Technology giants like Netflix, Uber, and Meta have set the standard for how applications experience user data. Users expect data to be integrated into applications, making it easy to find relevant content, track delivery, provide a spam-free Internet experience, and make quick, informed operational decisions. Up to this point, the speed and scale of real-time analytics has been challenging to achieve in applications.
Real-time analytics requires custom-built technologies and armies of data and infrastructure engineers to manage them. This is changing with the widespread adoption of real-time streaming data and cloud services that ease operations and improve resource efficiency.
This article explains real-time analytics, compares it with batch analytics, and provides examples and benefits across industries.
What is real-time analytics?
Real-time analytics is about using data as it is generated to answer questions, make predictions, understand relationships, and automate processes. Gartner defines it as “the discipline that applies logic and mathematics to data to provide insights for better decision making.” The main requirements of real-time analytics are access to new data and fast queries, which are essentially two measures of latency: data latency and query latency.
Data Latency: Data latency is a measure of the time between the time data is generated when it is queryable. There is usually a time lag during this process, and real-time analytics databases are designed to minimize that lag, allowing changes to the data to be reflected quickly.
Low data latency can be challenging to deliver, as the database must be able to write incoming data while simultaneously allowing applications to perform queries on the most recent data. This means having a database that can handle high write rates and is optimized for real-time data processing, not batch analytics jobs, which has traditionally been the data processing method for analytics.
Query Latency: Query latency is the time required to execute a query and return the result. Applications want to reduce query latency for fast, responsive user experiences, and teams are increasingly setting sub-second query latency benchmarks for their data applications. That said, massaging data and optimizing indexes to deliver consistently low query latency can be time-consuming, making it challenging for teams to iterate and expand their analytical features.
real-time vs batch analytics
Real-time analytics is optimized for low-latency analytics and ensures that data is available to query within seconds, while batch is high-latency analytics, where queries are performed on data that is at least ten minutes or hours old. but returns the result.
One use case for batch analytics is business intelligence reporting, which uses historical data to report on business trends and answer strategic questions. In these scenarios, the goal is to use the data to formulate strategy; not take immediate action. Real-time data generally will not affect the results of trend analysis, making it better suited for batch analysis. Batch analytics use cases such as business intelligence, reporting and data science have less stringent latency requirements and therefore can tolerate ETL pipelines to homogenize and enrich data for analytics. In contrast, real-time use cases require low latency and attempt to reduce or remove the need for ETL processes.
Many analytics systems such as Hadoop and data warehouses were designed for batch analytics. Batch analytics systems process data in batches, with data collected and loaded into the system over time. Instead of having “always on” systems for data processing, they can limit data processing to specific time intervals to reduce costs. Batching also helps in data compression, reducing the overall storage footprint and making it economical for periodic analysis on large scale data.
On the other hand, databases designed for real-time analytics have native support for semi-structured data and other modern data formats to avoid ETL processes and achieve low data latency. They are also optimized for compute efficiency to reduce the resources needed to continuously process incoming data and execute high-volume queries.
Use case for real-time analytics
The growing demand for real-time analytics is being driven by several benefits.
Fast, Responsive Experience: Fast, responsive experiences increase user acceptance. An investment management firm increased its application utilization by 350% by reducing the latency of its user-facing analytics. As a result, application insights became embedded in the day-to-day decision making of the organization.
Faster decision making: If it takes seconds or minutes for each query on your data to come back, you don’t delve as deeply into the information and rely more on intuition. Seesaw, the edtech company used by more than 10 million K-12 teachers, created a data-driven culture with sales, support, and product teams using real-time analytics to quickly improve the experience for schools and teachers.
Semi-automated and automated intelligence: Automated or semi-automated intelligence can reduce the cognitive load of decision making. Whatnot, a live video marketplace, uses a real-time ranking engine to showcase viral videos, relevant social interactions, and personalized shopping recommendations to keep users engaged on the site.
Time-sensitive interventions: Time-sensitive interventions save on operating costs and increase revenue. Command Alcon, a construction logistics company, tracks concrete deliveries in the North America market, ensuring construction sites are ready for delivery. Since concrete has a short life, sites need to be ready to use concrete immediately or risk jeopardizing the entire construction project.
Increase in real-time analytics
Real-time analytics databases have matured, making it easier for engineering teams to access streaming data and achieve low-latency analytics. Engineering teams don’t need to custom-build or self-manage complex, distributed systems to get real-time analytics.
The most fundamental change enabling the growth in adoption of real-time analytics is the cloud. Companies can scale up and scale down resources to meet changing application demands, avoiding overpaying for extra capacity when traffic is slow. Real-time Analytics Database also separates storage and compute, so you no longer need to over-provision resources to get better cost-performance at scale. The cloud provides new levels of operational simplicity and resource efficiency that will put real-time analytics within reach of even more companies in 2023.