What Is Data Streaming and How Does It Work?

Data streaming is becoming the driving force behind modern apps and businesses. It enables a continuous flow of data from multiple sources. These include sensors, social media feeds, and market data. The data is processed and analyzed in real time. This leads to immediate insights and faster decisions.

Data streaming is used in many areas. These include streaming apps, stock trading platforms, and home security systems. In all these cases, data is generated and processed on the fly. Unlike batch processing, streaming allows on-demand analysis. The data is often stored in data lakes or warehouses. This makes it easier for professionals across functions to use it later.

What Are the Different Types of Data Streaming?

Data streaming can be classified into different types based on two key factors:

• Event Streams: These are continuous data flows created in real time. These streams originate from various sources, such as IoT sensors, financial systems etc.

• Log Streams: Generated from log files of IoT devices, internal systems, or applications. These logs may include system errors, security records, or customer activity. Log streams often need extra processing to uncover insights. These insights include system performance, errors, or operational health.

• Sensor Streams: These carry data from sensors that track location, temperature, or inventory. Businesses use this real-time data for operational efficiency and monitoring.

• Social Media Streams: This data comes from social platforms. All the likes, shares, brand mentions on any post comes under this data. It helps businesses monitor user preferences, brand perception, and trending topics.

• Click Streams: Produced by user interactions on websites or apps. Click streams track visitor behavior, preferences, and engagement patterns.

Why Is Data Streaming Important?

Data streaming reflects how data naturally flows in the real world. Most modern apps and businesses rely on continuous data flow. They use it for tasks like fraud detection, media streaming, and more. All these processes function as ongoing events.

Streaming helps validate, structure, and visualize data with near-zero latency. It eliminates the need for constant storage by accessing data only when required. This approach offers greater flexibility in scaling and resource usage. Organizations use it to get real-time snapshots of operations, market trends and more. For example, e-commerce platforms can monitor user activity as it happens. They can also recommend products instantly.

What Are the Key Characteristics of Data Streaming?

Streaming data comes from many real-time sources—like apps, sensors and system logs. It has distinct characteristics that set it apart from traditional data.

• Time Sensitive: It is generated continuously and is only relevant for a short period. For example, a security system must analyze suspicious movement immediately.
• Heterogenous: Streaming data comes from different systems and regions. It exists in various formats like AVRO, JSON, or CSV. It can also include numbers, dates, strings, or binary values.
• Continuous: Data streams are generated in real-time and are continuous by nature. They may or may not be acted upon instantly.

What Are the Benefits of Data Streaming?

Data streaming brings with it an inherent improvement over traditional batch processing. According to Gartner’s survey in 2022, nearly 80% of businesses have seen revenues jump after implementing real-time analytics. Its implementation has been growing at a CAGR of nearly 26% YOY. There are many more quantifiable benefits for data streaming such as:

• High ROI: Gartner’s report also states that data streaming and its implementation in real-time analytics have helped organizations squeeze nearly $2.3 trillion in revenue. It has allowed organizations to identify process inefficiencies, improve customer satisfaction and reduce non-workforce costs such as machine failures. They can react and respond quickly to time-sensitive data.
• Reduced Infrastructure Costs: Unlike traditional batch processing, there is no need to store data in large warehouses for data streaming. This significantly reduces the cost associated with storage systems and hardware.
• Reduced Preventable Systemic Losses: As data streams work on live data provided by sensors and live feeds, the likelihood of identifying critical system failures is significantly higher. Organizations are better informed about security breaches, market fluctuations, negative customer sentiments and manufacturing defects as they happen, allowing them to act immediately and prevent losses.
• Improved Customer Satisfaction: Real-time data processing provides the organization with a competitive advantage in an open ecosystem where customers may decide in a split second whether to choose a competitor due to systemic failures or negative customer experiences.

How Does the Data Streaming Process Work?

The process can be broken up into 3 key steps. They are:

Data Ingestion

Where data is collected from various sources such as sensors, social media feeds, etc., through a stream processing software, data ingestion uses “Stream Producers”, which are software components in applications and IoT devices that collect data from the source. These stream producers transmit the records to the stream processor with parameters such as stream name, data value and sequence number. The processor groups the data temporarily using the stream name and sequence number to chronologically arrange it.

Data Processing

Next, the stream processing software applies transformations, aggregations or analysis to the ingested data as it occurs. Stream processors transform and structure the output of the stream producer to be analyzed further using analytical tools. The results of these transformations are usually alerts, actions, dynamic visualizations on dashboards or a new data stream that can be consumed by other consumers.

Data Analytics & Visualization

Processed data is presented with real-time insights through dashboards or reports on an application. These tools analyze the data ready for consumption.

Data Storage

An optional stage in some streaming systems is to store the processed data for later analysis. These essentially act as a hybrid of stream and batch processing systems. In most cases, these are flexible storage spaces such as data lakes. These may require relevant data partitioning, processing and backfilling with historical data for comparative or historical analysis.

When stream processing occurs, all the above steps work seamlessly and almost instantaneously, producing an output that could be a BI dashboard, an alert or a data stream that can be processed and consumed further.

What Challenges Does Data Streaming Present?

The real-time and complex nature of data streaming brings with it the following challenges;

• Data Diversity and Complexity: Real-time data streams are often plagued by data loss and damaged packets of data. Sources like IoT sensors, social media feeds, navigation data of GPS, etc. are heterogenous. As a result, data streaming also must handle disparate and diverse datasets. The system should be able to tag and present the same according to the consumer needs.
• Time Sensitive: Data streams depend on the timeliness of data as they often work on critical systems that have time-bound relevance and importance. The systems should be fast enough to analyze and visualize data while it is relevant.
• Elasticity of Data: Data stream processing systems must have a high level of quality even when the data load increases dynamically. The systems must adapt accordingly and increase or reduce capacity based on the volume of data they receive to ensure optimal usage of resources.
• Imperfect Nature of Data: The disparate nature of data sources and the mismatch in transmission mechanisms sometimes result in missed or damaged data elements. This may occur as the information elements appear in jumbled order.
• Lack of Replicability of Data: Due to the real-time nature of data sources, the data streams can’t often be repeated if data transmission is lost. Even though provisions exist for the re-transmission of data, the new data may not be a replica of the previous data stream.
• Data Security: Big data processors such as those involved in stream processing applications will have a large list of producers and consumers that communicate with a data cluster continuously. Without proper access control, any client can be configured to read and write any data topic. In such cases, it is important to properly authenticate any client attempting to write or read a particular topic.

What Are Some Real-World Use Cases of Data Streaming?

Some typical use cases involve event data that is generated by some action and requires immediate decision. The following are two such examples in the real world.

Real-Time Fraud Detection

Stream processing-powered anomaly detection has helped one of the world’s largest credit card providers reduce its fraud write-downs by $800Mn per year. As soon as a user swipes a credit card, stream processing enables systems to run algorithms that recognize and block fraudulent charges and send triggers for these anomalous charges to the bank and consumer without the consumer having to wait for approvals.

Real-Time Personalization and Marketing

Nearly 89% of marketers for top e-commerce companies use stream processing to deliver personalized messages. E-commerce companies create contextual user experiences as the user is on their website or app based on the user’s browsing activity, previous purchases, and other in-app behavior. Nearly 14% of marketers have created an ROI of nearly $15 for every dollar spent through these personalized campaigns.

What Should Businesses Consider When Choosing a Data Streaming Platform?

While choosing a data streaming platform, it is important to remember the most important KPIs. These are event rate, throughput (also called event rate times event size), latency, reliability and the number of topics (in the case of pub-sub architecture).

Another major factor is the scalability of the platform by adding nodes. Doing this in a clustered geometry can also improve the reliability of the platform.

One must also look at the number of programming languages supported by the platform as developers and client consultants will be the ones writing the applications for these platforms. For example, a good data streaming platform may support Java, Scala, C/C++, Go, .Net and Python to help the developers write applications. Some platforms support Java, JavaScript, PHP, Ruby, Swift or Node.js, WebSocket and C#. The choice of data streaming platform may be driven by the languages supported by it.

Another factor for consideration is the number of connectors it supports. Some platforms support as many as 120 connectors for all data sources, readily available and tested.

The ideal data streaming platform should be located where there is the least latency, ideally near the data sources and other components. If not, it is also good to choose platforms that provide a geographically distributed cluster that supports low latency for far-flung data sources and sinks.
Finally, it is also important to look at the overall manageability of the platform. Some platforms are notoriously hard to configure and maintain without dedicated expertise in running them. Commercially supported cloud servers may be easier to manage.

The choice of data streaming platform often is a balance of the above-mentioned factors. The right data streaming platform for one depends on one’s processing and analytical needs.

Why Does Data Streaming Matter for Businesses?

For consumer-centric businesses that deal with large variables in metrics and a multitude of data, data streaming isn’t just a technological upgrade, it is a strategic necessity. Data streaming enables real-time analytics, which can lead to more informed decision-making, operational efficiencies and enhanced customer experiences. For example,

Uber

Uber uses data streaming to power its rider-driver matchmaking through apps installed by the rider and driver to track the location details. When a rider requests a ride, Uber uses GPS data to match a driver who is nearby and leverages Google Maps traffic API to provide the ETA for the driver. On the driver’s end, Uber provides real-time traffic data and updates ride requests using the available requests sent out by nearby riders. The app also provides price information based on the demand and traffic conditions along with the distance to the destination.

Instagram

Nearly 95 million photos and videos are uploaded on Instagram every day and the system provides real-time view metrics, like counts, comments, and reactions to the content created. The live component of Instagram also requires critical instant data transfer for uninterrupted live calls and sessions.

How Does Stream Processing Compare to Batch Processing?

Batch processing is the traditional method that derives its terminology from traditional computing where computers periodically complete high-volume repetitive jobs in batches. In batch processing, data is aggregated from different sources and stored in a data warehouse to be used later by a business intelligence tool for analysis and visualization. It wasn’t ideal for real-time analytical purposes in machine learning or modern streaming and social media platforms. The need to process data immediately on an ongoing basis resulted in the genesis of stream processing. In this method, data isn’t normally stored but is accessed for processing from sources as and when required. Both have their merits and demerits, which include:

Batch Processing	Stream Processing
Processes high volume of data in batches within a specific period	Processes continuous stream of data in real-time
Data size is known and is finite in batch size	Data size can be unknown and of indefinite quantity
Data is processed in multiple passes and takes longer	Data is processed in relatively fewer passes and takes milliseconds
The input graph is static	The input graph is dynamic
Data is analyzed based on a snapshot	Data is analyzed continuously in real-time
The response is provided after job completion	The response is provided instantly
Used in payroll, billing, food processing systems, etc.	Used in payroll, billing, food processing systems, etc. Used in stock exchange, e-commerce transactions, social media, etc.

Many businesses have implemented a combination of stream processing and batch processing where data may optionally be stored and retrieved in batches for processing and analysis.

What Does the Future Hold for Data Streaming?

Data streaming is already becoming a ubiquitous method to get real-time analytics in most modern businesses and applications. Going forward, the technologies will become more accessible and user-friendly so that businesses of all sizes can leverage their power without needing technical expertise.

The increasing popularity of data streaming will also require platforms to be built on robust security measures and adhere to strict data piracy regulations. The future looks very promising with new technologies, ingenious applications and increased adoption by more industries.