With the rise in reliance on data to make decisions in many businesses, many are looking for ways to have fresh data for quick, actionable insights. One commonly used technique is stream processing.
In this blog post, we'll explain more about what stream processing is, its key components, popular frameworks, and tools, as well as the challenges and advantages it offers over traditional batch processing.
Stream Processing Explained
Stream processing is a data processing method that handles continuous data streams from an array of sources, such as transactions, stock feeds, website analytics, connected devices, and weather reports, to provide real-time analysis.
Through real-time stream processing, several applications can be used, including:
Businesses across numerous industries are taking advantage of stream processing to gain valuable insights from real-time data. Some examples include:
- Financial institutions monitoring transactions for fraud detection
- Healthcare providers tracking patient data
- Transportation companies analyzing live traffic data
Stream processing enables the transformation of how organizations leverage stream data and output data for decision-making using stream processing tools.
It is commonly utilized in big data technology to query continuous data streams and detect conditions quickly within a short period. Stream processing hardware plays a crucial role in ensuring the efficient processing of these data streams.
Key Components of Stream Processing
Various essential components ensure the effective functioning of stream processing architectures. A good reference for modern architecture can be found in this AWS Whitepaper.
Stream Processing Engine
The stream processing engine is the core component responsible for receiving, aggregating, and processing data streams in real time. It can handle low-latency data ingestion while supporting fault tolerance and data processing at scale.
There are three main types of stream processing engines:
- Open Source Compositional Engines (Apache Storm)
- Managed Declarative Engines (Apache Spark Streaming)
- Fully Managed Self-Service Engines
Storage in stream processing is used to store the processed data, as well as the metadata associated with it. It can be a local file system, a distributed file system like HDFS or Amazon S3, or a cloud-based service like Google Cloud Storage.
A messaging system enables communication between various components of the stream processing architecture. It helps in handling the incoming data streams and delivering them to the processing engine.
A dashboard or visualization tool is a critical component that displays the processed data in an interactive manner for users to monitor and analyze real-time data.
Some of these tools include:
- Power BI
- Apache Superset
Popular Stream Processing Frameworks & Tools
To visualize and share real-time data from stream processing, you'll require stream processing frameworks and tools.
Among these, Apache Kafka and Apache Flink are the most popular and widely used frameworks.
Both frameworks offer distinct features and capabilities, enabling organizations to choose the one that best suits their needs and use cases.
In the following sections, we'll overview frameworks and discuss some other good alternatives.
Apache Kafka, a favored open-source stream processing platform, offers a unified, high-throughput, low-latency platform for managing real-time data feeds.
Here are some of it's key features
- High-volume data processing
- Fault tolerance
- Offers many integrations
Kafka is designed to effectively handle real-time data feeds, managing high-volume, high-velocity, and high-variety data streams. It allows for continuous data ingestion and supports real-time analytics, enabling organizations to make timely and informed decisions.
Apache Flink, another popular open-source, distributed stream processing framework, presents robust capabilities for stateful computations over data streams.
Flink provides support for:
- Stream and batch processing (execution mode)
- Advanced state management
- Event-time processing semantics
- Exactly-once consistency for stream processing
Flink is a distinct data processing framework that facilitates stateful computations over data streams, handling both unbounded and bounded data streams. This makes it an ideal tool for handling big data and streaming applications.
In terms of performance, Apache Flink is deemed to be faster than Apache Spark for stream processing.
Other Common Frameworks and Tools
Besides Apache Kafka and Apache Flink, several other renowned stream processing frameworks and tools exist, including:
Each of these frameworks provides distinct features and capabilities for stream processing. They also belong to different architecture ecosystems as well, like Amazon, Google, and Microsoft.
For better data integration, go with your current tech stack to experience better integrations with your enterprise ecosystem solutions.
Challenges in Stream Processing
Despite its power and velocity, stream processing brings its own set of challenges. Some of the key challenges include:
- Ensuring scalability
- Maintaining fault tolerance
- Maintaining cost-effective use of data
- Ensuring data consistency
- Managing event ordering
Tackling these challenges is vital to fully utilize the stream processing technology and the high cost it demands.
Scalability and Fault Tolerance
Scalability is a critical consideration in stream processing, as it enables the system to process an increasing volume of data streams without a decline in performance. Stream processing systems must be able to scale up and down rapidly and effectively to accommodate the application's requirements.
Fault tolerance is another crucial aspect of stream processing, as it ensures the uninterrupted operation and recovery from any failures in the system.
By implementing fault tolerance mechanisms, stream processing systems can recover from any failures and proceed with processing data without interruption, guaranteeing the reliability and availability of the system.
Some of the measures to handle crash failures include:
- Active replicas
- Passive replicas
- Failure recovery
Stream processing can be expensive, as it requires high-performance computing resources to process large volumes of data in real time. Companies need to carefully evaluate the features and capabilities they require and choose a system that offers the best balance between cost and performance.
This also includes being selective in choosing which data points require real-time feeds.
Data Consistency and Ordering
Data consistency and event ordering are integral to stream processing, as they guarantee that the data is processed in the right sequence and that the outcomes are consistent. Stream processing systems must be able to guarantee that the data is processed in the right order and that the results are consistent across all nodes in the system.
Achieving data consistency and ordering can be challenging in distributed systems, but it is crucial for maintaining the integrity of data streams and ensuring that events are processed in the correct order.
Stream Processing vs. Batch Processing
Even though stream processing provides numerous benefits for managing real-time data, batch processing is also important, as each approach is apt for different scenarios and applications.
Here are their differences:
Stream processing is ideal for applications that require continuous data ingestion and real-time analytics, while batch processing is more suitable for regularly scheduled data processing tasks and scenarios where real-time processing is not required.
Advantages of Stream Processing
Stream processing holds multiple advantages over batch processing. One of the most significant benefits is its ability to process data in real-time as it enters the system, eliminating any wait time between collecting and processing the data and allowing for immediate analysis and decision-making.
Other advantages of stream processing include:
- Minimal latency
- Built-in features for handling imperfect data
- SQL queries on data streams
- Predictable and repeatable results
- Automatic scaling of applications across multiple processors and nodes
These features make stream processing a powerful tool for organizations looking to gain valuable real-time insights from their data.
When to Choose Batch Processing
Batch processing is apt for:
- Regular data processing tasks
- Handling large datasets
- Non-timely historical data analysis
- ETL workflows
- Processing simplicity
When deciding between batch processing and stream processing, organizations should consider the volume of data, the necessity of real-time processing, and the complexity of the task.
Batch processing is suitable for processing large volumes of data at specific time intervals, allowing for easier management and optimization, while stream processing is ideal for processing real-time data continuously.
In conclusion, stream processing is a powerful technology that enables organizations to process and analyze data in real time, offering numerous advantages over traditional batch processing methods.
As the demand for real-time insights continues to grow, stream processing will undoubtedly play an increasingly significant role in the future of data-driven decision-making.
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