Elevating Kafka: Driving operational excellence with Albertsons + Forrester | Watch Webinar

Online Talk

How to Build a Streaming Data Mesh with Confluent

์ง€๊ธˆ ์‹œ์ฒญํ•˜๊ธฐ

Available On-demand

Ready to turn data mess into a data mesh? Much has been shared on the fundamentals of data mesh, but how do you go about adopting a data mesh as a practitioner? And leverage streaming data pipelines across your architecture?

Weโ€™ll show you how to implement the four key principles of data mesh with Confluent and make the organizational and technical changes youโ€™re going to need to achieve higher data feature velocity and accelerated data value capture.

You can expect to take away:

  • How to use Confluent connectors, stream processing, and Stream Governance to implement a successful data mesh.
  • How to turn raw real-time data into high-quality data products, and make them easily discoverable and accessible across your organization.
  • How to create organizational best practices and a Data Streaming Center of Excellence.
  • How to create a winning strategy to gain broad adoption of your streaming data mesh.

Join us in this implementation webinarโ€”with Q&Aโ€”as we show you how to best put principles of data mesh into practice.

In his role, Travis is a Data Streaming Advisor at Confluent who helps enterprises of all sizes get their data in motion in a data mesh. He guides them through every stage of the process, evolving their organizations, their practices, their architecture, and their technology.

์ถ”๊ฐ€ ๋ฆฌ์†Œ์Šค

cc demo

Confluent Cloud ๋ฐ๋ชจ

Apache Kafka์—์„œ ์ œ๊ณตํ•˜๋Š” ์—…๊ณ„ ์œ ์ผ์˜ ์™„์ „ ๊ด€๋ฆฌํ˜• ํด๋ผ์šฐ๋“œ ๋„ค์ดํ‹ฐ๋ธŒ ์ด๋ฒคํŠธ ์ŠคํŠธ๋ฆฌ๋ฐ ํ”Œ๋žซํผ์ธ Confluent Cloud์˜ ๋ผ์ด๋ธŒ ๋ฐ๋ชจ์— ์ฐธ์—ฌํ•˜์‹ญ์‹œ์˜ค
kafka microservices

Kafka ๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค

๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค ์•„ํ‚คํ…์ฒ˜์— ๊ฐ•๋ ฅํ•œ ์‹ค์‹œ๊ฐ„ ์ŠคํŠธ๋ฆผ ๊ธฐ๋Šฅ์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์ฃผ์š” ๊ฐœ๋…, ์‚ฌ์šฉ ์‚ฌ๋ก€ ๋ฐ ๋ชจ๋ฒ” ์‚ฌ๋ก€๋ฅผ ํ™•์ธํ•˜์‹ญ์‹œ์˜ค.
Image-Event-Driven Microservices-01

e-book: ๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค ๊ณ ๊ฐ ์‚ฌ๋ก€

๋‹ค์–‘ํ•œ ์‚ฐ์—… ๋ถ€๋ฌธ์˜ 5๊ฐœ ์กฐ์ง์ด Confluent๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์–ด๋–ป๊ฒŒ ์ƒˆ๋กœ์šด ์ฐจ์›์˜ ์ด๋ฒคํŠธ ๊ธฐ๋ฐ˜ ๋งˆ์ดํฌ๋กœ์„œ๋น„์Šค๋ฅผ ๊ตฌ์ถ•ํ–ˆ๋Š”์ง€ ํ™•์ธํ•ด ๋ณด์‹ญ์‹œ์˜ค.