This article is the first in a three part series where we start with the basics of Apache Kafka and move to actual production grade implementations.
Kafka is an Apache open-source, distributed event processing platform. Kafka enables the creation of an unbounded number of messages to be sent through a scalable, fault-tolerant flow that can process, filter, or route data. Kafka is best suited for enterprise-level stream processing applications that require high throughput and low latency data transfer.
Event-driven Architecture is a software design pattern where events can be triggered by components and handled by other components that subscribe to them. Events can also be built into the system, assigning specific tasks to emitters. Emitters are a vital component of event-driven workflows.
The event-driven communication pattern is an alternative to the traditional request-response model of communication. Rather than taking turns to send messages, this pattern allows for simultaneous, bi-directional communication. They enable the user to define a specific action that will occur when a particular event is triggered, enabling the user to create powerful automated workflows with ease.
For decades, the request-response communication model has been the default for websites. Websites traditionally use input boxes for users to input information and then provide them with a response. However, this newer approach is more conversational and offers real-time feedback on user input.
With data streaming, companies and organizations can receive vast amounts of data as it flows by and study it. Data collected from customers empower businesses to make more informed decisions about their customers. In turn, it helps the team provide the products and services that customers want.
Kafka handles large amounts of data, ensures high latency, and keeps data safe from crashes by providing fault tolerance.
The benefits of using Apache Kafka include:
High throughput: Apache Kafka can handle over 1 million messages per second (1,000 kmsg/s) on a single node; it even handles 100 million messages per second when run on clusters with multiple nodes.
Low latency: For every write, Apache Kafka produces an acknowledgment within 100 ms.
Fault tolerance: Apache Kafka can automatically heal from failures without any manual intervention.
Getting Started With Apache Kafka and DataOps
Many companies, including Netflix and LinkedIn, use Apache Kafka. This practice has resulted in a set of common pain points and movements within the developer community.
The concept of "DataOps" has been evolving lately because of the complexity and speed of today's data flow.
DataOps is a new term coined to describe how companies need to organize their data and operations around the idea of getting insights from data at speed. DataOps is great for scenarios where data needs to be processed quickly and in large volumes.
It's a movement that aims to bridge the gap between IT and business by focusing on data to improve productivity and efficiency – not just as a means for reporting or compliance.
To do this, DataOps focuses on operational agility across the entire lifecycle from design through building, deploying, integrating, monitoring, and maintaining.
Managed Platforms vs. On Premises Installations for Apache Kafka
Managed solutions have been around for a few years, and it provides a simple, reliable, and high-performance solution to Kafka implementation. The managed service handles the installation, configuration, upgrade of Kafka nodes and the overall operational management of the cluster. It can provide affordable pricing options to those who need to run Kafka clusters in production environments.
On Premises describes a more traditional approach where you have to manage your installation, configuration, and upgrade of Kafka nodes and the operational management of the cluster yourself. This option provides you with more flexibility but at a higher cost because it requires hiring experienced developers or operations staff for this purpose.
A managed platform is suitable for those who want third-party companies to host the managed platforms and provide the benefits of scalability, easier management, and higher performance.
Managed platforms for Apache Kafka are an excellent service that companies can leverage to manage low latency, high throughput data streams. The significant benefit of using these platforms is that they include all the necessary components from hardware, software, and operational expertise that come with a hefty price tag.
On-Prem installations offer better control over the configuration of Kafka clusters, higher performance, and better security.
When it comes to the deployment of Kafka clusters to enable you to scale out your operating model across clusters of commodity hardware, there are several options available.
Lenses.io is one open-source alternative that you can install on a local computer and scale up to Enterprise level support. It provides the opportunity to leverage a hybrid execution model and try before you buy style of experimentation. Lenses.io can be used to run Apache Kafka on AWS. It is a tool that will help you install, configure and manage Kafka clusters. The tutorial below will cover working with Kafka Clients and autoscaling with
using a python client library.
A Kafka client is a program that enables applications to read from and write to the Kafka cluster. There are two main types of Kafka clients: Producer, and Consumer
Producer: The producer is a client that can send messages to the cluster and define what partition and can offset those messages to specific partitions. A producer can also request metadata for all or some of the partitions in a topic.
Consumer: The consumer is a client that reads messages from the Kafka cluster and can define what partition and offset where those messages should be read from. A consumer can also subscribe to all or some partitions in a topic with metadata. Consumers listen for new announcements sent by producers who have offsets after their current position.
Scaling Apache Kafka (Preview)
In a later post, we will work with a canonical use case for Apache Kafka and set it up primarily with python SDKs. One challenge aspect of Apache Kafka is its autoscaling ability, which may need to happen on a per-server basis. It is now common for organizations to build their clusters in the cloud that can scale to meet their needs.
provides an easy-to-configure, automatic scaling solution for Apache Kafka clusters. Lenses can provision and manage clusters in the cloud using Apache Mesos under the hood. It can provision additional nodes as needed, manage the load balancing, and automatically handle node failures. Lenses.io also provides hosting and management of Kafka clusters on AWS, Google Cloud, or Azure, which means it doesn't require any additional configuration on the customer side.
They also provide monitoring and metrics about the cluster's performance and health status for better insight into how it's working.
If you need to determine the benefit of running Lenses.io, you can use the Kafka project ROI calculator.