In today's business environment, with the rapidly increasing volume of data and the growing pressure to respond to events in real-time, organizations need data-driven strategies to gain valuable insights faster and increase their competitive advantage. To meet these big data challenges, you need a massively scalable distributed streaming platform that supports multiple producers and consumers, connecting data streams across your organization. Apache Kafka and Azure Event Hubs provide such distributed platforms. How is Azure Event Hubs different from Apache Kafka? Apache Kafka and Azure Event Hubs are both designed to handle large-scale, real-time stream ingestion. Conceptually, both are distributed, partitioned, and replicated commit log services. Both use partitioned consumer models with a client-side cursor concept that provides horizontal scalability for demanding workloads. Apache Kafka is an open-source streaming platform which is installed and run as software. Event Hubs is a fully managed service in the cloud.
I guess you came to this post by searching similar kind of issues in any of the search engine and hope that this resolved your problem. If you find this tips useful, just drop a line below and share the link to others and who knows they might find it useful too.
Stay tuned to my blog, twitter or facebook to read more articles, tutorials, news, tips & tricks on various technology fields. Also Subscribe to our Newsletter with your Email ID to keep you updated on latest posts. We will send newsletter to your registered email address. We will not share your email address to anybody as we respect privacy.
Announcements,Big Data,Serverless
Stay tuned to my blog, twitter or facebook to read more articles, tutorials, news, tips & tricks on various technology fields. Also Subscribe to our Newsletter with your Email ID to keep you updated on latest posts. We will send newsletter to your registered email address. We will not share your email address to anybody as we respect privacy.
This article is related to
Announcements,Big Data,Serverless
No comments:
Post a Comment