Top Data Streaming Trends:

• Democratization of Kafka: Evolved from specialized tool to essential in tech stack. End users face complexity in choosing right solution considering cost, performance, and features. Prioritize features for strategic needs. Commoditization offers affordable, customizable options.
• Kafka Protocol as the Standard: Vendors adopt Kafka protocol for flexibility and cloud optimization. Confluent introduced KORA engine. Other players followed with their cloud-native "data streaming engines". Challenges include limited compatibility. Evaluate solutions critically for seamless integration.
• BYOC Deployment Model: Gaining traction for security and compliance. BYOC allows businesses to host Kafka in their VPC while vendor manages control plane. Challenges include balancing security and ease of use. WarpStream's "zero access" model addresses these issues.
• Flink Becomes the Standard for Stream Processing: Apache Flink is the premier choice for stream processing with high throughput, low latency, and stateful operations. Challenges with Apache Spark include micro-batch architecture, limited stateful processing, fault tolerance complexity, performance overhead, and scalability challenges. Flink provides a more versatile and efficient solution.
• AI and Data Streaming: Real-time model inference with Flink opens new possibilities for predictive and generative AI. Challenges include providing context for AI applications. Leveraging Flink enables real-time context and model inference, supporting advanced machine learning workflows.
• Becoming a Data Streaming Organization: Companies shift to structured, enterprise-wide data streaming strategies. Challenges include fragmented efforts and data silos. Solution is building an integrated data streaming organization with unified platform, self-service, data as a product, alignment, and governance.