July 2023 Summaries

Confluent's event streaming platform was adopted by ACERTUS to integrate data from its multiple databases in real-time, enabling the development of microservices such as real-time order notifications. Prior to adopting Confluent, ACERTUS relied on batch processing and manual processes, which were inefficient and error-prone. The adoption of Confluent's event streaming platform allowed ACERTUS to build a centralized data warehouse and integrate its databases in real-time, resulting in improved customer experience, operational efficiencies, and reduced costs. With Confluent, ACERTUS was able to deploy microservices faster and use AI to analyze customer needs and find a good price for cars in real time.

The text discusses when and why it might be necessary to set up additional Apache Kafka clusters, despite the capability of a typical five-to-six node Kafka cluster to handle large volumes of data efficiently. A single cluster can manage up to 200,000 partitions, but high-end infrastructure can push this limit. The reasons for deploying multiple clusters include compliance requirements, geographic distribution, disaster recovery, and independent scaling for different business lines. While multiple clusters may introduce integration challenges, a single cluster offers advantages like simpler event correlation, cost efficiency, and reduced operational complexity. Confluent Cloud and tools like Confluent ksqlDB, KStreams, and Apache Flink enhance Kafka's data streaming capabilities, enabling real-time business insights by processing data in motion. The text also highlights best practices for federated service governance for large Kafka deployments, emphasizing security, capacity management, and self-service operations to optimize Kafka's use as a central data platform.

The Connect with Confluent (CwC) partner program is designed to enhance the global data streaming ecosystem by allowing technology partners to integrate Confluent directly into their platforms, providing a fast path to support end-to-end real-time use cases. As enterprises increasingly demand real-time data access for improved customer experiences, the program offers partners access to Confluent’s extensive data stream network, which processes over an exabyte of data annually, as well as expert resources across engineering, marketing, and sales to maximize integration and consumption. The program addresses the challenges of modular application development where critical business data is often dispersed across disconnected systems by facilitating low-latency data movement without the costs and complexities associated with traditional cloud storage. Through partnerships with Confluent, companies can leverage a cloud-native data streaming platform that offers built-in stream processing, governance, and security, enabling them to rapidly deploy new use cases securely and reliably across multiple environments. This collaboration not only enhances customer innovation but also accelerates market adoption and expands sales opportunities by offering a superior experience in managing real-time data streams.

The text discusses how data streaming with Apache Kafka® can support the distribution of real-time data for AI applications. It highlights that effective AI requires continuous learning and adaptation through continuous data ingestion, which is enabled by data streaming. Confluent, powered by Apache Kafka®, offers a massive ecosystem of connectors to tap into existing data stores and curate them for consumption by AI tools. Data streaming provides AI applications with the ability to perform continuous training on data streams, efficient and constant data synchronization from source systems to machine learning platforms, real-time application of AI models, and access to high volume data processing. The text also emphasizes the importance of democratizing data across organizations to enable AI teams to find and tap into data sets in a frictionless manner without setting up point-to-point integrations. Additionally, it mentions that Confluent's data streaming architecture is deployed with role-based access control (RBAC) and attribute-based access control (ABAC) at a granular level to ensure secure and trusted access to data. The text concludes by encouraging readers to reach out to a Confluent expert for more information on how to securely feed AI systems with data.