Bringing AI to your organization? Better bring the right database

By Patrick McFadin, DataStax developer relations and contributor to the Apache Cassandra project.

Netflix tracks every user’s actions to instantly refine its recommendation engine, then uses this data to propose the content users will love. Uber gathers driver, rider, and partner data in the moment and then updates a prediction engine that informs customers about wait times or suggests routes to drivers in real time. FedEx aggregates billions of package events to optimize operations and instantly share visibility with its customers on delivery status.

These leaders succeed with these real-time AI capabilities in large part because of their ability to aggregate massive amounts of real-time data from customers, devices, sensors, or partners as it moves through applications. This data in turn is used to train and serve machine learning models. These companies act on this data in the moment, serving millions of customers in real time. And they all rely on the open-source NoSQL database Apache Cassandra^®.

Let’s take a look at why Cassandra is the database of choice for organizations building enterprise-scale, real-time AI applications.

The challenges posed by real-time AI

Only 12% of AI initiatives succeed in achieving superior growth and business transformation, according to Accenture. Why? In a nutshell, data scientists and developers have been trying to build the most powerful, sophisticated applications for the next generation of business on complex infrastructure built for the demands of yesterday.

Many traditional AI/ML systems, and the outcomes they produce, rely on data warehouses and batch processing. The result: A complex array of technologies, data movements, and transformations are required to “bring” this historical data to ML systems. This alters and slows the flow of data from input to decision to output, resulting in missed opportunities that can open the door for customers to churn or allow recognized cyber security threat patterns to go undetected and unmitigated.

The velocity, type, and volume of data drive the quality of predictions and the impact of the outcomes. Real-time AI demands large amounts of data to train ML models and make accurate predictions or generate new content very quickly. This requires a high-performance database that can bring ML to the data. You’ve created the right architecture to collect and store your data and the best way to keep costs low is to leverage what you have. The solution to a storage cost problem is not adding more storage; it’s finding ways to process your data in place.

Enter Cassandra

There are various databases that can be used to develop a real-time AI application. Relational databases such as MySQL or PostgreSQL may be user-friendly, but they are not capable of managing the vast amounts of data required for web-scale AI applications. Although open-source data stores like Redis are available, they lack the durability necessary to support AI applications that are intended to form the foundation of a business.

For real-time AI to live to its full potential, the database that serves as its foundation must be:

• highly scalable to manage massive amounts of data
• reliable for continuous data access
• fast enough to easily capture big data flows
• flexible enough to deal with various data types.

Cassandra is an open-source NoSQL database that scales with performance and reliability better than any other. Many companies, like those mentioned above, have transformed their businesses and led their industries thanks to real-time AI built on Cassandra. Why?

Horizontal scalability: As AI applications become more sophisticated, they require the ability to handle ever-increasing volumes of data. Cassandra’s distributed architecture is based on consistent hashing, which enables seamless horizontal scaling by evenly distributing data across nodes in the cluster (a collection of nodes). This ensures that your AI applications can handle substantial data growth without compromising performance, a crucial factor from a statistical perspective.

High availability: The decentralized architecture of Cassandra provides high availability and fault tolerance, which ensures that your AI applications remain operational and responsive even during hardware failures or network outages. This feature is especially important for real-time AI applications, as their accuracy and efficiency often rely on continuous access to data for mathematical modeling and analysis.

Low latency: With real-time AI, signals generated by user activities must be captured at a very high rate; the ability to write this data to a database fast is critical. Cassandra’s peer-to-peer architecture and tunable consistency model enable rapid read and write operations, delivering low-latency performance essential for real-time AI applications.

Unlike many other data stores, Cassandra is designed in a way that doesn’t require disk reads or seeks during the write process, so writing data to Cassandra is extremely fast and provides the freedom to capture incoming signals with ease—no matter how fast they arrive.

It ensures that AI algorithms receive the latest data as quickly as possible, allowing for more accurate and timely mathematical computations and decision-making.

Flexible data modeling: Cassandra’s NoSQL data model is schema-free, which means that the methodology for storing data is far more flexible than alternative databases, making it possible to store and query complex and diverse data types common in ML and AI applications. This flexibility enables data scientists to adapt their data models as requirements evolve without having to deal with the constraints of traditional relational databases.

The Cassandra community

The Cassandra open-source project is built and maintained by a community of very smart engineers at some of the biggest, most-advanced users of AI (Apple, Netflix, and Uber, to name a few) who are constantly modernizing and extending the capabilities of the database. The upcoming Cassandra 5.0 release, for example, will offer vector search, a critical feature that will be a groundbreaking aid to organizations grappling with the massive datasets that accompany AI efforts.

These advantages make Cassandra a reliable foundation for real-time AI applications that need to handle massive volumes of data while ensuring continuous data access, high performance, and adaptability. If your organization aims to leverage AI to its full potential, choosing the right database is a critical step in your journey.

By adopting a scalable and durable solution like Cassandra, you can ensure the successful execution of your AI initiatives, reduce cost, and optimize processing. It’s time to reconsider your data infrastructure and invest in the right technology to fuel your growth. Remember, the success of your AI strategy doesn’t only lie in the complexity of your algorithms but also in the robustness of your data management system.

Join the growing community of businesses pioneering the future of AI with Cassandra. Seize the opportunity today and equip your business to make the most of real-time AI.

Learn how DataStax makes real-time AI possible here.

About Patrick McFadin

Patrick McFadin is the co-author of the O’Reilly book “Managing Cloud Native Data on Kubernetes.” He works at DataStax in developer relations and as a contributor to the Apache Cassandra project. Previously he has worked as an engineering and architecture lead for various internet companies.