Stream Processing

What is stream processing?

Data that is in motion is referred to as real-time data or stream data. Stream processing is a technique to process continuous data (unbounded data) streams where data flows in real time from one point to another, like from a sensor to a database. It helps in filtering and analyzing the data in small chunks (streams) rather than storing the large batch of data and then processing it. Stream processing response is very quick and varies from a few milliseconds to minutes. Actions that stream processing applies to data include aggregation, analytics, filtering, transformation, data enhancement, and ingestion.

Note: In general, streaming data, unbounded data, and real-time data are used interchangeably.

Where is stream processing used?

Stream processing is used across different industries and is mostly used where the data is generated through a certain event or needs continuous monitoring. Some of the popular use cases of stream processing are listed below:

1. Anomaly and fraud detection: Stream processing is used to monitor risk management and detect anomalies in real time. For example, in the finance sector, stream processing is used by major credit card companies for running fraud detection operations.

2. Analytics for the Internet of Things (IoT): Stream processing is used in IoT applications or devices like cameras and sensors to process data in real time and gain better insights and value to make faster decisions.

3. Manufacturing and transportation: Stream processing helps to monitor machine data and any downtime in real time. It also helps in monitoring vehicle performance, road traffic, and weather conditions, which helps to improve safety and optimize routes.

4. Healthcare: Stream processing helps monitor patient data in real time and can also be used with any medical device to keep track of data.

5. Marketing and advertising: Stream processing helps analyze social media posts and feeds from which we can get reports on customer interactions.

These are just a few use cases for stream processing, and there are a lot more across various industries.

Batch vs. stream processing

For a long time, the traditional method of processing data was batch processing. With all the technological innovations, there is a new era of data where data generation is increased by 10 to 100 times, which bumps the requirement of processing the data in real time.

While stream processing is quite quick, batch processing is a very time-consuming process that needs to store a large volume of bounded data (data that has a definite start and end point) before processing it all together. But, in certain cases, the data is continuously ingested as a never-ending stream (unbounded data that has a definite start but no end). In this case, you can’t wait to collect all the data for processing at once, as you might not know the end of it. Stream processing is the solution to this problem because you can process the streaming (continuous) data. Stream processing is used across different industries and applications where the main factor of use is real-time data analysis. This helps in better business decision-making, early detection of issues, cost reduction, and rapid application scaling.

Let’s look at the major points of difference between both techniques.

• Batch processing processes a large volume of data in batches based on a schedule such as weekly, monthly, etc. or on a certain predefined threshold. Stream processing, on the other hand, helps to analyze real-time data in small chunks as it comes.

• In batch processing, the data is stored in a database or a data warehouse. But in stream processing, the live data is continuously ingested.

• Batch processing is used for generating reports and statistical analysis, while stream processing is mainly used for detecting issues and monitoring real-time changes like fraud transactions, stock prices, etc.

How is publish/subscribe different from stream processing?

Publish or subscribe is a messaging pattern used to exchange messages between two parties. The publish-subscribe model also works in real time. In this model, the publisher publishes information about some topic, and one or more subscribers can subscribe to that information. It allows flexibility and greater scalability to the system. So, the publish-subscribe model is focused on message distribution and decoupling of components in distributed systems.

On the other hand, stream processing focuses on the real-time processing of continuous data in small chunks as it is generated or received. It involves applying multiple operations on the data for monitoring any issues and making better business decisions.

Stream processing in big data

Big data is the collection of large amounts of data, typically high volume and high velocity, which can be structured, unstructured, and semi-structured. Stream processing is a fundamental component of big data architecture and allows organizations to quickly derive insights and take immediate actions based on the data being generated in real time. Stream processing systems typically ingest high-velocity data streams, apply real-time processing algorithms and filters, and produce output that is used for decision-making, analytics, and other downstream processing.

Multiple organizations work on big data tools to process the streaming data. These tools involve Apache Spark, Apache Kafka, Apache Flink, or Apache Flume.

Stream processing architecture

To incorporate stream processing capabilities into applications, programmers either code the entire process or use an event stream processor. The architecture of stream processing is divided into several layers. Each layer plays a vital role in making the flow of data easy.

Layer 1: Data sources

The first layer is the data source, which means where the data comes from. It can be any medium like sensors, log data, social media posts, or streams generated from third-party applications.

Layer 2: Ingestion

After the data source is selected, ingesting data is important, which is responsible for storing the coming streams. It includes a message broker through Apache Kafka and a storage system of big data such as Hadoop HDFS, or for the cloud, it can be AWS S3.

Layer 3: Processing

The third layer is responsible for the actual processing of data streams. Multiple operations, including aggregation, filtration, and transformation, are applied on streams. Big data frameworks such as Spark, Storm, and Flink are mostly used to build this layer.

Layer 4: Analytics

This is an interesting layer where the processed data streams are analyzed in regard to business norms and decisions. It includes data visualization and dashboarding through Tableau or Power BI.

Layer 5: Storage

This layer is responsible for storing the analyzed data responses. It can include different types of storage like relational, hierarchical, NoSQL data, etc.

Layer 6: Action

This is the final layer responsible for taking action based on the insights obtained. It includes different components like an alert mechanism, automated workflows, and integration with other systems or applications like CRM software (for example, Salesforce or HubSpot), an enterprise resource platform (ERP) (such as SAP or Oracle ERP), and even a DBMS like PostgreSQL, MySQL, or MongoDB.

Best practices for stream processing

These are some of the best practices you can follow to build a better stream processing pipeline in your organization.

• Choose the right stream processing framework: Several frameworks are available around us, and you have to pick one that best suits your use case and is compliant with factors such as scalability and fault tolerance.

• Use data compression: Reducing the amount of data helps in faster processing and optimizing the cost. Techniques that are used for compression are gzip, snappy, or LZ4.

• Use data partitioning: Partitioning data across nodes based on keys like timestamp can help improve scalability and stream performance.

• Monitor and measure the performance: Regular monitoring and measuring the performance of the stream pipeline helps to identify areas for improvements, detect issues, and optimize metrics such as latency, throughput, and resources.

• Consider security and compliance: Stream processing frameworks can handle sensitive data. Each pipeline has a different requirement and structure. So, it is important to consider some policies and security measures to control data leakage or data breach problems. Techniques used are encryption, access control, and auditing to protect data to ensure compliance.

You now know that stream processing is the best technique to process a high volume of streaming data that has a high velocity and makes it useful in different industries like finance, healthcare, retail, and manufacturing. Also, stream processing enables organizations to uncover patterns from data in real time. Tools like Apache Flink, Apache Spark, and Apache Kafka are some of the leading tools in this space.