding of Kafka's partition assignment strategy
Proficiency in using Kafka's debugging tools
Experience in analyzing Kafka logs
Ability to identify and resolve performance bottlenecks
Knowledge of common Kafka issues and their solutions
Understanding of distributed system design principles
Ability to choose appropriate Kafka configurations for different use cases
Experience in capacity planning for Kafka
Knowledge of best practices for scaling Kafka
Understanding of KStream, KTable and GlobalKTable
Ability to perform stateful operations
Experience in handling late data and out-of-order data
Knowledge of windowing operations
Experience in setting up and configuring monitoring tools
Ability to interpret monitoring data and take appropriate actions
Understanding of Kafka's JMX metrics
Knowledge of alerting strategies for Kafka

Skill Overview

  • Expert2 years experience
  • Micro-skills92
  • Roles requiring skill4

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