In today’s rapidly evolving digital landscape, stream processing has become a crucial component for businesses to harness real-time data analysis and decision-making. With a multitude of frameworks available, it’s essential to understand the strengths, weaknesses, and use cases of each to select the optimal solution for your specific needs. In this comprehensive guide, we will explore the most popular stream processing frameworks, including options from the Apache Foundation and major cloud providers, while offering valuable insights to aid in your selection process.

What Is Stream Processing?

Stream processing refers to the continuous collection, integration, and analysis of real-time data as it flows from source to destination. Unlike traditional batch processing, stream processing handles data on-the-fly, enabling immediate insights and actions. This approach is critical in scenarios where delays can result in negative consequences.

Stream processing frameworks read data from input sources, process it in real-time, and output the results, often using multiple threads to ensure parallelism. These frameworks are indispensable for applications that require instantaneous data analysis, such as:

• Loan risk analysis
• Anti-fraud detection
• Sensor data monitoring
• Target marketing

Understanding how stream processing compares with batch processing can further clarify its unique advantages. For a detailed comparison, you might find our article on Batch vs. Stream Processing insightful.

Stream Processing Engines – How They Work

A typical stream processing framework comprises four main components:

1. Input Sources

Data is ingested from various input sources such as Apache Kafka, Apache Flume, social media platforms, and IoT sensors.

2. Output Streams

Processed data is written to output streams or stores, including HDFS (Hadoop Distributed File System), Cassandra, and HBase.

3. Processing Logic

This component defines how the data is processed. This is typically carried out using Java, Scala, or Python code. Here, transformations like filtering, aggregating, joining, and cleaning data take place.

4. State Management

State management keeps track of the processor's progress and maintains state information to ensure exactly-once processing, even in the event of failure or data replay.

Architecture and Execution

A stream processing engine organizes incoming data into small batches, presenting them as continuous streams to simplify integration and analysis. The core of these frameworks is designed to handle various tasks required for managing data processing efficiently and securely.

Apache Foundation’s Stream Processing Tools

Apache Kafka Streams

Kafka Streams is a client library for building robust, real-time streaming applications and microservices. It combines simplicity with the powerful capabilities of Apache Kafka.

Advantages:

• Easy integration with other applications
• Low-latency processing
• Eliminates the need for traditional message brokers

Disadvantages:

• Missing point-to-point queuing
• Limited streaming data analytics features
• Challenges with Kafka Cluster queues

Use Cases: Zalando, Pinterest, Uber, TransferWise

For a deep dive into Kafka architecture, visit our Kafka Architecture Guide.

Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing, well-suited for both batch and stream processing. The Spark Streaming module extends Spark’s capabilities for stream processing.

Advantages:

• Fault-tolerance
• Advanced streaming data analytics
• Support for multiple languages
• High performance
• Batch processing capabilities

Disadvantages:

• Steep learning curve
• High memory usage
• Lack of built-in caching algorithms

Use Cases: Uber, Shopify, Slack

For more details on Apache Spark, read our comprehensive article on What is Apache Spark? Architecture, Use Cases, and Benefits.

Apache Flink

Apache Flink is a powerful and versatile stream processing framework that supports low-latency and event-time processing.

Advantages:

• High throughput with low latency
• Easy-to-understand UI
• Dynamic task analysis and optimization

Disadvantages:

• Complex integration with YARN
• Limited language support

Use Cases: Gympass, Lime

Explore more about Apache Flink in our Flink Overview.

Apache Samza

Developed alongside Apache Kafka, Samza is a stream processing framework that provides fault tolerance and exactly-once processing semantics.

Advantages:

• Fault tolerance
• Exactly-once processing
• Stateful processing
• Pluggable architecture for integration with various systems

Disadvantages:

• Challenging without Kafka and YARN
• Lacks advanced streaming features

Use Cases: LinkedIn

Apache Hive

Built on top of Hadoop, Apache Hive can handle both batch and streaming data processing. Hive Streaming allows real-time data processing with SQL-like queries.

Advantages:

• Familiar SQL interface
• Scalable and efficient
• Supports batch, streaming, or interactive queries

Disadvantages:

• Poor performance for interactive queries
• Complex data updates
• High latency

Use Cases: Facebook, Netflix, FINRA

Apache Storm

Apache Storm is a distributed real-time computation system designed for processing high-velocity data streams.

Advantages:

• Simple API
• Capability to process millions of records per second
• Flexible and extensible

Disadvantages:

• No guaranteed message processing
• Lack of built-in windowing and state management

Use Cases: Twitter

Apache Apex

Apache Apex offers a native YARN stream processing framework known for handling high-velocity data streams effectively.

Advantages:

• Highly scalable and performant
• Flexible architecture
• Support for multiple data sources and sinks

Disadvantages:

• Complex debugging and customization
• Lacks built-in caching

Apache Flume

Apache Flume specializes in collecting, aggregating, and moving huge quantities of log data in flexible, fault-tolerant streaming pipelines.

Advantages:

• Distributed, reliable, and available architecture
• Fault-tolerant and extensible
• Simple integration for online data applications

Disadvantages:

• Poor ordering guarantees
• Potential data duplications
• Complex topology

Use Cases: Blue Cross Blue Shield Association

Fully-Managed Services for Stream Processing from Cloud Providers

Google Cloud Dataflow

Google Cloud Dataflow is a serverless stream processing service that enables users to develop and execute data processing pipelines with ease.

Advantages:

• Infinite capacity
• Reduces operational complexities
• Low latency
• Native integrations with BigQuery and AI Platform

Disadvantages:

• Limited to Cloud Datastore
• Costly in streaming mode
• Incompatible with custom sources

Use Cases: Spotify, NY Times

Amazon Kinesis Data Streams

Amazon Kinesis provides a fully managed service for real-time data streaming, ingestion, and analysis from multiple sources.

Advantages:

• Simple setup and maintenance
• Handles any streaming data volume
• Integration with Amazon’s big data toolset

Disadvantages:

• Commercial cloud service costs
• Complex documentation
• Lacks direct streaming support

Use Cases: Deliveroo, Lyft

Azure Stream Analytics

Azure Stream Analytics is a cloud-based stream processing service tailored for high-volume data analysis from various input and output devices.

Advantages:

• Low-cost, highly available
• Integration with Azure IoT Hub
• Supports filtering, aggregating, and joining streaming data

Disadvantages:

• No support for on-premises data sources
• Limited query language support

Use Cases: Renault-Nissan-Mitsubishi Alliance, Volkswagen Group

How to Choose a Stream Processor for Your Application?

With numerous stream processors available, choosing the right one requires careful consideration of your business needs and specific use cases. Here are some critical features to look for:

• Support for data ingestion from a message broker
• Streaming SQL for quicker development
• Stream processing APIs and a robust query environment for productivity
• High reliability and availability for minimal interruption

Combining Multiple Tools

Often, a combination of tools may be necessary to achieve the desired functionality. For instance, integrating Kafka with frameworks like Hadoop, Spark, or Flink can address various aspects of stream processing for big data architectures.

Leveraging managed cloud services can simplify integration and scalability. These services are often fully integrated into their ecosystems, making them an attractive option for many organizations.

Experiment and Consult Experts

If you’re uncertain about where to start, consider experimenting with a few stream processing frameworks in a test environment to gauge their effectiveness for your needs. Additionally, consulting with data scientists and engineers can provide tailored solutions and streamline the implementation process.

For more insights into stream data architecture, explore our article on Stream Data Architecture.