Kafka Consulting

• Kafka is a fast, horizontally scalable, fault-tolerant, distributed data streaming platform which provides a publisher-subscriber mechanism enabling processing and storing streams of data in a fault-tolerant way.
• Our experts have hands-on expertise in architecting, developing and managing Kafka clusters on Windows, Linux and cloud platforms.
• At Cazton, we help Fortune 500, large and mid-size companies with streaming best practices, Kafka development, consulting, recruiting services and hands-on training services.

Imagine a process which converts unstructured, unreadable pieces of information into something that is extremely valuable for your organization? information that gives you insights about your business, your products, customers and their preferences. Now imagine getting those insights in real time! We are talking about a process that gives you instant information about an active transaction. Such information is always valuable, isn't it?

Companies who often deal with Big Data have massive databases and millions or billions of files they are processing. Choosing the right technology to process such big data is a daunting task! Most importantly, obtaining real-time insights an uninterrupted supply of data to that processing platform is even more difficult. To help with this, a concept was created called Data Streaming. Data Streaming, as the name implies, is a stream or flow of raw data that is captured from multiple sources and sent for processing continuously. These streams of data hold great value as they contain real-time information about an ongoing transaction or process. Analyzing and processing such data streams makes an organization more efficient and opens up new opportunities.

What is Kafka?

Kafka is a fast, horizontally scalable, fault-tolerant, distributed data streaming platform originally started as an internal project at LinkedIn. It later became an open source project at in 2011. This project was originally written in Scala and Java. It provides a publisher-subscriber mechanism that enables processing and storing streams of data in a fault-tolerant way.

Kafka acts like a plugin technology that can be used with a wide range of technologies like Spark, Hadoop, Storm, HBase, Flink and many others for big data analytics. It can be used to build real-time streaming applications that react to streams to do real-time data analytics, transform, react, aggregate, join real-time data flows, and perform complex event processing. The most common use cases for Kafka include stream processing, messaging, website activity tracking, log aggregation and operational metrics.

Cazton has Kafka Consultants who can provide expert guidance for your big data streaming and processing requirements. Our experts have hands-on experience in standing up and administrating on-premise Kafka platform and managing Kafka clusters on Windows, Linux and cloud platforms like Azure, AWS & EMC. We are well-versed with Kafka API and understand the best practices for stream management and processing.

Kafka Infrastructure / Core Concepts

As we move ahead to know more about Kafka, it is important to understand some of its core concepts. Knowing these concepts will help you understand how Kafka works.

• Producers: A producer can also be termed as a publisher that is responsible for publishing messages to a Kafka cluster. They generate messages that are ingested into the Kafka system. Typical examples of producers would be your website, email system, customer database, application logs, etc.
• Consumer: As the name implies, a consumer is the one that consumes data. They subscribe to one or more Topic and consume published data by pulling it from Brokers. Consumers are usually a part of at least one consumer group that is associated with a topic.
• Message: A message represents the fundamental unit of information. It is a key-value pair that is stored as byte arrays.
• Topics: A collection of messages that belong to a particular category is called a Topic. The data sent by Producers are usually stored in Topics whereas Consumers who are interested in a particular topic subscribe to them.
• Partitions: Partitions are unique to Kafka and not found in traditional messaging systems. Each topic that contains a collection of messages is divided into multiple partitions. Kafka clusters use message keys to group multiple topics together. This scheme enables Kafka to dynamically scale the messaging infrastructure.
• Brokers: Each Kafka instance is called a Broker. It is responsible for receiving messages from producers, assigning offsets and finally saving messages to the disk. Based upon current hardware conditions, each Broker can easily handle thousands of partitions and millions of messages per second.
• Clusters: A collection of multiple brokers is called a Cluster. In this collection, one broker is termed as a Leader and the others are Followers. A leader is responsible for all read and write operations for the given partition and assigning partitions to other Follower brokers. When a Leader broker fails, Kafka automatically makes a Follower the new Leader.
• Zookeeper: Zookeeper is a distributed coordination service used by Kafka to store meta information about Kafka Cluster and Consumer client. It acts as a coordinator between Kafka brokers and consumers. It notifies the producer and consumer about the presence of any new broker in the Kafka system or failure of the broker in the Kafka system.

Benefits of using Kafka

Kafka is the most preferred platform in the field of messaging systems and data streaming. It is very easy to integrate Kafka in Hadoop and AWS environment. There are more benefits of choosing Kafka for your message streaming requirements.

• Highly Scalable: Kafka is a distributed data streaming platform that can be horizontally scaled across many clusters of servers thus avoiding any downtime. It is capable of handling terabytes of data within seconds.
• Highly Performant: It is capable of handling huge volumes of data using cheap servers and gives throughput of thousands of messages. It is capable of handling and processing messages within milliseconds thus making it a real-time streaming platform.
• Fault Tolerant: When messages are published to Kafka clusters, they replicate and store them on disks for a limited period of time. If a failure occurs, Kafka is easily able to resist that failure by using replicated data. Thus, it is fault tolerant and highly reliable when compared to other messaging systems.

How can Cazton help you with Kafka Consulting?

Cazton has Kafka Consultants who can provide expert guidance for your big data streaming and processing requirements. Our experts have hands-on experience in administrating on-premise Kafka platform and managing Kafka clusters on Windows, Linux and cloud platforms like Azure, AWS & EMC. We are well-versed with Kafka API and understand the best practices for stream management and processing.

Our Kafka Consultants have strong analytical and problem-solving skills. To name a few, our Kafka experts have hands-on experience with Big Data technologies that includes Hadoop, Spark, HIVE, HBase, Kafka, Impala, PIG, Zookeeper etc., document databases like Cassandra, Couchbase, MongoDB etc. and have a proven track record of building solid production level software that processes large streams of data. We have high-level expertise in programming languages like C#, Java, Scala, Python, and R, which make our experts a great resource for your business.