Today, digital innovations are taking place rapidly. Companies, be it small or big, are using data and analytics way differently than a few years ago. One of the biggest drivers of rapid tech evolution is cloud computing. Cloud allows for quick configuration of IT setup without having to procure it in-house. This allows companies to respond faster to customer demands and changes in market conditions. 

Out of the different public cloud services, the maximum velocity of growth is from SaaS or Cloud Management Services. When shifting to the cloud, companies would explore which SaaS product would be apt for them. They would prefer to go with a SaaS platform that will manage applications, data, middleware, storage, servers, and networking.

Forward-looking organizations are leveraging solutions like cloud-native Kafka to develop stream processing applications and elevate the customer experience. 

Why adopt Apache Kafka?

Apache Kafka is a distributed publish-subscribe messaging system. It’s a fast, scalable, and reliable alternative to other solutions in the market. Kafka is the best option for you if you want to work with real-time data. The benefits of using Kafka are as below. 

  • Kafka is super scalable. Since it’s a distributed system, it can scale easily with minimum downtime. It can manage many terabytes of data without incurring much of an overhead. 
  • Kafka is reliable; it replicates data and supports numerous subscribers. Plus, it inherently balances consumers in the events of failure. It means Kafka is more reliable than other messaging services out there. 
  • Kafka is very durable. It persists the messages on the disk that gives intra-cluster replication. As a result, you get a highly durable messaging system. 
  • Kafka delivers excellent performance. It offers high throughput for publishing and subscribing both. It incorporates disk structures that can provide a constant level of performance. Thus, the performance stays at peak even while dealing with large terabytes of stored messages.  

The Steps Towards Embracing Cloud-Native Kafka
A typical journey from a legacy platform to cloud-native Kafka would look something like this

  • Rolling upgrade 

    A rolling upgrade is carried out to prevent downtime, data loss, and preserve business continuity. A test methodology is adopted to validate new versions. These verify performance improvements and pass the upgrade fit for rolling out

  • Reliable service availability 

    Comprehensive monitoring is carried out with the help of Kafka Health Check, Datadog, and DRUID for every Kafka cluster. Since the collection of every single metric entails a high-cost overhead, it is important that this is self-managed as a part of the Kafka native-cloud integration.

  • Self-serve provisioning 

    There will be a need to deploy the Kafka cluster with other cluster setups on demand. A Kafka expert can drive this agility, as this technique of provisioning dedicated clusters on demand is a complex process 

  • Scaling the capacity of dedicated clusters 

    The clusters need to be re-balanced once the new clusters are configured. An expert like Confluent and its partners have a tool called Auto Data Balancer. It helps re-balance the load and prevent the overloading of a single broker.

  • Cost-efficient data storage     

    The big data boom has made it necessary to store large amounts of data. Experts would utilize the prowess Tiered storage - Kafka’s object storage, to store infinite data. This way, companies can offload data from brokers and archive it to remote and cost-efficient storage 

  • Fully managed components

    Experts would provide fully managed connectors to help easily move data between other systems to Kafka. This additional component will enable a successful transition to the Kafka streaming platform.  

Addressing the Complex Cloud Transitioning Process
Many of the large corporations use Apache Kafka to develop streaming applications. It gives real-time data pipelines that are fast, scalable, and reliable. However, Kafka is a bit complicated to deploy and manage. That’s why you should seek the expertise of companies that helps you adopt the platform. 

CIGNEX Datamatics is one such forward-looking company that can help deploy Apache Kafka with its Confluent partnership. Thus, companies can leverage the various advantages of a robust and scalable event streaming platform. You can get real-time data streaming applications to obtain easy access to enterprise data at a faster rate while maintaining data integrity.

This industry-leading competency helps companies get more out of their big data and IoT initiatives and perform real-time data streaming and analytics. CIGNEX Datamatics is a Confluent Plus Partner, the company founded by the creators of Apache Kafka. Their combined tech proficiencies help enterprises solve the challenges of data integration by building stream processing applications.

To Summarize

Confluent cloud and Kafka can fulfill real-time data streaming requirements of your organization. The best part of about applications built using cloud-native computing is they provide quick access to enterprise data. Such solutions also maintain the integrity of all your business data. Connect with us at CIGNEX Datamatics if you want to incorporate next-gen cloud strategies.