Real-Time Data & Analytics - The Complete Guide

Real-time data (RTD) refers to information that is processed, consumed, and/or acted upon immediately after it's generated. While data processing is not new, real-time data is a newer paradigm that changes how businesses run.

Batch vs Real-Time Data Processing

In previous years, batch data processing was the norm. Systems had to collect, process, and store large volumes of data as separate functions before data could be utilized for further action. In situations where real-time data or analytics are not needed, batch processing is still a viable process.

In contrast, real-time data processing (or streaming data) can collect, store, and analyze continuously, making data readily available to the end-user as soon as it's generated with no delay.

While databases and offline data analysis remain valid tools, the need for real-time data has increased exponentially with the advent of modern applications. After all, the world isn’t a batch process - it runs in real-time.

So why is real-time data important? The most important asset businesses can have is accurate, timely data. From consumer behavior, inventory tracking, and social media feeds, to risk mitigation, the ability to leverage real-time insights, performance, and trends within seconds or minutes vs days or months are what make businesses successful and competitive. Here are five benefits of real-time data.

1. Customer Satisfaction: Real-time data improves customer experience by enabling services to become more flexible, dynamic and interactive. Today, customers expect personalized experiences on their mobile devices. Advertising and recommendations must be tailored to customer preferences in the moment. Rules engines can combine customer data with channels and content to enable interactive experiences. Chatbots can talk to customers and offer products appropriate to their needs. In the future, Augmented Reality and the Internet of Things will create opportunities for businesses to interact with customers in new ways.

2. Business Intelligence: Real-time data can help managers to visualize key performance indicators on dashboards and intervene in areas where they can be most effective. Business intelligence can enable banks to customize their risk models and make quicker decisions about loans. Customer Relationship Management systems can join machine learning capabilities with customer data to build decision-making engines and content management systems that improve the profitability of each customer.

3. Business Development: Real-time data enables businesses to understand their markets and respond quickly with new business models, products and services. Google was able to transform itself into an advertising giant because of its data. Uber is able to match customers to drivers, thanks to GPS streams. Social networks and dating apps have capitalized on the human need for social bonding. Sales organizations use data to predict customer behavior and identify cross-selling opportunities.

4. Operational Intelligence: Real-time data can empower companies to optimize their supply chain and operational processes. For example, supermarkets can manage their inventories in real-time, saving millions of dollars for consumers. Investment banks can buy and sell financial instruments using artificial intelligence. Manufacturers can improve their production schedules and reduce costs.

5. Real-Time Analytics: Real-time data can be combined with data analytics and machine learning to unlock new business use cases. We have already mentioned decision-making engines and automated trading systems. Another example would be predictive maintenance in a power station. By monitoring the vibrational modes of turbines in real time, the operator can predict mechanical failures before they happen. This can prevent costly outages and improve safety.

A customer requests a ride from Uber. A thief uses a stolen credit card. A patient’s blood pressure drops. A server fails in a data center. All of these are considered real-time data (also knows as events.

We ingest real-time data into an event log, which captures a sequence of events as they happen. It is natural to imagine the data as a stream of events flowing in time. A data stream is an abstraction built on this analogy. The purpose of streaming is the ability to take on events in-flight, without waiting for that information to be stored first. This is the largest differentiation between real-time streaming and batch processing, and allows for real-time data analytics at scale.

The basic principles of real-time data processing are simple. We distinguish between producers and consumers of data. Producers are the sources of data. Consumers are the services that use the data. In modern streaming systems, producers send messages to a message broker. The broker assigns each message to a topic and publishes it. A topic is simply a queue of related messages. Consumers can then subscribe to different topics that interest them. This is often called the publish and subscribe model (also referred to as pub-sub). It works almost like a Twitter feed.

As usual, the devil is in the detail. We want our system to be scalable and fault-tolerant. We want to have high throughput and low latency. We want an immutable record of our data, but we also want flexibility in how we use the data in our applications. We want the right architecture and the right performance guarantees. This is where Apache Kafka's stream processing technology excels.

• GPS Data: GPS-enabled devices, including mobile phones, produce streams of geographical data. Using real-time location data, businesses can track delivery fleets. Air traffic controllers can land planes safely. Commuters can use live traffic data to choose the fastest route. Social networks can use GPS data streams to build a more accurate model of our social relationships. Real-time data streams allow cars to ingest, store, and integrate live GPS data with self-driving software to form the backbone of autonomous cars, delivery drones, and the internet of things (IoT).
• Ride Share Applications: Uber relies on real-time data to match customers to drivers. Real-time data is also collected to forecast demand, compute performance metrics, and extract patterns of human behavior from event streams. Not only would real-time data streams allow for seamless customer experience, they'd also provide real-time fraud detection, anomaly detection, marketing campaigns, visualization, and customer feedback. The company uses Apache Kafka to achieve real-time data at this scale, processing over 30 billion messages per day.
• Streaming Platforms: Netflix embraces event streams to achieve speed and scalability in all aspects of its business. Streaming is the communication mechanism for the entire Netflix ecosystem. The company uses Apache Kafka to support a variety of microservices, ranging from studio financing to real-time data on the service levels within its infrastructure.
• Walmart: Walmart operates thousands of stores and hundreds of distribution centers across the world. The company also makes millions of online transactions. Walmart uses Apache Kafka to drive its real-time inventory management system. The system ingests 500 million events per day and ensures that the company has an accurate view of its entire inventory in real-time. The system also supports Walmart's telemetry, alerting, and auditing requirements.
• Medical data: Real-time data on heart rate, blood pressure and oxygen saturation enables hospitals to identify patients whose health is at risk of deteriorating. In the case of Covid-19, when hospitals were short on equipment, personnel, and at patient capacity, real-time data analytics this would enable hospitals to optimize the use of Intensive Care Units, ventilators, and patient health data in real-time, increasing efficiency and streamlining processes.
• Another example is heart attacks. Approximately 10% of patients suffer heart attacks while they are already in a hospital. By using real-time data analytics, heart attacks could be predicted before they happen. Electronic monitoring and predictive analytics are vital in many clinical areas where patient safety is at stake.