Top 5 Stream Processing Platforms to consider in 2023

Platforms for stream processing have grown in importance as companies try to handle and analyse real-time data in the era of big data.

Organizations can process, analyse, and act on data streams in real-time using these platforms, which enables them to take wise decisions swiftly and effectively.

Businesses should assess the various stream processing systems on the market as we approach 2023 and select the one that best suits their requirements. The demand for stream processing platforms is expected to increase over the future years as Internet of Things (IoT) devices and other real-time data sources become more prevalent.

In this article, we will explore some of the top stream processing platforms to consider in 2023 and highlight their key features, pros, and cons to help you make an informed decision for your business.

1. Apache Kafka

Apache Kafka is an open-source stream processing platform that was originally developed by LinkedIn and later donated to the Apache Software Foundation. Kafka is designed to handle high-throughput data streams in real-time, making it an ideal choice for applications such as messaging systems, log aggregation, and real-time analytics.

Apache Kafka is a powerful stream processing platform that provides high throughput, scalability, fault tolerance, and durability. However, it can be complex to set up and monitor, resource-intensive, and has a steep learning curve. Despite these challenges, Kafka is a versatile platform that can be used for a variety of real-time data processing applications.

Here are some of the pros and cons of using Apache Kafka:

Pros:

1. High throughput: Apache Kafka is designed to handle high-throughput data streams, making it an ideal choice for applications that require real-time processing of large amounts of data.
2. Scalability: Kafka is designed to be highly scalable, allowing you to add or remove brokers as needed to handle changes in data volume.
3. Fault-tolerance: Kafka provides a high level of fault tolerance, with data being replicated across multiple brokers to ensure that it is not lost in the event of a failure.
4. Durability: Kafka is designed to store data for a long period of time, making it an ideal choice for applications such as log aggregation.
5. Versatility: Kafka can be used for a variety of applications, including messaging systems, log aggregation, and real-time analytics.

Cons:

1. Complexity: Kafka can be complex to set up and configure, especially for users who are not familiar with distributed systems.
2. Monitoring: Kafka requires monitoring to ensure that it is running smoothly, which can be time-consuming and require specialized skills.
3. Resource-intensive: Kafka can be resource-intensive, requiring a significant amount of CPU and memory to handle high volumes of data.
4. Limited data transformation: Kafka does not provide built-in support for data transformation, which may require additional tools or custom code.
5. Learning curve: Kafka has a steep learning curve, especially for users who are new to distributed systems or real-time data processing.

2. Apache Flink

Apache Flink is another open-source stream processing platform that is designed to handle both batch and stream processing workloads. Flink provides a high level of performance, fault tolerance, and flexibility, making it an ideal choice for many applications.

Apache Flink is a powerful stream processing platform that provides high performance, fault tolerance, flexibility, and advanced streaming features. However, it can be complex to set up and monitor, resource-intensive, and has a steep learning curve. Despite these challenges, Flink is a versatile platform that can be used for a variety of real-time data processing applications.

Here are some of the pros and cons of using Apache Flink:

Pros:

1. High performance: Apache Flink is designed to deliver high performance, with low-latency processing and support for both batch and streaming workloads.
2. Fault-tolerance: Flink provides a high level of fault tolerance, with data being replicated across multiple nodes to ensure that it is not lost in the event of a failure.
3. Flexibility: Flink is designed to be flexible and can be used for a variety of applications, including real-time analytics, event processing, and machine learning.
4. Scalability: Flink is designed to be highly scalable, allowing you to add or remove nodes as needed to handle changes in data volume.
5. Advanced streaming features: Flink provides advanced streaming features, such as event-time processing, windowing, and stateful stream processing.

Cons:

1. Complexity: Flink can be complex to set up and configure, especially for users who are not familiar with distributed systems.
2. Resource-intensive: Flink can be resource-intensive, requiring a significant amount of CPU and memory to handle high volumes of data.
3. Limited data sources: Flink may not support all data sources, which can require additional tools or custom code to integrate with existing data sources.
4. Limited community: Flink has a smaller community than other stream processing platforms, which can make it harder to find support or community-driven plugins.
5. Learning curve: Flink has a steep learning curve, especially for users who are new to distributed systems or real-time data processing.

3. Apache Storm

Apache Storm is an open-source stream processing platform that is designed to handle real-time processing of large amounts of data. Storm is widely used for applications such as machine learning, fraud detection, and real-time analytics.

Here are some of the pros and cons of using Apache Storm:

Pros:

1. High throughput: Apache Storm is designed to handle high-throughput data streams, making it an ideal choice for applications that require real-time processing of large amounts of data.
2. Fault-tolerance: Storm provides a high level of fault tolerance, with data being replicated across multiple nodes to ensure that it is not lost in the event of a failure.
3. Scalability: Storm is designed to be highly scalable, allowing you to add or remove nodes as needed to handle changes in data volume.
4. Versatility: Storm can be used for a variety of applications, including machine learning, fraud detection, and real-time analytics.
5. Extensibility: Storm provides a plug-in architecture that allows you to add new features and functionality as needed.

Cons:

1. Complexity: Storm can be complex to set up and configure, especially for users who are not familiar with distributed systems.
2. Limited data sources: Storm may not support all data sources, which can require additional tools or custom code to integrate with existing data sources.
3. Learning curve: Storm has a steep learning curve, especially for users who are new to distributed systems or real-time data processing.
4. Limited community: Storm has a smaller community than other stream processing platforms, which can make it harder to find support or community-driven plugins.
5. No built-in support for batch processing: Storm does not provide built-in support for batch processing, which may require additional tools or custom code.

Apache Storm is a powerful stream processing platform that provides high throughput, fault tolerance, scalability, and extensibility. However, it can be complex to set up and monitor, and has a steep learning curve. Despite these challenges, Storm is a versatile platform that can be used for a variety of real-time data processing applications.

4. Amazon Kinesis

Amazon Kinesis is a fully-managed, cloud-based stream processing platform that is designed to handle large amounts of real-time data. Kinesis is widely used for applications such as log processing, real-time analytics, and IoT data processing.

Here are some of the pros and cons of using Amazon Kinesis:

Pros:

1. Fully-managed: Amazon Kinesis is a fully-managed platform, which means that you do not have to worry about managing infrastructure, patching, or scaling.
2. High scalability: Kinesis is designed to be highly scalable, allowing you to handle changes in data volume and traffic patterns.
3. Integration with other AWS services: Kinesis integrates seamlessly with other AWS services, such as Lambda, S3, and DynamoDB, making it easy to build end-to-end data processing pipelines.
4. Versatility: Kinesis can be used for a variety of applications, including log processing, real-time analytics, and IoT data processing.
5. Easy to use: Kinesis provides an intuitive user interface and simple APIs, which makes it easy to get started with.

Cons:

1. Cost: Kinesis can be expensive, especially for high-traffic applications, which may make it less attractive for smaller businesses.
2. Vendor lock-in: Kinesis is an AWS service, which means that you are tied to the AWS ecosystem.
3. Limited customization: Kinesis provides limited customization options, which may make it less suitable for applications that require more control over data processing.
4. Limited features: Kinesis provides limited features compared to other stream processing platforms, which may make it less suitable for advanced applications.
5. Learning curve: Kinesis can have a steep learning curve, especially for users who are new to AWS services or real-time data processing.

Amazon Kinesis is a fully-managed, cloud-based stream processing platform that provides high scalability, integration with other AWS services, versatility, and ease of use. However, it can be expensive and provide limited customization options and features compared to other stream processing platforms.

Despite these challenges, Kinesis is an excellent choice for businesses that require a fully-managed, cloud-based stream processing platform with a wide range of use cases.

5. Google Cloud Dataflow

A completely managed service for constructing data pipelines is offered by the cloud-based stream processing platform known as Google Cloud Dataflow. Built on Apache Beam, an open-source unified programming model for batch and streaming data processing, it is intended to manage both batch and streaming data processing.

Here are some of the pros and cons of using Google Cloud Dataflow:

Pros:

1. Fully managed: Google Cloud Dataflow is a fully managed service, so you don’t need to think about patching, scaling, or handling infrastructure.
2. Flexibility: Dataflow is a flexible platform for a variety of use cases because it enables both batch and streaming data processing.
3. Easy to use: Dataflow provides an intuitive user interface and simple APIs, which makes it easy to get started with.
4. Integration with other Google Cloud services: Building end-to-end data processing processes is made simple by the seamless integration of Dataflow with other Google Cloud services like BigQuery, Pub/Sub, and Datastore.
5. Auto-scaling: Dataflow has the ability to automatically scale, allowing it to adapt to shifts in data volume and traffic patterns.

Cons:

1. Limited community: Compared to other stream processing platforms, Dataflow has a smaller community, which can make it more difficult to find help or community-driven plugins.
2. Cost: Dataflow may not be as appealing to smaller companies due to its high cost, particularly for high-traffic applications.
3. Learning curve: Users who are unfamiliar with distributed systems or real-time data handling may find dataflow to have a steep learning curve.
4. Limited features: Dataflow has fewer features than other platforms for stream processing, which may make it less appropriate for complex apps.
5. Vendor lock-in: Since Dataflow is a Google Cloud service, your access to the Google Cloud environment is restricted.

Both batch and streaming data processing are supported by the flexible and fully-managed stream processing technology known as Google Cloud Dataflow. It offers auto-scaling, simplicity of use, and integration with other Google Cloud services.

However, it might be pricey, offer few functions, and have a challenging learning curve. Despite these difficulties, Dataflow is a great option for companies needing a completely managed cloud-based stream processing platform with a variety of use cases.

Conclusion

Stream processing platforms play a critical role in enabling real-time data processing for businesses. There are several options available in the market, each with its own set of features, pros, and cons. While each platform has its strengths and weaknesses, it’s important to evaluate your business needs, budget, and technical expertise to choose the right platform for your use case.

Reference:

Stream Processing: How it Works, Use Cases & Popular Frameworks