Native Apache Kafka Service Is Coming Soon to StreamNative Cloud. Join the waitlist and get $1,000 in credits.

Join Waitlist >
StreamNative Logo
WebinarApr 24, 2025

Kafka Migration Best Practices: Minimize Cost & Downtime

Unlock Instant Access

Complete the form to start watching.

Webinar Overview

Learn Kafka migration best practices and how to ensure a smooth transition without disruption using StreamNative UniLink to minimize costs and eliminate downtime.

Join StreamNative and Nexaminds for an expert-led webinar covering Kafka migration best practices. Learn how to minimize costs and eliminate downtime using StreamNative UniLink technology.

What You'll Learn

  • Best practices for migrating Kafka workloads with zero downtime
  • How StreamNative UniLink enables seamless Kafka migration
  • Strategies for reducing infrastructure costs during and after migration
  • Real-world migration experiences from Nexaminds' data engineering team

About Speaker

Kundan Vyas

Kundan Vyas Kundan is a Staff Product Manager at StreamNative, where he spearheads StreamNative Cloud, Lakehouse Storage and compute platform for connectivity, functions, and stream processing. Kundan also leads Partner Strategy at StreamNative, focusing on building strong, mutually beneficial relationships that enhance the company's offerings and reach.

Tejpal Dhillon

Tejpal Dhillon Tejpal Dhillon is a Solutions and Sales Engineering Leader at Nexaminds with 30 years of experience in digital transformation, enterprise architecture, and the implementation and management of large-scale enterprise platforms and shared services.

Guillermo Hernandez

Guillermo Hernandez Senior Data Engineer at Nexaminds, specializing in data architecture, real-time processing, and cloud-based solutions. With expertise in technologies like Kafka, Spark, Snowflake, Airflow, and AWS. Have helped enterprises optimize their data pipelines and enhance decision-making with scalable, high-performance systems. Passionate about automation and data quality. Committed to driving innovation in modern data engineering.