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The Confluent data streaming platform introduced a new improvisation of Confluence Cloud, its fully managed service compiled on Apache Kafka. The key upgrade is to introduce queries to images that allow users to ask for dose and streaming data using SCH SQL. The company has also introduced new private networks and security functions that increase the processing of flows safer and ready for business.
The AI, which concerns autonomous systems capable of considering, planning and acting on behalf of users, quickly moves from the emerging concept to the basic element in business technology. However, for AI agents make the right decisions and bring maximum value, they need streaming and historical data sets. This may be a problem if the data is placed in different systems such as older databases and separate platforms for cloud storage. Complex ETL tasks may be required to combine data, but it would add complexity, costs and inefficient to the overall workflow.
Confluent aims to solve this problem by queries on images by providing AI agents a unified approach. Instead of relying on fragile pipes that mix data between batch and stream systems, images are designed to combine everything in one place. This means that teams can work with a single language of questions and analyze past trends and respond to live events without making a separate workload or synchronizing across instruments.
“Agencies ranges from Hype adoption to business because the organization is trying to gain a competitive advantage and win on the market,” said Shaun Clowes, Chief Product Director of Confluent. “But without high -quality data, even the most advanced systems cannot bring real value. The new Confluent Cloud for FLINK APACHE functions allows you to mix data in real time and doses so that businesses can trust their agent AI to control real change.”
According to Confluence, images of images could be, in particular, using the data that reflects the status of the data at a certain time, the analysis of historical audit data for the purposes and tuning problems by exploring past data states. Images questions would also be useful for developers who build AI agent systems and workflows for processing events that require historical data enrichment.
Available in early access via Confluent Cloud for Apache FLINK, Snapshot questions rely on the advanced SQL query optimizer, which determines that data should be loaded from topical substances Kafka or open table formats such as Apache Iceberg or Delta. This feature uses the Tableflow table to materialize the flow of Kafka events into these tables, allowing an effective historical approach together with real -time processing.
This translates less complexity for users. For example, a developer that builds a fraud detection system no longer has to manually organize pipes to pull out historical transaction formulas from one system and live activity from another. Instead, they can write a single SQL query and the new automatic Snapshot Query Engine module determines where data increases live and load it efficiently.
AI AI needs more than speed, needs context. As Stewart Bond IDC states, the aim is to “unify diverse data types, including structured, non -structural, real and historical information in one environment.” This is what Confluence love is to add with the latest questions about FLINK -powered images.
Many organizations hesitate to deploy real -time systems in scale due to the risks of security and compliance, especially in a hybrid cloud. The latest Confentuent updates bring a new level of security and efficiency on FLINK workload, in particular through CCN routing (Confluent Cloud Network) and IP filtering.
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CCN routing works by leaving teams to re -use their existing private network configurations that already for coffee. This means that Doe has to create new network settings from scratch to trigger FLINK workload, saved time and reduce complexity. By extending the same secure connection to FLINK, teams can keep up from security principles in both data systems. CCN routing is now generally available on Amazon Web Services (AWS) in all regions where FLINK is supported.
Many organizations running in Hybrid need stricter control over which data can be publicly accessed. IP filtering for FLINK helps limiting access to the approved IP address and facilitates monitoring unauthorized attempts. When paired with CCN routing, the team gives more control over their FLINK workload and helps satisfy the needs of security and compliance with real -time regulations. IP filtering is now generally available to all cloud confluence users.
The platform updates show that Confluence wants to move to Kafka and become a fully streaming data platform to build for modern AI needs. It is not only about adding new abilities, but also signaling a wide shift of the strategy.
While Snowflake built its basis on batch processing and databricks promotes the Lakehouse, Confluence more focuses on providing intelligence in real time. Apache flink is in the center of this pressure. For AI AI and another fast -moving workload, FLINK systems provide the new context that they need to make a smart decision.
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(Tagstotranslate) Agent (T) AI Agents (T) Apache FLINK (T) BATCH (T) Confluencies (T) Streaming Data (T) Kafka