As a Kafka Developer, you might have faced challenges deploying Kafka, particularly across the hybrid cloud. Due to this, many streaming data customers choose to employ a Kafka service, which offloads infrastructure and system administration to a service provider. Hence a new service was introduced – “Kafka as a Service” to solve the above challenges. Kafka Service is a Cloud-based version of Apache Kafka. Here, the Kafka infrastructure is provisioned, built, and maintained by a third-party provider. This makes it simple to deploy Kafka without requiring experience in Kafka infrastructure or management. So, now you can spend less time maintaining infrastructure and focus more on other priority tasks.
This article provides a comprehensive overview of Kafka as a Service. You will learn more about Managed Services and discuss how they are different from hosted services. In addition, you will understand the need for Kafka as a Service and explore the key features and benefits offered by it. At the end of this article, you will discover the popular options offered by Apache Kafka as a Service with Confluent Cloud.
Table of Contents
What is Apache Kafka?
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Apache Kafka is a distributed Event Streaming platform that allows applications to manage large amounts of data quickly. Its fault-tolerant, highly scalable design can handle billions of events with ease. The Apache Kafka framework is a Java and Scala-based distributed Publish-Subscribe Messaging system that receives Data Streams from several sources.
The capacity of Kafka to handle Big Data input volumes is a distinct and powerful benefit. With minimum downtime, it can easily and swiftly scale up and down. Because of its minimum data redundancy and fault tolerance, Kafka has increased in popularity among other Data Streaming systems.
Key Features of Apache Kafka
Apache Kafka has become quite popular, because of its features, such as ensuring uptime, making scaling simple, and handling large volumes of data. Let’s have a look at some of its most useful features:
- High Scalability: Kafka’s partitioned log model distributes data over several servers, allowing it to scale beyond a single server’s capability.
- Low Latency: As Kafka separates data streams, it has very low latency and high throughput.
- Fault-Tolerant & Durable: Partitions are distributed and duplicated across several servers, and data is written to the disc. This makes data fault-tolerant and long-lasting by protecting it against server failure. The Kafka cluster can withstand master and database failures. It can restart the server on its own.
- High Extensibility: A lot of additional applications have developed connectors for Kafka. This makes it possible to add more features in a matter of seconds. Check out how you can integrate Kafka with Redshift and Salesforce.
- Metrics and Monitoring: For tracking operational data, Kafka is a popular solution. This necessitates collecting data from a variety of apps and combining it into consolidated feeds with analytics. To read more about how you can analyze your data in Kafka, refer to Real-time Reporting with Kafka Analytics.
What is Streaming Data and Why does it matter?
Streaming data is the continuous flow of real-time information, which is frequently represented as a running log of changes or events in a data set.
Data streaming use cases can include any situation that necessitates a real-time response to events, such as financial transactions, Internet of Things (IoT) data, or hospital patient monitoring.
Using the event-driven architecture model, software that interacts with streaming data allows data to be processed as soon as it arrives.
Event consumers in an event streaming model can read from any part of the stream and join the stream at any time. A simple data streaming event consists of a key, a value, and a timestamp. A data streaming platform receives events and processes or transforms them. In addition, event stream processing can be used to detect patterns in data streams.
Why Kafka as a Service is Important?
Despite its many powerful features and benefits, Kafka is difficult to deploy at scale. In production, on-premises Kafka clusters are difficult to set up, expand, and maintain. For example, you must supply workstations and configure Kafka while setting up an on-premises architecture to run Kafka. You must also plan the cluster of distributed servers to assure availability, maintain data storage and security, set up monitoring, and scale data wisely to handle load variations. Then you have to keep that infrastructure running by replacing systems when they fail and patching and upgrading it regularly.
For the above challenges, many Apache Kafka customers shift to Managed Cloud Service, in which infrastructure and system maintenance are delegated to a third party. Enterprises can immediately benefit from Kafka as a Service by deploying the platform on any architecture, including On-Premises, Cloud, and Hybrid. You can leverage Apache Kafka’s large ecosystem of tools and connectors. It also offers exceptional scale, robustness, and performance when combined with Connect, ksqlDB, and KStream API.
Differences between Managed Services & Hosted Solutions in Apache Kafka
The quickly changing IT landscape, along with the complicated and ever-increasing expectations of clients, has resulted in a wide range of hosting alternatives. There are several options available, ranging from simple Hosting Solutions and Managed Services to Cloud Services and Hybrid Solutions. In this section, you will understand a few differences between Hosted Solutions and Managed Services, to help you select the best solution for your Kafka.
Hosted Solutions
Users of Hosted Solutions must indicate how much storage per broker is needed. This gives the user control over size, which is a difficult process that frequently leads to over-provisioning as a means of compensating for the lack of accuracy. Over-provisioning means that users will be charged for resources they do not use. As a result, you’ll be in charge of software monitoring and management. This also involves server maintenance and troubleshooting. So, if you have a staff with the proper software skills and knowledge, hosting solutions are the way to go.
The main difference between Hosted Solutions and Managed Services is that the user is still responsible for Apache Kafka management as if it were operating on-premises. It’s just as difficult to construct event streaming apps using hosted solutions as it is to manage Kafka yourself.
Managed Services
Managed Services provide you the ability to use Apache Kafka without having to learn how to use it. This is a feature of genuine Managed Services, which enables developers to focus on what matters most. Managed Services, as opposed to Hosting Solutions, provide a greater number of alternatives for data backups, system and software administration, and even operating system and application management.
A Managed Service should never place the user in the driver’s seat while making difficult decisions, such as choosing the specifics of hardware, extracting information about the network and computing infrastructure, inquiring about the number of servers available, and asking the user to update Kafka.
Hevo Data, a No-code Data Pipeline, helps load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources. You can use Hevo Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system. It loads the data onto the desired Data Warehouse/destination and transforms it into an analysis-ready form without having to write a single line of code.
Hevo’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. Hevo supports two variations of Kafka as a Source. Both these variants offer the same functionality, with Confluent Cloud being the fully-managed version of Apache Kafka.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled securely and consistently with zero data loss.
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- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
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Top Features & Benefits of Kafka as a Service on Confluent Cloud
Users can leverage Confluent Cloud to swiftly harness the power of Kafka as a Service. This allows them to develop event-driven applications and offer the high-quality digital experiences that their consumers demand. For businesses trying to improve customer experience, grow their company, and sharpen their competitive edge, Kafka as a Service provides a tremendous benefit.
This section provides the robust features offered by Kafka as a Service with Confluent Cloud:
1) All-in-One Event Streaming Applications
The Kafka Managed Service provides a one-stop shop for Event Streaming applications. It includes all of the tools required to create Event Streaming applications, so there’s no need to go far. Clients API, Kafka Connect, Schema Registry, Kafka Streams, and many more features are included in Kafka as a Service.
Confluent Cloud provides ready-to-use connections, eliminating the need for Developers to create and manage their own. Postgres, MySQL, Oracle cloud storage (GCP Cloud Storage, Azure Blob Storage, and AWS S3), cloud functions (AWS Lambda, Google Cloud Functions, and Azure Functions), Snowflake, Elasticsearch, and others are among the integrations offered. More significantly, Confluent Cloud offers Kafka Connect as a Service, eliminating the requirement for users to manage their own Kafka Connect clusters.
2) Interoperability
Full support for popular tools and frameworks used by developers working with Apache Kafka and all of its features is referred to as interoperability. Confluent Cloud offers full-fledged Kafka clusters, allowing client applications to take advantage of all of Kafka’s and Confluents’s advantages. Furthermore, the code written by Developers is the same code used for Kafka clusters running On-Premises, thus no new tools or frameworks are required.
3) Support by Kafka Professionals
Using Managed Services that are supported by Kafka specialists also means getting better support. You can submit support requests for difficulties and ask sophisticated questions regarding the fundamental technology and how it might be tweaked to improve application performance. Experts in Apache Kafka are better equipped to deliver precise and effective responses in a timely manner.
Image Source: confluent.io
Confluent Cloud is the top provider of Apache Kafka support. It supports programming languages such as Java, C/C++, Go,.NET, Python, and Scala with native clients. Confluent, which has an engineering staff devoted to this, develops and supports all of these clients.
4) No Vendor Lock-In
Users nowadays are considerably more careful if the Managed Service allows you to switch from one Cloud provider to another. As a result, Managed Apache Kafka services must accommodate a variety of Cloud providers.
Using Managed Services that handle Apache Kafka similarly across multiple Cloud providers will help you avoid situations like these. To do this, these Managed Services must support numerous Cloud providers while also ensuring that the Developer experience is consistent across all of them.
This is excellently delivered via Confluent Cloud. It not only works with major cloud providers like Google Cloud Platform, Azure, and AWS, but it also guarantees that the Developer experience is consistent across all of them. Users can use any of these cloud providers, and they can operate several clusters on various Cloud providers at the same time.
Popular Options Offered by Apache Kafka as a Service with Confluent Cloud
Using Kafka as a Service provider is one method to assure a professionally built and maintained Kafka deployment. Any managed Kafka service should include round-the-clock monitoring, preemptive maintenance, and the best uptime guarantee. Confluent Cloud is the only Kafka service designed by Kafka Developers. It’s completely managed for maximum deployment simplicity. Confluent Cloud is designed to enable enterprise-scale, mission-critical applications by being scalable, robust, and secure.
Let’s discover some of the options offered by Kafka as a Service with Confluent Cloud:
1) Kafka as a Service with AWS Managed Streaming for Kafka (MSK)
Image Source: techcrunch.com
Amazon offers Managed Streaming for Kafka (MSK) – Kafka Service to its AWS customers. AWS Developers can now simply install Kafka on AWS systems and start building streaming pipelines with technologies like Spark Streaming in just a few minutes. They don’t have to worry about maintaining Kafka brokers, Zookeeper, or anything else, so they can focus on developing streaming pipelines. Streaming development is now a lot easier with a reduced turnaround time as a result of this. The price is a little convoluted, but a basic Kafka instance will start at $0.21 per hour, much like everything else on AWS.
2) Kafka as a Service with Google Cloud Platform (GCP)
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Confluent Cloud on Google Cloud offers fully managed Apache Kafka as a Service, allowing you to focus on developing applications rather than cluster management.
Image Source: confluent.io
Users may combine the premier Kafka service with GCP services to support a variety of use cases with the new Confluent Cloud on GCP. You can do the following:
- Analyze data in real-time and at a vast scale. You can stream data to BigQuery, Cloud Machine Learning Engine, and TensorFlow, which are all part of Google Cloud’s big data offerings.
- Create applications that are triggered by events. You can combine Google Cloud Functions, App Engine, and Kubernetes with Confluent Cloud pub/sub-messaging services.
- Provide a robust connection to the Cloud. You can accelerate multi-Cloud adoption by creating a real-time data conduit between data centers and Google Cloud.
3) Kafka as a Service with Microsoft Azure
Image Source: confluent.io
Microsoft Azure in Confluent Cloud allows Developers to construct Event Streaming applications with Apache Kafka using Azure as a public cloud. Developers can concentrate on designing applications rather than managing infrastructure using Confluent Cloud on Azure. You can also integrate with Azure SQL Data Warehouse, Azure Data Lake, Azure Blob Storage, Azure Functions, and other Azure services using prebuilt Confluent connectors.
Developers can use their existing billing service on Azure to get started with Confluent Cloud. You only pay for what you stream by using consumption-based pricing. Confluent Cloud additionally provides a fully customizable, dedicated solution for gigabyte-per-second scaling, indefinite detention, and private networking. Confluent Cloud on Azure applications can achieve sub-25 millisecond latency even at gigabytes per second scale.
Managed Apache Kafka vs. DIY: How to Choose?
Any organization considering using Apache Kafka as an event streaming solution should first conduct a thorough audit of all IT resources. SMB and enterprise support for websites, mobile apps, and IoT have different requirements. Many large enterprises use Kafka event streaming as their corporate CNS, integrating all products and services in real-time analytics.
It is critical to consider how API data from software applications, devices, and users will be generated in order to create event records. Each IT department will need to develop a strategy for recording platform events as data points, as well as additional processing pipelines for the data to be used in interactive constructs such as DXPs or real-time logistical searches. The overall cost of implementation includes the development of custom software to support event streams.
Following the programming of the stream events, IT managers will be able to calculate the expected message queue processing requirements based on the web/mobile application or IoT products supported. Estimate the total number of events per second, minute, hour, and so on that are expected on the network. Based on this estimate, it should be possible to begin determining the level of hardware support required for real-time processing of event streams from all applications, as well as the persistent storage capacity required to store the data over time.
Managed Apache Kafka vs DIY: Pros & Cons
Following an IT audit, a company should have a better idea of the budget, time, and resources required to implement usable event streaming infrastructure and analytics. With this information in hand, the next step is to weigh the benefits and drawbacks of the various event-streaming implementation options.
Managed Cloud solutions are ideal for organizations with smaller IT teams that lack the time and resources to deploy and maintain custom event streaming infrastructure. They can instead pay a monthly subscription to have experts deploy, scale, log, and monitor their infrastructure. DIY approaches, on the other hand, can take months or even years to implement but provide greater configuration flexibility.
1) Managed Cloud Event Streaming Architecture
A) Pros
- Managed cloud services assist SMBs in adopting Apache Kafka event streaming architecture on an organizational level more quickly, affordably, and efficiently for engineering teams without the risk of in-house development.
- Rather than spending money to staff and support 24/7 data center operation teams, invest in custom software solutions for event stream messaging using Kafka APIs.
- Adopt industry best practices and enterprise security for your data on managed event streaming platforms like Keen, which offer persistent storage and real-time analytics.
B) Drawbacks
- Building custom data infrastructure often makes more financial sense for teams with sufficient IT resources than paying a recurring cost.
- Custom infrastructure should be considered for use cases with extremely high event volumes and performance requirements.
2) DIY Apache Kafka Event Streaming Architecture
A) Pros
- DIY solutions for Apache Kafka event streaming architecture can be managed on VMware, OpenStack, and Kubernetes platforms in public, private, hybrid, or multi-cloud environments.
- Business organizations can use tools such as Apache Storm, Spark, Flink, and Beam to create custom AI/ML processing for web/mobile app or IoT integration needs.
- With embedded real-time data analytics, you can support high-performance web/mobile applications, IoT networks, enterprise logistics, and industrial manufacturing facilities.
B) Drawbacks
Due to the complexity of implementing a usable solution and scope creep, DIY solutions carry a higher risk of increasing costs and delaying timelines for smaller IT teams.
DIY solutions necessitate dedicated resources for deployment, scaling, logging, and monitoring, which are inaccessible to smaller IT departments.
Conclusion
This article provided a holistic overview of Kafka as a Service. You learned more about Managed Services and discussed how they are different from hosted services. In addition, you understood the need for Kafka as a Service and explored the key features and benefits offered by it. At the end of this article, you discovered the popular options offered by Apache Kafka as a Service with Confluent Cloud.
However, extracting complex data from Apache Kafka can be quite challenging and cumbersome. If you are facing these challenges and are looking for some solutions, then check out a simpler alternative like Hevo.
Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources, into your Data Warehouse to be visualized in a BI tool. You can use Hevo Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system. Hevo is fully automated and hence does not require you to code.
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Shubnoor is a Data Analyst with extensive expertise in market research, and crafting marketing strategies for data industry. At Hevo, she specialized in developing connector integrations and product requirement documentation for multiple SaaS sources.