Apache Kafka Certification Training helps you in learning the concepts about Kafka Architecture, Configuring Kafka Cluster, Kafka Producer, Kafka Consumer, Kafka Monitoring. Apache Kafka Certification Training is designed to provide insights into Integration of Kafka with Hadoop, Storm and Spark, understand Kafka Stream APIs, implement Twitter Streaming with Kafka, Flume through real life cases studies.

Goal: In this module, you will understand where Kafka fits in the Big Data space, and Kafka Architecture. In addition, you will learn about Kafka Cluster, its Components, and how to Configure a Cluster

Skills:
 
  • Kafka Concepts
  • Kafka Installation
  • Configuring Kafka Cluster
GoalKafka Producers send records to topics. The records are sometimes referred to as Messages. In this Module, you will work with different Kafka Producer APIs.
Skills:
  • Configure Kafka Producer
  • Constructing Kafka Producer
  • Kafka Producer APIs
  • Handling Partitions
Objectives:
At the end of this module, you should be able to:
  • Construct a Kafka Producer
  • Send messages to Kafka
  • Send messages Synchronously & Asynchronously
  • Configure Producers
  • Serialize Using Apache Avro
  • Create & handle Partitions
Goal: Applications that need to read data from Kafka use a Kafka Consumer to subscribe to Kafka topics and receive messages from these topics. In this module, you will learn to construct Kafka Consumer, process messages from Kafka with Consumer, run Kafka Consumer and subscribe to Topics

Skills:

  • Configure Kafka Consumer
  • Kafka Consumer API
  • Constructing Kafka Consumer

Objectives: At the end of this module, you should be able to:

  • Perform Operations on Kafka
  • Define Kafka Consumer and Consumer Groups
  • Explain how Partition Rebalance occurs
  • Describe how Partitions are assigned to Kafka Broker
  • Configure Kafka Consumer
  • Create a Kafka consumer and subscribe to Topics
  • Describe & implement different Types of Commit
  • Deserialize the received messages
Goal: Apache Kafka provides a unified, high-throughput, low-latency platform for handling real-time data feeds. Learn more about tuning Kafka to meet your high-performance needs.
Skills:
  • Kafka APIs
  • Kafka Storage
  • Configure Broker
Goal:  Kafka Cluster typically consists of multiple brokers to maintain load balance. ZooKeeper is used for managing and coordinating Kafka broker. Learn about Kafka Multi-Cluster Architectures, Kafka Brokers, Topic, Partitions, Consumer Group, Mirroring, and ZooKeeper Coordination in this module.
Skills:
  • Administer Kafka
Objectives:
At the end of this module, you should be able to
  • Understand Use Cases of Cross-Cluster Mirroring
  • Learn Multi-cluster Architectures
  • Explain Apache Kafka’s MirrorMaker
  • Perform Topic Operations
  • Understand Consumer Groups
  • Describe Dynamic Configuration Changes
  • Learn Partition Management
  • Understand Consuming and Producing
  • Explain Unsafe Operations
Goal: Learn about the Kafka Connect API and Kafka Monitoring. Kafka Connect is a scalable tool for reliably streaming data between Apache Kafka and other systems.
Skills:
  • Kafka Connect
  • Metrics Concepts
  • Monitoring Kafka
Objectives: At the end of this module, you should be able to:
  • Explain the Metrics of Kafka Monitoring
  • Understand Kafka Connect
  • Build Data pipelines using Kafka Connect
  • Understand when to use Kafka Connect vs Producer/Consumer API
  • Perform File source and sink using Kafka Connect

The four major components of Kafka are:

  • Topic – a stream of messages belonging to the same type
  • Producer – that can publish messages to a topic
  • Brokers – a set of servers where the publishes messages are stored
  • Consumer – that subscribes to various topics and pulls data from the brokers.

Messages contained in the partitions are assigned a unique ID number that is called the offset. The role of the offset is to uniquely identify every message within the partition.

Consumer Groups is a concept exclusive to Kafka.  Every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.

Kafka uses Zookeeper to store offsets of messages consumed for a specific topic and partition by a specific Consumer Group.

No, it is not possible to bypass Zookeeper and connect directly to the Kafka server. If, for some reason, ZooKeeper is down, you cannot service any client request.

Every partition in Kafka has one server which plays the role of a Leader, and none or more servers that act as Followers. The Leader performs the task of all read and write requests for the partition, while the role of the Followers is to passively replicate the leader. In the event of the Leader failing, one of the Followers will take on the role of the Leader. This ensures load balancing of the server.

Replicas are essentially a list of nodes that replicate the log for a particular partition irrespective of whether they play the role of the Leader. On the other hand, ISR stands for In-Sync Replicas. It is essentially a set of message replicas that are synced to the leaders.

Replication ensures that published messages are not lost and can be consumed in the event of any machine error, program error or frequent software upgrades.

It means that the Follower is unable to fetch data as fast as data accumulated by the Leader.

Since Kafka uses ZooKeeper, it is essential to initialize the ZooKeeper server, and then fire up the Kafka server.

  • To start the ZooKeeper server: > bin/zookeeper-server-start.sh config/zookeeper.properties
  • Next, to start the Kafka server: > bin/kafka-server-start.sh config/server.properties
Goal: In this module, you will learn about Apache Hadoop, Hadoop Architecture, Apache Storm, Storm Configuration, and Spark Ecosystem. In addition, you will configure Spark Cluster, Integrate Kafka with Hadoop, Storm, and Spark.
Skills:
  • Kafka Integration with Hadoop
  • Kafka Integration with Storm
  • Kafka Integration with Spark
Objectives:
At the end of this module, you will be able to:
  • Understand What is Hadoop
  • Explain Hadoop 2.x Core Components
  • Integrate Kafka with Hadoop
  • Understand What is Apache Storm
  • Explain Storm Components
  • Integrate Kafka with Storm
  • Understand What is Spark
  • Describe RDDs
  • Explain Spark Components
  • Integrate Kafka with Spark
 Objectives:
At the end of this module, you should be able to,
  • Understand Flume
  • Explain Flume Architecture and its Components
  • Setup a Flume Agent
  • Integrate Kafka with Flume
  • Understand Cassandra
  • Learn Cassandra Database Elements
  • Create a Keyspace in Cassandra
  • Integrate Kafka with Cassandra
  • Understand Talend
  • Create Talend Jobs
  • Integrate Kafka with Talend
Goal: In this module, you will work on a project, which will be gathering messages from multiple
sources.
Scenario:
In E-commerce industry, you must have seen how catalog changes frequently. Most deadly problem they face is “How to make their inventory and price
consistent?”.
There are various places where price reflects on Amazon, Flipkart or Snapdeal. If you will visit Search page, Product Description page or any ads on Facebook/google. You will find there are some mismatch in price and availability. If we see user point of view that’s very disappointing because he spends more time to find better products and at last if he doesn’t purchase just because of consistency.
Here you have to build a system which should be consistent in nature. For example, if you are getting product feeds either through flat file or any event
stream you have to make sure you don’t lose any events related to product specially inventory and price.
If we talk about price and availability it should always be consistent because there might be possibility that the product is sold or the seller doesn’t want to sell it anymore or any other reason. However, attributes like Name, description doesn’t make that much noise if not updated on time.
This Project enables you to gain Hands-On experience on the concepts that you have learned as part of this Course.
You can email the solution to our Support team within 2 weeks from the Course Completion Date. Edureka will evaluate the solution and award a Certificate with a Performance-based Grading.
Problem Statement:
You are working for a website techreview.com that provides reviews for different technologies. The company has decided to include a new feature in the website which will allow users to compare the popularity or trend of multiple technologies based on twitter feeds. They want this comparison to happen in real time. So, as a big data developer of the company, you have been task to implement following things:
• Near Real Time Streaming of the data from Twitter for displaying last minute’s count of people tweeting about a particular technology.
• Store the twitter count data into Cassandra.

Within the Producer, the role of a Partitioning Key is to indicate the destination partition of the message. By default, a hashing-based Partitioner is used to determine the partition ID given the key. Alternatively, users can also use customized Partitions.

QueueFullException typically occurs when the Producer attempts to send messages at a pace that the Broker cannot handle. Since the Producer doesn’t block, users will need to add enough brokers to collaboratively handle the increased load.

The role of Kafka’s Producer API is to wrap the two producers – kafka.producer.SyncProducer and the kafka.producer.async.AsyncProducer. The goal is to expose all the producer functionality through a single API to the client.

Even though both are used for real-time processing, Kafka is scalable and ensures message durability.

These are some of the frequently asked Apache Kafka interview questions with answers. You can brush up on your knowledge of Apache Kafka with these blogs.

Apache Kafka is a publish-subscribe open source message broker application. This messaging application was coded in “Scala”. Basically, this project was started by the Apache software. Kafka’s design pattern is mainly based on the transactional logs design.
For detailed understanding of Kafka, go through, Kafka Tutorial

The most important elements of Kafka are:

Kafka Interview Questions- Components of Kafka

  • Topic –

Kafka Topic is the bunch or a collection of messages.

  • Producer –

In Kafka, Producers issue communications as well as publishes messages to a Kafka topic.

  • Consumer –

Kafka Consumers subscribes to a topic(s) and also reads and processes messages from the topic(s).

  • Brokers –

While it comes to manage storage of messages in the topic(s) we use Kafka Brokers.
For detailed understanding of Kafka components, go through, Kafka – Architecture

There is a sequential ID number given to the messages in the partitions what we call, an offset. So, to identify each message in the partition uniquely, we use these offsets.

The concept of Consumer Groups is exclusive to Apache Kafka. Basically, every Kafka consumer group consists of one or more consumers that jointly consume a set of subscribed topics.
For details, follow the link: Kafka Consumer Group

Apache Kafka is a distributed system is built to use Zookeeper. Although, Zookeeper’s main role here is to build coordination between different nodes in a cluster. However, we also use Zookeeper to recover from previously committed offset if any node fails because it works as periodically commit offset.

It is impossible to bypass Zookeeper and connect directly to the Kafka server, so the answer is no. If somehow, ZooKeeper is down, then it is impossible to service any client request.

Apache Kafka has 4 main APIs:

  1. Producer API
  2. Consumer API
  3. Streams API
  4. Connector API

Mainly, Kafka Consumer subscribes to a topic(s), and also reads and processes messages from the topic(s). Moreover, with a consumer group name, Consumers label themselves. In other words, within each subscribing consumer group, each record published to a topic is delivered to one consumer instance. Make sure it is possible that Consumer instances can be in separate processes or on separate machines.