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Navigating Kafka: Challenges, Solutions, And The Future Of Real-Time Data Streaming

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Manage episode 431442547 series 3575842
Innhold levert av Aiven. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Aiven eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

The complexities of real-time data streaming and auto-scaling in the cloud are no joke. But three are solutions to make it more simple and efficient.

In this Data (R)evolution episode, Matan Mizrahi and Filip Yonov join us to discuss how Kafka effectively handles and optimizes data streams, the pursuit of standardized APIs, and the vital role of AI in optimizing complex systems. They also share how AI and open-source tools are transforming the landscape. Tune in to hear about the evolution of data streaming and the transformative impact of AI and cloud-native solutions.

Key Takeaways:

  1. Emphasize the need for customer-defined scaling logic based on metrics and auto-scaling events for efficient Kafka management.
  2. The importance of integrating AI, like the Ivan AI database optimizer, to enhance workload optimization and complex system management in Kafka.
  3. The significance of strategic partner relationships in providing advanced software solutions and necessary knowledge and experience to mitigate risk in critical data infrastructure projects.

Resources:

Timestamps:

05:13 Challenges in running and optimizing streaming systems

09:10 Using stream data to produce and consume

12:22 Product manager solves customer problems with new capabilities

16:10 Agreement on system complexity and standard API

18:22 Kafka not yet fully cloud native

23:30 Simplifying cloud setup through productization and self-service

26:17 Hope for AI integration with Kafka, improved management

28:24 AI optimizations, reduce customer pain, lower barriers

32:05 Matan emphasized importance of experience and knowledge

  continue reading

11 episoder

Artwork
iconDel
 
Manage episode 431442547 series 3575842
Innhold levert av Aiven. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Aiven eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

The complexities of real-time data streaming and auto-scaling in the cloud are no joke. But three are solutions to make it more simple and efficient.

In this Data (R)evolution episode, Matan Mizrahi and Filip Yonov join us to discuss how Kafka effectively handles and optimizes data streams, the pursuit of standardized APIs, and the vital role of AI in optimizing complex systems. They also share how AI and open-source tools are transforming the landscape. Tune in to hear about the evolution of data streaming and the transformative impact of AI and cloud-native solutions.

Key Takeaways:

  1. Emphasize the need for customer-defined scaling logic based on metrics and auto-scaling events for efficient Kafka management.
  2. The importance of integrating AI, like the Ivan AI database optimizer, to enhance workload optimization and complex system management in Kafka.
  3. The significance of strategic partner relationships in providing advanced software solutions and necessary knowledge and experience to mitigate risk in critical data infrastructure projects.

Resources:

Timestamps:

05:13 Challenges in running and optimizing streaming systems

09:10 Using stream data to produce and consume

12:22 Product manager solves customer problems with new capabilities

16:10 Agreement on system complexity and standard API

18:22 Kafka not yet fully cloud native

23:30 Simplifying cloud setup through productization and self-service

26:17 Hope for AI integration with Kafka, improved management

28:24 AI optimizations, reduce customer pain, lower barriers

32:05 Matan emphasized importance of experience and knowledge

  continue reading

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