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Applying Transformations to Streaming Data with Materialize

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Manage episode 302247320 series 2881014
Konten disediakan oleh Travis Lawrence. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Travis Lawrence atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

This episode features Arjun Narayan Co-Founder & CEO @ Materialize, during our discussion we talk all about transforming streaming data, the do’s the don’ts and how Materialize is changing the landscape of streaming.

Top 3 Value Bombs:

  1. When creating schema changes organizations should always strive to create forward compatible schema changes only. This means consumers will be able to consume your data model without impacting them, they just may be missing your newly added column.
  2. Materialized computations are bound to change in the future, either due to bugs or requirement changes. Kafka allows you to replay all your previous messages to update the calculation.
  3. The cloud is still young, over the coming years we will see many more technologies that are specifically built with a cloud focus.

  continue reading

43 episode

Artwork
iconBagikan
 
Manage episode 302247320 series 2881014
Konten disediakan oleh Travis Lawrence. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Travis Lawrence atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

This episode features Arjun Narayan Co-Founder & CEO @ Materialize, during our discussion we talk all about transforming streaming data, the do’s the don’ts and how Materialize is changing the landscape of streaming.

Top 3 Value Bombs:

  1. When creating schema changes organizations should always strive to create forward compatible schema changes only. This means consumers will be able to consume your data model without impacting them, they just may be missing your newly added column.
  2. Materialized computations are bound to change in the future, either due to bugs or requirement changes. Kafka allows you to replay all your previous messages to update the calculation.
  3. The cloud is still young, over the coming years we will see many more technologies that are specifically built with a cloud focus.

  continue reading

43 episode

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