June 2023 Summaries

ACI Worldwide, a leader in combating fraud and driving innovation in the payments industry, leverages real-time data and machine learning to deliver exceptional value to its customers. The company's primary challenge is keeping up with the ever-evolving payment landscape and addressing inconsistencies in data schemas across products. ACI has chosen Apache Cassandra and DataStax for their exceptional performance capabilities, enabling them to achieve best-in-class KPIs for fraud management solutions. Looking ahead, ACI plans to move towards cloud-based solutions, incorporate more data from its products into the data warehouse, and expand its consortium data model. For other enterprises working with data, John Madden advises taking one piece at a time, not being afraid to fail, and following where the data insights lead.

Digital River, a technology company specializing in ecommerce solutions, has been using DataStax Astra DB to optimize its real-time data environment and support the growth of online businesses. The company's technology stack includes APIs and microservices, with DataStax Astra DB playing a key role in managing real-time data for fraud detection, inventory management, pricing, and delivery costs. Digital River chose Apache Cassandra and DataStax Astra DB due to their scalability, performance, and always-on availability features. By using DataStax Astra DB, the company has achieved a 60% reduction in total cost of ownership and faster time to market for new applications.

DataStax has introduced a new feature, the GPT Schema Translator, which uses generative AI to create schema mappings for streaming pipelines in its Astra Streaming service. This tool automates the process of mapping schemas between different systems within a pipeline, reducing complexity and time spent on manual mapping efforts. The translator supports various data types and structures, making it easier to manage evolving schemas due to changes in data sources or business requirements. By automating schema translation, developers can focus more on building and maintaining real-time pipelines instead of dealing with the intricacies of manual mapping.

In a recent fireside chat at the Gartner Apps Summit with Curtis Cook, director of back-end engineering at Commonstock, an investment social network platform, they discussed their experience in evaluating different streaming options for real-time data. Commonstock leveraged DataStax Astra Streaming to build a robust architecture that enables high-velocity application development, data distribution, and next-generation AI. The platform allows users to share their investment track records and real-time trades linked to their brokerage accounts while facilitating verified knowledge exchange between investors for well-informed decisions. Commonstock's adoption of Astra Streaming provided more than double the throughput and significantly lower latency compared to other streaming technologies like Kafka and Apache Pulsar. The platform also focuses on real-time data processing, enabling a seamless user experience and facilitating the integration of AI into application design patterns.

Companies are increasingly integrating AI into their business operations, particularly generative AI technology. To support this integration, a robust database solution is needed to handle the scale, performance, security, and unique requirements of these new applications. Apache Cassandra has emerged as an effective distributed database optimized for generative AI, with its ability to manage large amounts of data and provide high availability. Datastax, in collaboration with Google Cloud, has developed several significant new capabilities for Cassandra and Astra DB to enhance their effectiveness as databases for AI applications. These include the CassIO open source library, Google Cloud BigQuery integration, and Google Cloud DataFlow integration. Additionally, a new vector search tool is available in DataStax Astra DB, making it an ideal option for both startups and enterprises that manage sensitive user information and want to build impactful generative AI applications.

Apache Cassandra® has emerged as a powerful distributed database solution, handling massive amounts of data and providing high availability. With the introduction of generative AI and large language models (LLMs), new query capabilities are needed. Vector search is a revolutionary feature that enhances Cassandra's search and retrieval functionalities for generative AI applications. It leverages vector similarity calculations to focus on the semantic meaning and similarity of data points, enabling more accurate and intuitive search results. The integration of vector search with Cassandra offers several benefits, including querying unstructured data, performing similarity-based queries, reducing latency, improving overall query performance, and supporting diverse applications like recommendation systems, fraud detection, image recognition, and natural language processing. Vector search is particularly useful for enhancing generative AI use cases by allowing developers to create more relevant prompts and caching LLM responses. A software framework called CassIO has been created to integrate seamlessly with popular LLM software such as LangChain, making it easy to leverage vector search in your database.