Organizations must constantly monitor and analyze real-time data through data streaming systems. Data streaming technology helps organizations in processing and analyze real-time data with ease. It is primarily used in situations where dynamic data is generated regularly. Today, organizations generate a massive amount of real-time data from different sources like IoT devices, social media, mobile applications, and more. Information from such digital solutions becomes the source for deploying data architecture to automate data processing.
Businesses can use different data architectures (batch or streaming) based on the requirements. While batch architecture is for processing large amounts of data, streaming architecture is for real-time processing. In this article, let’s understand what is a data streaming architecture.
What is Data Streaming Architecture?
Data streaming architecture is a framework of layers built to ingest and process large amounts of streaming data from different sources. It helps businesses process, manipulate and analyze real-time data quickly. Today, most organizations use digital solutions like customer relationship management (CRM), digital marketing, and human resource management systems (HRMS) to run their business operations. These digital solutions are usually the source of real-time event data streams. As a result, there is considerable demand for a streaming data infrastructure to enable complex, powerful, real-time analytics in businesses.
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Why Use a Data Streaming Architecture?
Companies use the two most common ways to process and analyze data: batch processing and stream processing. A batch processing system allows companies to process and analyze large volumes of data in batches. As a result, businesses have to wait until the entire batch of data is processed before it is ready for analysis. Batch processing is mostly useful for large quantities of information that are not time sensitive.
Consequently, businesses started using data streaming architecture to avoid waiting. In data streaming architecture, data is consumed as soon as it is generated. This enables businesses to make better data-driven decisions in real-time.
Data streaming architecture has numerous benefits:
- Identify data patterns: Today, it has become essential to identify patterns in data in real-time to understand the data effectively and make quick decisions. For example, if you are looking for a trend in website traffic, it requires continuous data processing and analysis. In data streaming architecture, businesses can easily identify patterns in data as it is processed and analyzed in real-time.
- Automation: Data streaming architecture helps automate data processing. With a reliable architecture, you can collect data from different sources and moves data per the business requirements. Generally, setting up data steaming architecture is a one-time effort to handle real-time data. Although it automates the entire process, you must perform maintenance activities to avoid flaws and breakdowns.
- Eliminate Data Silos: Often, companies collect data to perform batch processing. However, it leads to data silos, a repository of isolated data. Since data piles up, companies need help harnessing data’s power. Data streaming architecture allows companies to consume data in real-time and eliminates data silos. The ability to process data and make real-time decisions will allow organizations to gain a better return on investment.
Key Components of the Streaming Data Architecture
Data streaming platform architecture consists of the following software components designed to handle large streams of raw data from different sources:
Message Brokers
Businesses use message brokers to enable communication between applications, services, and systems. Some popular message broker platforms are Apache Kafka, RabbitMQ, IBM MQ, RedHat AMQ, and more.
Message brokers are also known as message queues. In data streaming architecture, message brokers communicate event data using messaging protocols. Messaging protocols are rules for applications to interact with each other. It describes the communication in which the messages are processed, prioritized, and routed between producers and consumers. Message brokers take the event data, convert into a message, and stream it continuously. Other components in the data streaming architecture can listen to the message from the message broker and consume it. Message brokers follow a publish/subscribe model in the communication of services.
Companies use ETL tools to gather data from different sources, transform it into a specified format, and then analyze it using various BI tools. ETL tools allow businesses to fetch event data from data stream architecture and then apply queries to analyze it. The result of the ETL processes is in the form of an action, alert, or a new data stream.
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Streaming Data Storage
After transformation, you can store structured and semi-structured data in a data warehouse. And unstructured data can be stored in a data lake. Cloud service providers like AWS, Azure, and Google Cloud platform provide data lakes and warehouses to store different data types for analysis. You can use the storage system based on the type of data you collect.
Data Analytics/Serverless Query Engine
After the streaming data is stored, it is analyzed using different tools to gain meaningful insights. There are several tools for streaming data analytics—Amazon Athena, Cassandra, and Elasticsearch.
Data Streaming Architecture Patterns
Data streaming patterns help businesses to build reliable, secure, and scalable applications in the cloud. Some of them are mentioned below:
Idempotent Producer
The idempotent producer pattern in data streaming architecture is most commonly used to deal with duplicated events in an input data stream. Each producer is assigned a Producer Id (PID), sent every time it sends messages to a broker.
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Claim Check
You must implement a claim checker to ensure your steaming data system handles large data sizes. Messages from data streams can contain large images, audio, and text files. It is not ideal for sending such large messages to the message bus directly, as it requires more resources and bandwidth. Therefore, to avoid this issue, you need to store the entire message payload in an external service like a database. You can just send the reference to the message bus, which acts as a claim check to retrieve information.
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Event Splitter
In data streaming architecture, the event splitter pattern is used to split one event into multiple events for streaming analytics. It is used when storing the event data into subtopics to process each event differently. This gives you better granularity in your analysis.
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Event Grouper
As the name suggests, the event grouper pattern is used to group similar events in the data streaming architecture. This is used for an event that occurs numerous times after a few intervals. As a result, the event grouper combines the event by counting the number of occurrences of similar events over a specific time.
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Event Aggregator
The event aggregator combines related events to produce a new event. The event aggregator combines multiple events to calculate the new event’s average median or percentile. As event grouper and event aggregator are often combined in a stream processing architecture, the event grouper pattern acts as an input to the event aggregator pattern.
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Final Thoughts
Data streaming platform architecture has extensive capabilities of handling data in real-time. Due to its relatively high performance, easy deployment, fault tolerance, and reliability, the steaming architecture is used for critical business operations. It is suitable for multiple use cases like fraud detection, real-time stock trades, business analytics, sales, and more. However, data streaming architecture requires additional efforts to keep it running due to the constantly varying requirements. This can increase the data streaming architecture’s complexity and lead to failure.
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Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.