Jay Kreps

@jaykreps

CEO of (). Co-creator of . Sí se puede.

Mountain View, California
Vrijeme pridruživanja: ožujak 2010.

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  1. Prikvačeni tweet
    20. stu 2019.

    We think this is a big step in making stream processing easier. I'm really excited about it.

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  2. proslijedio/la je Tweet
    17. sij

    This is a wonderful 4-part series by @miguono. The whole thing pivots around the relationship between the stream and the table but it's really a comprehensive guide to understanding the nature of data in flight.

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  3. 15. sij

    A really good primer by on the fundamentals of events vs state, streams vs tables, and and stream processing.

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  4. 13. sij

    Neha has done so much to help build both Apache Kafka and . From the early Kafka development at LinkedIn, to helping found and grow Confluent to where it is today, we wouldn't be where we are without her.

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  5. proslijedio/la je Tweet
    19. pro 2019.

    A good tutorial by with code examples on how to implement a real-time app with , , and .

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  6. proslijedio/la je Tweet
    16. pro 2019.

    “Stream processing” is a term that you might hear a lot, but what exactly does it mean? How would you explain it to a newcomer? And what are its key properties? Time for a quick lesson. 1/

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  7. 12. pro 2019.

    Event streams and drug discovery. Very cool.

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  8. proslijedio/la je Tweet
    11. pro 2019.

    Gwen Shapira on the Power of Event-Driven Architecture

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  10. proslijedio/la je Tweet
    5. pro 2019.

    My talk 🎙️ on the "Future of Data Engineering" is up! I cover the six stages of data pipeline maturity: 0. None 1. Batch 2. Realtime 3. Integration 4. Automation 5. Decentralization Check it out! 👀 (I'm so sorry for the link picture)

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  11. 26. stu 2019.

    Nice edition of ’s famous blog tutorials on , stream processing, and...doughnuts. :-)

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  12. proslijedio/la je Tweet
    20. stu 2019.

    So happy to finally take all the wraps off what my team has been working on at . is the start of a really powerful idea to make a substantial dent in the complexity of building stream processing applications. 1/

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  13. 20. stu 2019.

    This is still early days for this line of work, but we think this is a really promising direction for making stream processing easy and ubiquitous. Check it out, the blog has the full story.

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  14. 20. stu 2019.

    You can control connectors directly from within SQL, and can do real-time lookups against the tables you are materializing in your stream processing queries.

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  15. 20. stu 2019.

    This release of KSQL let's you simplify this significantly by bringing together these different capabilities.

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  16. 20. stu 2019.

    It's enough to make you long for the good old days of CRUD applications, where at least you had just one database that served the basic needs of the app.

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  17. 20. stu 2019.

    The stack is usually something like Kafka, a stream processing layer, a traditional database, various connectors, etc.

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  18. 20. stu 2019.

    The reality is that event streams are becoming ubiquitous in software engineering but the stack supporting stream processing is still a bit too complex. You end up with lots of moving parts and it's up to you to integrate them all.

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  19. proslijedio/la je Tweet
    6. stu 2019.

    We’re making a big change to Sentry today:

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  20. proslijedio/la je Tweet

    Welcome aboard the rocket ship, Erica Schultz! We’re honored you’re joining the team as our President of Field Operations.

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