Subham Rakshit

Organizations are accelerating innovation with generative and agentic AI and getting there depends on a handful of real-time data primitives: durable, high-throughput ingestion; stream processing and enrichment; low-latency, event-driven delivery; and a scalable place to land and serve data.

In today's fast-paced environment, timely insights from data are crucial for organizations to maintain a competitive edge and respond effectively to market and customer demands.

Event streaming in Apache Kafka is powerful, but sometimes a queue-based model is more suited for workloads where parallel consumption is more important than ordering. KIP-932 introduces share groups, a feature that allows consumers to cooperatively consume data from Kafka topics, akin to how message queues work.

Building a resilient and highly available Apache Kafka cluster on AWS involves more than just deploying across multiple Availability Zones and Regions. This advanced chalk talk explores Kafka cluster resiliency complexities.

Change Data Capture (CDC) from various source databases to a data lake is critical for analytics workload. However different CDC mechanism exists and there are trade-offs with the approach.

Change Data Capture (CDC) from various source databases to a data lake is critical for analytics workload. However different CDC mechanism exists and there are trade-offs with the approach.

Data enrichment is a critical step in stream processing. Real-time enrichment of streaming data with contextual information adds missing information, improves accuracy, increases trustworthiness, and facilitates better decision-making.

In order to run a multi-tenant, Apache Kafka–based, real-time data streaming platform, security and trust are paramount. Organizations often seek architectural recommendations when building Zero Trust, secure Kafka clusters.

In the race to digitally transform, companies face the challenge of garnering value from an enormous volume of generated data. It is crucial to analyze, learn, and make predictions from your data in a timely manner because the value of data diminishes over time.