Stream Processing Platforms

It’s no secret that companies around the world are generating more data quicker than ever before. As a result, the demand for real-time data processing and analytics is increasing by the day. And it’s easy to see why when you consider that real-time data analysis allows companies to make better data-driven decisions, understand their customers better, and make their business processes more efficient.

To get all these benefits, though, companies need the right tools. And herein lies the problem. There are so many stream processing platforms available that it can be challenging to find the right one. Compounding this challenge is the sheer amount of information available on these platforms, so researching them takes a lot of time and effort.

Fortunately, we’re here to help. So, with this post, we’ll take a closer look at some of these platforms in more detail.

What Is Stream Processing

Before looking at these stream processing platforms, it’s necessary that we quickly recap what stream processing actually is. Simply put, stream processing is a data management technique that involves the real-time or near real-time processing of data. Unlike batch processing that was used traditionally on data at rest, stream processing involves processing data when it’s in motion.

This enables companies to query and analyze continuous data streams, get faster insights from their data, and react quicker based on this data. Because of this, an increasing number of companies from stock trading platforms to eCommerce websites and rideshare apps are implementing stream processing to get these benefits.

Popular Stream Processing Platforms

Now that we’ve recapped what streaming processing is, let’s look at some of the popular options available on the market.

Apache Spark

Apache Spark is an open-source analytics engine that’s specifically designed for big data analytics. The platform can perform analytics, ETL (extract, transform, load), machine learning, and graph processing on data at rest and in motion. Based on this, it’s easy to see why it’s one of the leading real-time stream processing platforms.

The platform is known for its speed and ease of use. In fact, it’s up to 100 times faster than Hadoop and can process vast amounts of complex data effortlessly. Spark can run independently in standalone cluster mode on top of cluster nodes like Hadoop YARN, EC2, Mesos, Kubernetes, and others.

Pros:

  • Spark is a mature product with a large community.
  • It’s fault-tolerant.
  • It’s very fast.
  • Supports multiple languages and advanced analytics.

Cons:

  • Can be complex to set up and implement and has a steep learning curve.
  • Memory intensive.
  • It has latency of a few seconds.

Apache Flink

Apache Flink is an open-source stream processing platform that’s able to process live streams within milliseconds because it only processes new, changed data in real-time. As a result, it’s extremely fast at complex stream processing. With Flink, streaming data can be ingested, processed, and distributed across various nodes and it’s fully capable of executing both batch and stream processing hassle-free. In addition, it can also manage machine learning, event and graph processing, and it comes with various built-in connectors for third-party applications and databases.

Pros:

  • It offers low latency and high throughput.
  • Simple and intuitive UI.
  • Dynamic and automated task optimization.
  • Good documentation.

Cons:

  • Only supports Scala and Java.
  • Limited support.
  • Integrating with Hadoop YARN can be challenging.
  • Scaling limitations.

Apache Kafka Streams

Apache Kafka Streams is a stream processing Java API that allows developers to filter, join, aggregate, and group data without writing any code. And because it’s a Java library, developers are also able to integrate it with any services they are currently using to create scalable, fault-tolerant applications. Also, writing and deploying standard Java and Scala applications on the client-side is very accessible, so Apache Kafka Streams has a low barrier to entry. In other words, it has a level of operational simplicity Not many other platforms have and it could be the easiest service to manage.

Pros:

  • Integration with other applications.
  • Low latency
  • Reduces the requirement for multiple integrations.

Cons:

  • Somewhat lacking in respect of analytics.
  • Lacks vital messaging paradigms.
  • Slows down as the number of queues increase.

Apache Storm

Apache Storm is a real-time computation system that’s simple to use for processing unbounded streams of big data. Here, it’s able to process vast amounts of data in near real-time with lower latency than many other stream processing platforms. One of its biggest benefits, though, is that Storm was designed to work with any programming language which, in turn, gives users a lot of flexibility. In addition, it’s capable to perform real-time analytics, machine learning, ETL, and continuous computation. It’s also fault-tolerant, scalable, and can integrate with many existing systems.

Pros:

  • One of the best solutions for true real-time processing.
  • Suits any programming language.
  • Flexibility.

Cons:

  • Can be complex to implement.
  • Does not guarantee the ordering of messages, so reliability may be compromised.

Amazon Kineses

Kineses is Amazon’s platform for processing streaming data in the cloud in real-time. As such, it’s able to collect, process, and analyze streaming data and it was specifically designed to allow companies to get the valuable information they need to make quicker decisions. The platform is scalable and can handle vast amounts of streaming data with low latency from various sources which include event streams, social media feeds, application logs, video, audio, and other applications.

Pros:

  • Easy to set up, implement, and maintain.
  • Can handle any amount of streaming data.
  • Integrates with other Amazon services.

Cons:

  • It’s a commercial service that’s paid for by the hour.
  • Documentation isn’t as clear.
  • No support for direct streaming.
  • The library to consume data is resource-intensive.

Which is Best?

So, now to answer the question, which platform is best. Well, the answer isn’t as clear-cut. Ultimately, it depends on your organization’s specific needs and requirements. So, you should consider the available options and how they cater to these needs and requirements.

Hopefully, this post helped illustrate some of the options available. Keep in mind, though, these are just some of the options available and there are many more, from established players to newcomers, each bringing something unique and improvements to the table.

With that in mind, let’s introduce the Scramjet platform. We believe the time has come for companies of all sizes to reap the benefits of stream processing without having to deal with the inherent complexity.

So, we provide seamless data transport and scalability mechanisms to cloud and on-premises, real-time and offline processing pipelines, allowing you to focus on your business logic. This gives you the efficiency and scalability of streaming, while also being cost-effective and developer-friendly.

Project co-financed by the European Union from the European Regional Development Fund under the Knowledge Education Development Program. The project is carried out as a part of the competition of the National for Research and Development: Szybka Ścieżka.

Author:

Natalia Czapracka

Natalia Czapracka

Marketing expert with 13 years professional experience.

JOIN OUR SCRAMJET CLOUD PLATFORM BETA PROGRAM

Get limited possibility to test our next generation 3-in-1 data processing platform! Be the first on our platform helping us shape the roadmap and having an influence on the product development.

JOIN BETA