What Is Streaming Data: Use Cases, Examples, & Architecture

Streaming data (or, Data streaming) is the foundation of most technologies we use daily. From making real-time financial transactions to streaming online videos, we depend on data streaming to provide smooth and quick access to information. However, the utility of data streaming extends beyond personal convenience. It empowers organizations across various industries by facilitating real-time monitoring, customer behavior analysis, and more.  

In this article, you will learn all about streaming data, its architecture, use cases, and the popular data streaming platforms available. You can leverage these tools to derive actionable insights, make informed business decisions, and streamline your organization’s workflows.

What Is Streaming Data?

Streaming data is a continuous flow of data generated by many sources within your organization, including IoT sensors, log files, and servers, at high volume and speed. This data is processed as it arrives, making it ideal for applications like fraud detection or traffic monitoring that require up-to-the-second insights and responses.     

Characteristics of Streaming Data

Some of the key characteristics of data streaming, besides high volume and high velocity, are:

Continuous Flow

A data stream arrives constantly in real-time for as long as required. It has no clear beginning or end and can be infinite in length. You cannot predict the amount of data you will receive. 

Time-sensitive

Each piece of data within a stream usually carries a timestamp, which is crucial for analyzing the data in the order in which it occurs.

Heterogenous

Streaming data from various sources can have diverse formats, such as JSON, CSV, and JPEG, with multiple data types, including strings, numbers, and dates. Your processing systems should be able to handle such variations. 

Volatile and Unrepeatable

Storing the entire stream for later analysis is often challenging or impractical. Unlike batch data, you can't easily rewind and analyze a specific point again. Once processed, you usually discard or overwrite the streaming data, necessitating real-time analysis or capturing only essential summaries.

Key Difference between Batch vs. Stream Data Processing

Batch processing involves collecting and processing high-volume data in fixed-size chunks, typically at scheduled intervals before analysis. It is cost-effective for data processing as there is no pressure for efficiency. However, data storage can be expensive. You can use batch processing for historical analysis and large-scale computations.

On the other hand, stream processing involves processing micro batches or individual records over a sliding window or in real time. This results in low latency before analysis. Stream processing can be expensive as it requires faster processing capabilities, but it is cost-effective for data storage. You can use it for time-sensitive applications requiring instant responses to incoming data.

‍Want to learn more? Check out this topic: Processing Paradigms: Stream vs. Batch in the ML Era. 

The Data Streaming Architecture

Data streaming architecture is the framework for handling the continuous influx of data. It consists of five major components working together to ingest, process, store, and analyze the data streaming.

• Data Source: Your data streams originate from diverse sources, such as IoT devices, web applications, or social media platforms. These sources are usually semi-structured and unstructured in format. 
• ‍Stream Ingestion: This layer facilitates capturing raw data and preparing it for further processing or transferring it to a storage system using traditional ELT or ETL processes. These processes involve filtering out irrelevant data, converting it into a standard format, and ensuring data accuracy and consistency.
• ‍Streaming Data Storage: It is the repository for seamless storage and retrieval of ingested data, providing durability and accessibility for downstream processing and analysis. The data storage is either temporary or permanent based on your needs.   
• ‍Stream Processing Engine: This is the core component of the data streaming architecture. The engine continuously analyzes and processes the incoming data stream in real-time or near real-time. It performs various operations based on your needs, such as filtering, aggregation, transformation, or data enrichment.
• ‍Data Sink: This is the final destination of your processed data streams. Depending on your use case, the data sink could be a data warehouse or lake for data analysis or a visualization tool for real-time dashboards.

Suggested Read: What is Data Architecture

This five-component architecture provides a general framework to help you understand how your streaming data gets handled from source to destination. However, there are two main types of data streaming architectures that you should consider when building a system for your organization—lambda and kappa architectures. 

Lambda architecture processes data in batch and real-time mode, allowing for historical data analysis and real-time insights. On the other hand, Kappa architecture focuses solely on real-time processing, aiming to simplify the data pipeline. 

You can choose one over the other based on your specific needs and the value you place on real-time responsiveness compared to historical data.   

Use Cases of Data Streaming 

The most common streaming data examples are online media streaming, multi-player video gaming, and stock trading. Let’s explore other data streaming use cases that can help increase your organization’s operational efficacy.

Real-time Data Analytics and Monitoring

Data streaming enables you to monitor your operations in real time instead of waiting to collect all datasets. You can use it to streamline inventory management, track customer traffic, and analyze sales patterns and upcoming trends. This allows you to optimize your business strategies and make timely decisions. 

Predictive Maintenance

As a manufacturing enterprise, you can utilize data streaming to predict the maintenance of machinery and equipment. By collecting and analyzing the data streaming from the sensors embedded in your machines, you can monitor their health and predict potential failures before they occur. This approach minimizes downtime, reduces maintenance costs, and enhances overall efficiency.      

Customer Behavior Analysis

You can use data streaming from your e-commerce applications and social media platforms to understand users' behavior, preferences, and general sentiment in the market. You can leverage the insights you gain to personalize recommendations, target advertising more effectively, and provide a more engaging user experience. 

An easy example is movie recommendations by streaming services based on your search history. 

Data Streaming Technologies and Platforms

Data streaming technologies and platforms form the data streaming architecture, which we discussed previously. Here's a breakdown of some key tools:

Apache Flume

Apache Flume is a distributed and reliable service for efficiently collecting, aggregating, and moving large amounts of log data to a centralized repository. 

Logstash

This tool allows you to process and transform your log data from various sources before sending it to a data store. It also offers a flexible pipeline for log parsing, transformation, and enrichment.

Apache Kafka

A distributed messaging system with a high-throughput, scalable platform for real-time data feeds. It combines queueing and publish-subscribe messaging models and provides the best of both.  

Amazon Kinesis

A managed service offered by AWS for handling and storing high volumes of real-time data streams, including application logs, videos, and clickstreams for later analysis or machine learning (ML) applications. 

Cloud Object Stores

Services like Amazon S3 or Google Cloud Storage offer cost-effective storage for extensive streaming data archives.

Apache Spark Streaming

This platform is a real-time data processing engine built on top of Apache Spark. It provides micro-batch processing capabilities, allowing you to analyze streaming data in near real-time.

Apache Flink

A stream processing engine with low latency capabilities. It is well-suited for complex event processing (CEP) and real-time analytics on data streams.

Azure Stream Analytics

This is a service offered by Microsoft. It allows you to analyze real-time data streams using queries and integrate them with other Azure services.

Using a combination of these tools, you can build robust data streaming pipelines for real-time data ingestion, storage, and analysis. However, it is important to consider all options. The following section will provide an alternate way to process your data. 

A Cost-Effective Alternative to Stream Processing Using Airbyte

Batch processing is a well-established approach for data pipelines that don’t require immediate results. It involves scheduled extraction, loading, and processing of large volumes of data with efficiency. A popular tool that leverages this method is Airbyte.  

Airbyte simplifies data movement between various sources and destinations by offering a batch-based data ingestion method. It provides a user-friendly interface and a library of over 350 pre-built connectors to ensure seamless data retrieval and transfer to your preferred destination. Airbyte also offers the flexibility to create custom connectors tailored to your requirements using the Connector Development Kit (CDK). 

Key Features of Airbyte

• As an ELT (Extract, Load, Transfer) data integration solution, Aibyte provides a unified view of your data by consolidating it from disparate sources into the target storage system. 
• Airbyte’s CDC functionality lets you capture data modifications within the source system using incremental sync replication, significantly minimizing latency. This eliminates the need to copy the entire source dataset.
• You can leverage its schema change management feature to automatically propagate and reflect all the source schema modifications at the destination, ensuring data consistency.
• Airbyte allows you to integrate with dbt, a robust data transformation tool that helps perform complex transformations. 

As an open-source tool, Airbyte is cost-effective and accessible, so you can easily get started without any commitments. You can refer to the official documentation and explore its features further. 

Closing Thoughts

Data streaming has changed how businesses collect, analyze, and react to information. You can leverage real-time insights from continuous data streams to optimize your business strategies and make faster data-driven decisions.

This article explains streaming data, its characteristics, architecture, and use cases. It also introduces various data streaming technologies and platforms that help you ease into the process of working with real-time data. By investing in robust data streaming infrastructure, you can grab new opportunities for growth and innovation. 

FAQs

How can you use data engineering techniques to handle and process large volumes of streaming data in real-time?

To handle high-volume real-time streaming data, you can use several techniques, such as data partitioning, data compression, and in-memory computing. 

How do you handle data quality in streaming pipelines?

Schema validation checks at the ingestion step can help you identify structural inconsistencies. You can also use data-cleansing techniques to address missing values, typos, and outliers. Continuous monitoring allows you to make quick interventions and take appropriate security measures.

What are the types of data streams?

You can categorize data streams into several types based on their characteristics and usage. Bounded, unbounded, static, and dynamic streams are a few names on the list.