The advent of Generative AI (GenAI) has revolutionized an array of industries by ushering in applications capable of generating human-like text, images, audio, and increasingly more. However, to develop reliable, real-time GenAI applications, it is not only the size and capabilities of the underlying model that are important but also how quickly and efficiently your application can process data. This is where streaming databases come into play.
This post explores the role of streaming databases in building real-time GenAI applications, providing insights into how they work and their benefits, as well as an overview of the top data streaming solutions and how to select the most suitable option for your GenAI application.
Understanding Streaming Databases
Streaming databases are designed to process continuous data streams in real time as they are generated from a source, such as an application. This is in contrast with a traditional relational database management system (RDBMS), in which static data is typically processed in batches at regular intervals, i.e., batch scheduling.
Initially developed for the financial industry, to handle the high-velocity data associated with stock trading and fraud detection, streaming databases have evolved to support a wide range of real-time applications across all industries. By collecting, processing, and analyzing data as soon as it’s available, streaming databases enable immediate actions and insights within applications and systems.
As opposed to a specific type of database, the term “streaming database” actually refers to several types of databases that process streaming data in real-time, including in-memory, NoSQL, and time-series databases. The core capabilities of a streaming database include:
- Data Streaming: the ability to process a continuous flow of data generated from various sources, known as producers. As producers generate data, the streaming database processes it and delivers it to endpoints, referred to as consumers.
- Event-Driven Processing: instead of querying sources for new data, streaming databases listen for predefined events, such as data being added or changed, which trigger it to process data. This is essential for time-critical applications for which intermittent batch processing isn’t feasible.
- Real-Time Analytics: streaming databases allow for the instant analysis of live data, which enables faster, data-driven decision-making and the ability to deliver better products and services.
The Benefits of Streaming Databases for GenAI Applications
Here are some of the key advantages that streaming databases bring to GenAI applications:
- Real-Time Data Processing: streaming databases enable real-time data processing, which is essential for GenAI applications that require the most up-to-date information to perform effectively. Real-time chatbots and recommendation systems, for instance, benefit from streaming databases as they grant them access to the most current available information – resulting in superior user experiences.
- Scalability and Performance: streaming databases are designed to process large volumes of continuous data with minimal latency by distributing processing tasks across multiple nodes. This enables horizontal scaling as data loads increase, which is desirable for GenAI applications that need to process vast amounts of data efficiently.
- Easy Integration with AI Tools: GenAI platforms integrate seamlessly with streaming databases for efficient end-to-end application development. As well as enhancing the performance of GenAI applications, streaming databases support the continuous training and updating of AI models, by providing real-time access to continuously updated datasets, helping to improve their accuracy and capabilities.
Top Streaming Databases for Real-Time GenAI Applications
Let us turn our attention to looking at the top streaming data platforms, considering open-source, source-available, and closed options.
Open-Source Streaming Databases: as they’re free to use (to a certain extent, in some cases), you might opt for an open-source platform if you need to minimize costs. Alternatively, if customization and control are required for your project, the transparency and access to the code provided by open-source databases make them a great fit.
- Apache Kafka: a widely-used data streaming platform known for its high throughput, fault tolerance, and scalability, making it ideal for building enterprise-level streaming applications
- RisingWave: well-suited for GenAI applications with its real-time SQL-based analytics and cloud-native scalability, enabling dynamic data exploration and interactive generation tasks.
- Arroyo: provides low latency and fault-tolerant processing, crucial for real-time adjustments and instant responses from GenAI applications that require real-time content generation and refinement.-
Source-Available Streaming Databases: the licensing agreements for source-available streaming databases sit between open and closed-source to various degrees, with their vendors placing certain restrictions on how you can use their database, e.g., the number of users.
- KsqlDB: Built on Kafka Streams, it provides powerful real-time SQL queries on streaming data, making it effective at interactive data manipulation and dynamic content generation.
- Materialize: provides instant materialized views with minimal latency, facilitating real-time data insights and immediate feedback, essential for interactive GenAI applications that rely on up-to-date data.
- EventStoreDB: specializes in event sourcing with strong consistency, allowing for efficient handling of event-driven GenAI applications – particularly when tracking and managing complex event histories.
Closed-Source Streaming Databases: when stability and security are of paramount importance, as in production environments, then a closed-source streaming database is most suitable. Another reason to go the closed-source route is if support is essential, as the vendor takes responsibility – as opposed to it being a community effort, as with open-source solutions.
- Timeplus: excels in time-series analysis with advanced queries and visualizations, which is ideal for GenAI applications that involve real-time monitoring and dynamic content generation based on temporal data
- DeltaStream: offers seamless real-time data transformation and rapid pipeline deployment, which is essential for GenAI applications that require continuous and dynamic data processing.
Considerations for Selecting a Streaming Database
Here are the main aspects to consider when choosing a streaming database for your GenAI application:
- Performance Metrics: evaluate metrics such as latency, throughput, and processing speed to ensure your chosen database platform meets the performance demands of your application.
- Scalability: how well the database handles increasing data volumes and concurrent users without compromising performance. This includes its support for vertical, horizontal, and elastic, i.e., automatic, scaling.
- Ease of Use and Integration: how intuitive the streaming database’s user interface is, as well as how easily it integrates with the existing tools and platforms within your application’s ecosystem.
- Cost Considerations: calculate the initial setup and ongoing operational costs, including licensing fees, infrastructure, and maintenance. It’s also important to factor in the potential hidden expenses associated with managing open-source solutions, such as support costs.
Conclusion
By providing continuous, event-driven data processing, streaming databases play a crucial role in the development of performant real-time GenAI applications. Their scaling capabilities ensure performance isn’t compromised as your application grows and their ease of integration incurs a low technical overhead when adding them to your IT ecosystem.
As organizations find new and innovative ways to integrate GenAI into their operations, the use of streaming databases is sure to become increasingly common. We encourage you to explore the concepts and solutions from this post further, so you can determine how streaming databases can improve the efficacy of your real-time GenAI applications.
