Confluent may be a major player in the data streaming game, but it isn’t the only option. If you aren’t happy with the features Confluent offers, or you just want to know what else is out there, then we have a few other choices for you.
Streaming large amounts of data at once can be helpful, because in this digital world, our data is always on the move. Confluent gives you the toolset to store and process your data and then utilize it as necessary for a variety of applications. The stream processing tools you get with this platform will greatly expand your ability to simplify data, and make it more usable. You can pour vast amounts of data into the platform, and then manage it in many different ways.
As powerful as Confluent is, it isn’t the only game in town, and we want to share with you some of the best data platforms that are similar to Confluent. Some of these may offer better scalability to better suit a growing business. Some do an excellent job of protecting your data against accidental loss, and others accommodate virtual networks much better.
Strimzi
Like Confluent, Strimzi works with the Kafka program to manage your data. Where they differ is their ability to make data very simple and accessible. Operators using Strimzi will be able to make topics and clusters quickly, and manage these with ease. Configuring these different data packets is a breeze with Strimzi, compared to many of its competitors. You can also quickly access information on any topic in your data warehouse with this application.
There are no load balancers with Strimzi, however, which means that external access is somewhat limited. The security it offers is pretty basic, but it will be good enough for most uses. You will receive alerts for security issues, and be able to monitor all of the data effortlessly as soon as you set up Strimzi.
Postgres
This is a very capable tool for analyzing data in real time. It offers powerful data streaming functionality and a variety of analytical tools. You can easily migrate Postgres to BigQuery for data transfers. It’s ideal for financial institutions that need to examine large amounts of data without having to break down the data into smaller chunks. With that kind of streaming and processing power, your company can quickly react to changing and emerging market trends, making smart choices before they become obvious to less well equipped competitors. That can give you a serious competitive edge.
The standout feature of Postgres is its Real-Time ETL (Extract, Transform, Load), which makes it one of the most robust and useful analytical tools in data streaming. Once you use it to send large amounts of data very quickly, you won’t want to go back to slower, more ponderous systems. Postgres makes you far nimbler with your data than you would be otherwise, which dramatically improves your business’ performance in risk assessment, fraud detection, and trading analytics.
Quix
Some of the platforms we are looking at here are limited as to which tools they work with. Quix is only going to function alongside the Quix Streams library, but it is a fully equipped data streaming system that works remarkably well. Quix provides automatic installation when you tell it what kind of dependencies you need. It is excellent at separating your resources and prioritizing what is used and when it is used. Your data streaming can be accomplished through Quix as a serverless system, and it handles functions like sourcing, processing, and analytics in a single location. External APIs can be connected and provide data to Quix using the connector toolset already provided. Quix also includes a full set of monitoring functions that create log entries, monitor your infrastructure, and more.
Azure Event Hubs
This streaming platform runs off of Microsoft Azure, and offers a simple method of streaming data in real time. It contains a fully-realized toolset that secures your data and builds data stores through Kafka’s protocol. You won’t even need to change your applications’ code to make the platform function with Kafka.
Azure Event Hubs works as an excellent alternative to Confluent mostly because of its scalability design. As your business expands and your data streaming needs get bigger and more demanding, Azure Event Hubs can keep up. It has a handy Auto-Inflate feature that can be set to scale with your needs. This platform does a lot of the work for you, handling the Kafka and Zookeeper clusters on its own to create, manage, and moderate them as necessary. Of course, the real-time processing tools are really powerful, and you can stream several data flows at the same time without losing much speed at all. As one of the more flexible tools of its kind, we have to include Azure Data Hubs in this list.
Aiven
Aiven is the last of the data streaming platforms we want to talk about. It lets you run a Kafka service on your web console and in several other ways- whatever is most convenient for you. Aiven gives you more than just programmer functionality, though, as you can hook outside data connections and operate it with tools like Datadog for observation.
Aiven comes with some decent customer support right out of the box, though you can upgrade for improved support, if you like. The included monitoring tools are dependable and work flawlessly as cooperative units, creating logs and providing alerts as necessary.
You don’t have to feel boxed in by the limitations of Confluent. There are some great data streaming platforms out there that offer all sorts of functionality and features very different from what most people use. We encourage you to take a closer look at some of their benefits and get a feel for what serves you best. When choosing a stream processing solution, consider scalability to meet changing needs as well as the speed of the tool to reduce latency and ensure excellent processing capabilities.