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

Online Talk

How Vimeo Uses Streaming Pipelines to Optimize Real-Time Experiences for 260M+ Users

์ง€๊ธˆ ์‹œ์ฒญํ•˜๊ธฐ

Available On-demand

When it comes to video platforms, user experience is king. Video creators and viewers expect a rich toolset and highly engaging platforms that cater to their needs and interests in real time, with zero buffering or other performance hiccups. Add to this sheer scaleโ€”hundreds of millions of users, a hundred billion viewsโ€”and data pipelines become vital for ingesting and processing real-time data.

To meet high customer expectations, Vimeo needs access to real-time data from numerous systems and apps across their organization. Thatโ€™s where Confluentโ€™s data streaming platform and streaming pipelines come in.

Tune in to hear from Vimeoโ€™s Director of Data Engineering Babak Bashiri and Principal Data Platform Engineer Burton Williams, about how they implemented a streaming data architecture that plays a critical role in driving user growth. Youโ€™ll learn about:

Real-time data warehousing for real-time analytics:

  • From batch ETL with a 1-day delay to building streaming pipelines to a cloud data warehouseโ€”learn how Vimeo unlocked real-time behavioral insights for 260M+ users to adapt and personalize in-product interactions, experiments, and marketing campaigns.

Real-time monitoring and adaptive bitrate streaming:

  • By monitoring real-time data on network bandwidth, latency, and other parameters, Vimeo can optimize the video quality to match the viewer's available resources, ensuring smooth playback even in fluctuating network conditions.

Register now and take home best practices for building streaming pipelines and leveraging real-time data to deliver remarkable customer experiences.

์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.