Deciphering Real-Time Data Streaming: Unleashing the Power of Instant Insights
What is real-time data streaming, and why should we care?
In today’s digital data-filled era, the prominence of real-time data streaming has surged, allowing organizations to process and analyze data as it’s generated and make informed decisions in real-time. In this blog post, we’ll understand the real-time applications used in our daily lives, why it’s important, and how we break down the market.
Let’s start with a popular example. Picture yourself ordering food from a delivery app. The seemingly simple act is built on real-time data analysis. To set the delivery prices of the food, apps need to analyze real-time data to understand the demand, driver availability, and traffic conditions. Next, to dispatch and assign the correct driver to your delivery, the app needs to understand the existing orders for an individual driver on route, find who’s closest to the restaurant, and combine these factors to find a driver who can deliver in the most efficient time and cost. Finally, as the driver is in the process of delivering the order, real-time location updates enable you to track your delivery’s progress seamlessly.
In addition to online ordering, there are a number of additional real-time data streaming use cases across industries and end customers, including:
- Financial transactions: Real-time applications in financial transactions range from determining whether transactions are fraudulent to algorithmic trading enabled at hedge funds
- Healthcare: Real-time monitoring of patients enables healthcare providers to quickly respond to changes in a patient’s conditions with the support of cutting-edge technology
- Inventory and availability: Real-time data helps businesses respond quickly to changes in demand, optimize their supply chain, and ensure the availability of products.
- EV charging: real-time data enables EV charging stations to allocate power supply based on demand, ensuring efficient charging
- Real-time server monitoring: Monitoring a website’s performance deployment and server logs to improve efficiency and optimization
- Large data volumes: Processing data in real-time allows companies with large data volumes to process and only save the data that is relevant, minimizing storage costs
- Manufacturing equipment monitoring: Real-time processing enables companies to monitor their equipment and production performance in real-time to optimize their manufacturing process
- Advertising: Businesses can monitor user engagement and success of their ads in real-time and re-target those that haven’t completed a transaction
- User recommendations: Real-time personalized recommendations based on the individual’s current interest and search history enrich user experience (e.g., real-time changes to YouTube’s recommendation algorithm)
- IoT home sensors: Real-time data safeguards homes by swiftly addressing potential issues and enhancing the safety and security of a home
- Transportation and meal delivery: Communicating delivery locations and dispatching efficient drivers in real-time streamlines the consumer’s experience
Does everything need to be in real-time?
Not all scenarios need real-time streaming. Batch processing is a process where data is collected and stored over a period of time and processed in a certain time batch, such as hourly or daily intervals, creating a latency between when the data is received and finally sent to the data consumer. Examples, where batch processing occurs include processing orders from customers, billing, and payroll, whereas stream processing can include fraud detection and recommendation systems.
The real question is: do you need the data in 0 (seconds) or 0 (minutes)? Many use cases do not need data immediately as real-time streaming is not mission-critical to their efficacy. Modern companies will be powered by a combination of batch (historical data) and real-time stream processing depending on the use case and whether the most up-to-date data is mission-critical.
The core principles of real-time data are:
- Minimal Latency: Data is processed immediately as it comes in
- Freshness: Continuous flow of fresh and relevant data
- Concurrency: Multiple streams of data along with the processing and querying of data concurrently
The end-to-end flow of real-time data streaming can be broken down into three key steps:
- Event creation: Data flows from event sources such as user interactions or data captured from IoT devices [Event Producers]
- Event curation: Ingestion, processing, and transformation of real-time data
- Event serving: Making real-time data available to downstream applications – from developing on top of real-time data, customer real-time data platforms, product analytics, and real-time machine learning [Event Consumers]
Surrounding all these layers are managed service tools to support the management of data workflow and integrations into other tools.
Real-time data stack
We break down real-time data tools into the following sub-categories:
- Data sources: Not necessarily exclusively real-time, but these sources capture the data (data producers) that is sent to data consumers
- Data integration: Tools to manage and transform data into data pipelines and stream processing systems
- Event streaming platforms: Used for streaming and processing data in real-time, designed to handle large volumes of data and employ built-in fault mechanisms
- Stream processing frameworks: Typically used in conjunction with event streaming platforms
- Real-time analytics: Downstream application for users to query and analyze real-time data immediately after it is available
- Real-time developer tools: Enable building of applications on top of real-time tools and allow the access of real-time data in a code-first way
- ML Ops: Enable the training of machine learning models using real-time data
With the continued proliferation of data, we expect the continued implementation of real-time streaming in B2B and B2C processes. While batch processing is sufficient for many use cases, real-time streaming will be used in conjunction with existing data methods in cases when having the most up-to-date and accurate data is mission-critical to a successful outcome. We anticipate the growth of new real-time-specific tools and the product expansion of existing incumbent solutions as the embrace of real-time streaming ushers in an era of unparalleled insights and operational excellence.