Real-time graph analytics combines streaming data technology, graph databases, and graph algorithms to tackle problems not suited for relational databases and batch processing. It works best for complex social network research, creating product recommendations for e-commerce platforms, or detecting fraudulent transactions in large systems.
Let’s start with real-time analytics
Real-time analytics is the process of preparing and measuring data as it arrives in the system and offers low response times that can deal with large amounts of data with high velocity. It can answer queries within seconds, making it possible for data science teams to understand relationships, automate processes, and make predictions immediately.
There’s a lot of data businesses analyze in real-time, here are a couple of the most common examples. If you want to expand on this subject, you can have a look at our guide for real-time data.
Customer relationship management (CRM) data: Customer’s relevant personal information, number of purchases, general interest
Enterprise resource management (ERP) data: Transactional data, analytical data, master data
Third-party SaaS vendors or solution providers data: Account balance, transaction
Website and application data: Number of users, bounce rate, top traffic source
Customer support system data: Customer’s ticket type, satisfaction level, personal information
A little bit about graph technology
In graph databases, data relationships are represented by graph models. Users can apply machine learning algorithms and other statistical techniques to these models, allowing for more efficient data analysis at scale across large volumes of specific types of data. When it comes to graph analytics, we use graph algorithms that look at the paths and distances between the nodes, as well as the importance and clustering of the nodes. To assist in evaluating the importance, the algorithms will frequently check incoming edges and the relevance of adjacent nodes. Graph algorithms are employed to model data connections, and they allow for querying and data analytics considering those relationships. Mainly, a property graph is made up of nodes that hold specific information about a subject, as well as edges that show how the nodes are related.
Graph databases explicitly capture the relationship between data points, allowing the various algorithms and queries to run in milliseconds rather than for several hours on relational databases that use unsuitable data models.
Use cases for real-time graph analytics
Real-time graph analytics can help turn data into insights immediately after it’s collected. It can predict when your system is about to be breached. For example, some graph database providers can help companies utilize real-time graph analytics to detect and prevent credit card fraud even before the transaction is completed. Graph databases ensure that relationship-oriented queries are conducted in real-time. Representing a series of credit card transactions as a graph enables your team to instantly identify and stop fraudulent activities. Ecommerce businesses are using real-time analytics to personalize the user experience and boost the number of sales. They utilize the specific technology to assess the user’s activity over the last 5 minutes. Based on the analysis, the eCommerce websites recommend the items to the buyers that they have been looking for. Ritual, a health tech company, generated a huge number of sales from the new product lines by implementing personalized banner ads, targeted bundled offers, and email campaigns.
