Confluent Unites Batch and Stream Processing for Faster, Smarter Agentic AI and Analytics - Middle East Business News and Information
'Agentic AI is moving from hype to enterprise adoption as organizations look to gain a competitive edge and win in today's market,' said Shaun Clowes, Chief Product Officer at Confluent. 'But without high-quality data, even the most advanced systems can't deliver real value. The new Confluent Cloud for Apache Flink® features make it possible to blend real-time and batch data so that enterprises can trust their agentic AI to drive real change.'
Bridging the Real-Time and Batch Divide
'The rise of agentic AI orchestration is expected to accelerate, and companies need to start preparing now,' said Stewart Bond, Vice President of Data Intelligence and Integration Software at IDC. 'To unlock agentic AI's full potential, companies should seek solutions that unify disparate data types, including structured, unstructured, real-time, and historical information, in a single environment. This allows AI to derive richer insights and drive more impactful outcomes.'
Agentic AI is driving widespread change in business operations by increasing efficiency and powering faster decision-making by analyzing data to uncover valuable trends and insights. However, for AI agents to make the right decisions, they need historical context about what happened in the past and insight into what's happening right now. For example, for fraud detection, banks need real-time data to react in the moment and historical data to see if a transaction fits a customer's usual patterns. Hospitals need real-time vitals alongside patient medical history to make safe, informed treatment decisions. But to leverage both past and present data, teams often have to use separate tools and develop manual workarounds, resulting in time-consuming work and broken workflows. Additionally, it's important to secure the data that's used for analytics and agentic AI; this ensures trustworthy results and prevents sensitive data from being accessed.
Snapshot Queries Unify Processing on One Platform
In Confluent Cloud, snapshot queries let teams unify historical and streaming data with a single product and language, enabling consistent, intelligent experiences for both analytics and agentic AI. With seamless Tableflow integration, teams can easily gain context from past data. Snapshot queries allow teams to explore, test, and analyze data without spinning up new workloads. This makes it easier to supply agents with context from historic and real-time data or conduct an audit to understand key trends and patterns. Snapshot queries are now available in early access.
CCN Routing Simplifies Private Networking for Flink
Private networking is important for organizations that require an additional layer of security. Confluent offers a streamlined private networking solution by reusing existing CCNs that teams have already created for Apache Kafka® clusters. Teams can use CCN to securely connect their data to any Flink workload, such as streaming pipelines, AI agents, or analytics. CCN routing is now generally available on Amazon Web Services (AWS) in all regions where Flink is supported.
IP Filtering Protects Flink Workloads in Hybrid Environments
Many organizations that operate in hybrid environments need more control over which data can be publicly accessed. IP Filtering for Flink helps teams restrict internet traffic to allowed IPs and improves visibility into unauthorized access attempts by making it easier to track the attempts. IP Filtering is generally available for all Confluent Cloud users.
Now organizations can more easily turn the promise of agentic AI into a competitive advantage. To learn more about the other new Confluent Cloud features, including the Snowflake source connector, cross-cloud Cluster Linking, and new Schema Registry private networking features, check out the launch blog.
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