Real-Time Data

What is Real-Time Data?

Real-time data is information that is made available for use as soon as it is generated. In real-time systems, there is little to no latency between data collection and data processing. This type of data typically has a very short shelf life because its value is tied to being acted upon immediately.

Key Takeaways

Any information that is processed and used as soon as it’s generated, regardless of its format, can be considered real-time data.
Real-time data is often produced in large volumes at high speeds.
Delays in processing or responding to real-time data can render the data outdated or ineffective.
A well-designed architecture helps ensure that real-time data can be used to generate timely, reliable insights.
The choice of whether to process real-time data locally or in the cloud depends on the specific use case and network constraints.

Table of Contents Table of Contents

What is Real-Time Data?
Key Takeaways
How Real-Time Data Works
Real-Time Data Architecture
Types of Real-Time Data
Real-Time vs. Batch
Real-Time Data Analytics
What We Can Learn From Real-Time Data
Real-Time Data Uses
Real-Time Data Benefits and Challenges
The Bottom Line
FAQs
References

Table of Contents

What is Real-Time Data?
Key Takeaways
How Real-Time Data Works

Show Full Guide

Real-Time Data Architecture
Types of Real-Time Data
Real-Time vs. Batch
Real-Time Data Analytics
What We Can Learn From Real-Time Data
Real-Time Data Uses
Real-Time Data Benefits and Challenges
The Bottom Line
FAQs
References

How Real-Time Data Works

Real-time data works by capturing information the moment it is generated and making it available for immediate use.

Depending on an application’s purpose, real-time data can be processed locally or it can be sent to a remote data center in the cloud for processing.

Local processing has lower latency and better resilience because it doesn’t require Internet connectivity. Cloud processing can be more cost-effective and is better suited for applications that rely on information sharing or require complex deep learning computations.

How Real-Time Data is Collected

Real-time data can be collected from data logging and data acquisition systems as well as application programming interfaces (APIs) that retrieve data from sensors.

It should be noted, however, that a number of potential bottlenecks can affect the data collection process. For example, insufficient bandwidth can restrict the amount of data that can be transmitted in real time, and inadequate processing capacity can delay stream processing for data collected through APIs.

Real-Time Data Architecture

Real-time data requires a data architecture that supports data ingestion, data extraction, stream processing and data analytics in real time.

To be effective, the architecture should be scalable and be able to handle a large volume of data that’s generated at high speeds.

Types of Real-Time Data

Real-time data can be broadly categorized into two categories depending on how the data is generated.

Streaming data is continuously generated and updated.
Event data is only generated when a specific event or trigger occurs.

Real-Time vs. Batch

Real-time data loses its value quickly, so it needs to be processed with minimal delay to enable timely responses.

In contrast, batch data is collected and processed later at a scheduled interval or when a sufficient amount of data has been collected.

Feature	Real-time data	Batch data
Processing time	Immediate	Delayed
Latency	Low	High
Frequency	Continuous	Periodic
Data volume	Usually smaller	Usually larger
Complexity	Can be complex to manage	Generally simpler to implement
Cost	Can be higher due to infrastructure requirements	Can be more cost-effective

Real-Time Data Analytics

Real-time data analytics platforms are designed to minimize the delay between data generation, insight generation, and action.

Special analytics tools like Google Dataflow and in-memory databases like Redis are required to reduce latency and handle the fast ingestion, processing, and analysis of data as it is generated.

What We Can Learn From Real-Time Data

Real-time data allows changing situations to be understood as they unfold. Ultimately, it empowers people and computer programs to make decisions based on the most current information available.

Real-Time Data Uses

Here are some examples of how real-time data is used:

Autonomous vehicles

Real-time sensor data helps cars detect obstacles and avoid collisions.

Financial trading

Real-time data allows brokers and high-frequency trading platforms to make informed decisions about whether to buy or sell assets.

Fraud detection

Banks and payment systems use real-time transaction data to identify and block potentially fraudulent activities.

Healthcare monitoring

Real-time data from wearable medical devices can alert the patient and/or healthcare professionals about conditions that need immediate attention.

Supply chain logistics

Real-time data about inventory levels allows companies to manage deliverables and shipping schedules.

Smart homes

Internet of Things (IoT) devices in smart homes can use real-time data to dynamically control lighting, heating, and security systems.

Energy management

Real-time data from smart grids and advanced metering infrastructures (AMIs) meters help power companies optimize energy distribution.

Social media monitoring

Social networking sites analyze user interactions in real-time to curate personalized content feeds.

Customer support

Real-time data from customer interactions can alert support teams to emerging issues or outages.

Manufacturing and industrial automation

Machine learning algorithms can use real-time data from machines and sensors to enable predictive maintenance.

Retail personalization

Retailers can use real-time browsing data to offer hyper-personalized discounts and recommendations.

Telecommunications

Telecom companies use real-time data to monitor network traffic and buffer jitter in VoIP phone calls.

Real-Time Data Benefits and Challenges

Real-time data can be used to make fast, informed decisions based on the most current information, but its effective use requires careful planning, the right infrastructure, and a commitment to data quality and data security.

Benefits

Autonomous systems can use real-time data to make decisions and take actions
Real-time data can be used to improve the efficiency of operations and enhance customer experience management (CEM)
Industry sectors like DeFi and cybersecurity can use real-time data to manage risk

Challenges

Processing real-time data at scale can require a significant investment in IT infrastructure and compute resources
Handling large volumes of fast-moving data can lead to information overload and bad decisions if not managed properly
Because the goal is to reduce latency as much as possible, it can be difficult to ensure that real-time data is always accurate, clean, and reliable

The Bottom Line

In the past, real-time data definitions often assumed there would be a slight delay in data use due to limitations in processing power, data transfer speeds, and infrastructure. With advancements in cloud computing, network technology, and artificial intelligence (AI), however, true real-time processing is now achievable and is often expected.