The data landscape has expanded immensely, making it crucial for organizations to leverage data effectively for better decision making.
Data is now the substrate — and differentiator — for all modern apps, including and especially gen AI apps. Databases play a vital role in storing, managing and retrieving data in any application lifecycle. Obtaining insights from the data is okay but the real value lies in obtaining real-time insights to drive superior decisions. Advances in AI and machine learning have amplified the importance of real-time data and subsequently, data platforms.
These platforms not only support immediate analytics but also foster innovation by allowing companies to dynamically experiment with data-driven approaches. This article delves into understanding real-time data platforms, exploring their significance and the pivotal role they play in building modern, intelligent applications.
Understanding real-time data
Real-time data is invaluable, serving as the cornerstone for companies making timely and informed decisions. It refers to information delivered immediately after collection, without any delay. In today's fast-paced environment, stale information is unacceptable, and applications are often judged by their responsiveness to user queries and events.
For instance, consider a baseball game in San Francisco. As a fan in Bangalore, you want real-time updates, not delayed information. Similarly, an eCommerce company needs real-time data to track what products their customers view and purchase to effectively manage inventory.
Real-time data processing is crucial in such scenarios, offering significant advantages over batch processing. While batch processing handles data in large, periodic chunks, real-time processing deals with data instantly as it arrives, providing immediate insights and enabling swift responses.
SingleStore supports real-time data processing and analytics. Think of a company that has millions of data points being generated based on user interactions and behavior. SingleStore analyzes incoming data coming in milliseconds, creates segments and matches offers accordingly (the offers will be generated by an LLM model). Then finally, we take all that fast moving data, connect OpenAI’s ChatGPT (or any other model) and talk to it in plain English.
The preceding image is an example of how customers get real-time notifications and offers from a retail company. You can see the full demo here.
Batch vs. real-time data processing
Batch processing involves collecting data over a specified period, then processing it all at once. This method is suitable for large-scale data analysis where time sensitivity is not critical. However, real-time data processing shines in environments where data needs to be processed immediately to make quick decisions. For example, financial trading platforms rely on real-time data to execute trades at optimal prices, and healthcare monitoring systems use real-time data to provide critical patient status updates to medical staff.
| Feature | Batch processing | Real-time processing |
| Definition | Processing of data in large, pre-defined chunks or batches. | Continuous processing of data as it arrives, enabling instant insights and actions. |
| Data latency | High latency; data is collected over a period of time and processed later, causing delays in decision making. | Low latency; data is processed almost immediately, ensuring timely responses and actions. |
| Use cases | Suitable for non-time-sensitive tasks like end-of-day reports and payroll processing. | Essential for critical tasks like fraud detection, live analytics and stock trading where real-time decisions are crucial. |
| Data size | Handles large volumes of data at once, but this can lead to bottlenecks and inefficiencies. | Efficiently deals with continuous data streams, maintaining smooth operations without bottlenecks. |
| Scalability | Scales well for large data volumes but only periodically, limiting real-time responsiveness. | Requires highly scalable infrastructure that supports continuous data streams, ensuring consistent performance and reliability. |
| Processing time | Delayed; can be minutes to hours after data generation, leading to outdated information. | Immediate; within milliseconds to seconds of data generation, providing up-to-date information for real-time decision making. |
| Error handling | Errors are detected and managed in bulk, often causing significant delays. | Immediate error detection and handling, ensuring minimal disruption and continuous improvement in data quality and processing. |
Real-time processing systems are designed to handle rapid input and output, making them essential in fields like telecommunications and emergency response. These systems are characterized by their ability to provide a quick transaction turnaround and low latency, crucial for applications where timing is critical.
Importance of real-time data
The importance of real-time data extends across various industries, enhancing operational efficiency and customer satisfaction. Real-time analytics allow businesses to monitor and respond to customer needs promptly, improving service delivery and customer engagement. For example in retail, real-time data aids in dynamically adjusting inventory levels, helping retailers avoid out-of-stock and overstock situations.
In logistics, real-time data is used to optimize routes and deliveries, which is essential for efficiency — especially when unexpected events occur like traffic delays or weather changes. Additionally, industries like manufacturing benefit from real-time data to monitor equipment performance and predict maintenance needs before failures occur, thereby minimizing downtime and maintenance costs.
Real-time data processing not only supports operational efficiency but also enhances strategic decision making by providing businesses with the ability to act on insights derived from the latest data. This agility can lead to a significant competitive advantage in today's fast-paced market environments.
Key components of a real-time data platform
There are many key points to consider when choosing a real-time data platform, but here are a few key ones to consider that you don’t want to ignore.
Data ingestion
Real-time data platforms excel in ingesting and storing streaming data from a plethora of sources including sensors, social media feeds and transactional systems. This capability is pivotal for maintaining data freshness and relevance, which is crucial for downstream analytics and decision-making processes.
Real-time data ingestion is defined by its ability to capture data as soon as it is generated, ensuring it is immediately available for use. This process is supported by technologies like Apache Kafka, which facilitate the continuous movement of data to cloud and on-premises endpoints.
SingleStore can load data continuously or in bulk from a variety of sources. Popular loading sources include files, a Kafka cluster, cloud repositories like Amazon S3 and HDFS, or from other databases. Check out our documentation for more on data ingestion in SingleStore.
Low-latency queries
The essence of a real-time data platform lies in its ability to perform low-latency queries. These platforms are optimized for high write throughput and low-latency read access, crucial for real-time analytics applications like financial trading and emergency response systems. Real-time databases, particularly those optimized for Online Analytical Processing (OLAP) workloads, utilize columnar storage systems.
This setup enhances the speed of queries that perform filters, aggregations and joins, supporting complex analytics on large datasets with minimal delay. With SingleStore, you can have low latency queries and support for millions of real-time queries.
Multi-model support
A significant advantage of modern real-time data platforms is their multi-model support, which allows them to handle various data types — structured, semi-structured and unstructured — within a single system. This eliminates the need for multiple databases, reducing the complexity associated with managing different data models.
SingleStore supports SQL queries across different data models and enables dynamic schema modifications to meet evolving data needs. This flexibility is essential for applications requiring full-text search, relational analytics and real-time transaction processing, making SingleStore the ideal choice for organizations seeking a versatile, efficient real-time data solution.
Types of data SingleStore supports include:
- Relational
- Geospatial
- Vectors
- Key value
- JSON
- Time series
SingleStore: The real-time data platform for intelligent applications
SingleStore is a real-time data platform that operationalizes all enterprise data to your models — empowering you to build long-lasting, scalable intelligent applications. Key features of this real-time data platform include:
In-memory processing. Data is stored in memory, allowing for quick query processing
Distributed SQL architecture. This architecture enables high throughput transactional data and low latency analytics
Universal Storage. Combines the scan performance of a columnstore and the CRUD performance of a rowstore database to deliver high-performance analytics on operational data in real time
- Real-time ingestion. SingleStore supports real-time ingestion with massive parallel streaming. So, you can easily ingest data directly into your database. And since SingleStore is a distributed database, you can ingest data directly into the partitions where your data resides
- Fast analytics. SingleStore can process over 1 trillion rows per second. It supports low latency with millisecond response times — leading to 10-100x performance improvements over legacy databases
- For gen AI applications, SingleStore can also ingest new vector embeddings and make them immediately searchable. It combines full SQL, vector search and full-text search in one engine, eliminating the need to use three special purpose databases
One key feature of SingleStore is its ability to compile SQL queries into machine code, enhancing query performance and efficiency. SingleStore can be used as the only data platform for all your diverse workloads, whether you deploy in the cloud, on-premises or in a hybrid environment.
Getting started with SingleStore as a data platform
Start by activating a free SingleStore trial. Check out our Free Shared Tier, which you can use free forever.
The process to create a database and load the data is easy:
- Sign up with our Free Shared Tier
- Create a database
- Load data using Stages or S3 bucket
- Query your data
Once you sign up, head over to SingleStore Spaces to start building. Here’s one of our favorites on creating a real-time recommendation engine.
We have delved into the multi-faceted benefits and capabilities that a unified real-time data platform, particularly SingleStore, brings to the table for businesses building their intelligent applications. Emphasizing the critical role of real-time data analytics, OLAP, OLTP workloads and more, these platforms have proven fundamental in propelling modern businesses forward.
SingleStore is an all-in-one real-time database, exemplifying how advanced databases can support diverse workloads while providing the scalability and speed required for immediate analytics and decision processes.
Start free and experience SingleStore as an all-in-one data platform.
