Apache Kafka — All You Need to Know
Due to the constant data generation, contemporary organizations require multiple servers to support their different business operations. Reliable and swift communication is also required between the servers to ensure data availability. This function is often done by data pipelines that act as means of data communication between the units.
However, since every data pipeline has individual requirements, managing a large number of pipelines can become a complicated task. Here Apache Kafka becomes relevant. Kafka specializes in stream processing, i.e., handling the streaming data. It was constructed with the sole purpose of making the management of data pipelines simple and stream processing more efficient.
What is Apache Kafka?
Kafka is an end-to-end event streaming platform. It collects data in real time from different sources, stores it, and processes it — both when the data is streaming and retrospectively. A messaging system like Apache Kafka makes managing data pipelines easier. This also includes the adding or removing of pipelines.
LinkedIn developed Apache Kafka in 2011. It was later donated to the Apache Foundation as an open-source project.
Apache Kafka can be described as a database; however, it is quite different from a relational database. It is so because the very purpose of a relational database is to store intentionally. Whereas, data storage is incidental in the case of Kafka since it is meant to keep a track of events only.
Working Principle
Before we get into how Kafka works, it is important to understand the purpose of messaging and its variations.
Messaging connects data by transmitting data records between different sources in an ecosystem. It has undoubtedly become essential to the data-driven application development process of today. There are two primary messaging models; Message queuing and Publish Subscribe messaging system. While a message queue model transmits a message to only one consumer on a topic, the Publish-Subscribe model can simultaneously send the information to multiple consumers.
Kafka is a distributed publish subscribe messaging system, but it also combines the properties of queue messaging in its partitioned log model. Understandably, Apache Kafka supports more than one server, which makes it highly scalable and faster.
When it comes to handling data pipelines, Apache Kafka works by decoupling a pipeline into two parts:
· Producer
· Consumer
Producers are the entities or client applications that generate data and stream them into a Kafka cluster. They are the processes that publish or write a message. Consumers refer to those entities that consume the produced data. As mentioned earlier, Kafka works according to a publish subscribe messaging system model; the communication depends on producers writing the data and consumers reading it. The data may subscribe to a particular topic. Apache Kafka completely decouples the producers from the consumers to achieve high levels of scalability.
The platform operates through multiple Kafka clusters. These Kafka clusters further constitute various servers. The servers are known as Kafka brokers. They ultimately contain the topics and the partitions related to the events. Since the system tends to replicate the data, similar copies of a data set exist within a Kafka cluster. Therefore, when a broker goes down (which is technically a node), the data is not really lost and can be retrieved from another Kafka broker.
Components of Apache Kafka
Kafka comprises a distributed architecture that supports both partitioning and replication. The architecture can be explained by discussing its components in detail.
Topics
Topics are the categories or feed names that keep records. A topic acts like a folder that contains events and their details. Topics in Kafka can also have numerous producers and consumers. They can also be further divided into several partitions.
Partitions
Partitions are the divisions of topics that can be linearly appended. Each partition has a sequential id, known as offset, which saves all the messages. Every new event containing the same key event gets added to its respective partition. Since Kafka follows a sequential form, it makes sure that consumers consume the events in an orderly manner, i.e., according to the sequence in which they were written or produced.
Message
A message refers to an event data, record, or transaction. Each message contains a topic and its partition along with other specifications like timestamps (when it was generated). It also carries a key-value pair which can be specified.
Zookeeper
In Kafka, a Zookeeper handles several tasks. It erects a controller that selects a broker for replication. If a broker goes down, it elects another broker. And if the controller goes down, the zookeeper chooses another controller. A zookeeper also determines:
- The number of topics in a broker
- Online and offline brokers
- Replication factors
- Topic Configuration
- Saving of metadata
- Coordination of internal processes
Major Features of Kafka
Apache Kafka provides the following dividends to its users:
High Throughput
The term throughput refers to the amount of streaming data flowing through a pipeline at a time. In Kafka’s case, the throughput is of two types; producer throughput and consumer throughput. Users can easily increase or decrease one of the throughputs by making changes in the server at the backend without causing downtime.
Linearly Scalable
You can easily increase the number of consumers or producers without shutting down the system. All you need to do is add a server and subscribe to a topic. Since Kafka is a distributed streaming platform, it can achieve unprecedented levels of scalability in terms of data communication. It can handle several terabytes of data without incurring downtime.
Durability
Kafka uses distributed commit logs and thus retains its messages on disk. This allows for intra-cluster replication. In this way, Apache Kafka provides a messaging system that is not only fault-tolerant but also durable and reliable.
Stream Processing
As data can be transformed in an ETL pipeline, the same thing can also be performed in Kafka to transform the data into a stream. Kafka provides a consumer API where data can be processed according to specifications (for example, time). Kafka also provides a Stream API application where users can carry out data aggregation and data joining. In this way, Kafka makes stream processing quite easier.
Zero Data Loss
Owing to its replication abilities, Kafka saves users from facing data losses. It owes this feature also to its capacity to connect to multiple subscribers. If a node goes down, another server can be used for data accessibility.
Use Cases of Kafka
Kafka is being used for multiple purposes in the present-day digital world. Some of the prominent use cases of Apache Kafka are given below:
· Messaging (Case in point: LinkedIn)
· Activity tracking, i.e., following different activities of a person on a platform. For example, time spent on different pages, engagement with different accounts, etc. (Case in point: LinkedIn)
· Log Aggregation (Cases in point: Airbnb and Spotify)
· Stream Processing (Case in point: Netflix for its recommendation system)
· Generating Metrics (Case in point: LinkedIn)
According to an account, 80% of the top fortune 100 companies are using Kafka for handling Big Data through streaming architecture.
Royal Cyber data engineers and scientists have years of hands-on experience working with Apache Kafka and its use cases. If you want to discuss your options regarding this technology, feel free to contact the team.
Final Verdict
Apache Kafka is well-suited to handle real-time events and convey the related data analytics of an organization. As a result, it has quickly become the go-to platform for businesses handling large volumes of data on a daily basis. This publish subscribe messaging system owes its popularity to its operational simplicity and brilliant support for streaming data pipelines and applications.
Author bio:
Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.
