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Innhold levert av Aaron Francis and Try Hard Studios. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Aaron Francis and Try Hard Studios 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.
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The database for all your AI needs

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Manage episode 506779303 series 3579868
Innhold levert av Aaron Francis and Try Hard Studios. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Aaron Francis and Try Hard Studios 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.

Marcel Kornacker, the creator of Apache Impala and co-creator of Apache Parquet, joins me to talk about his latest project: Pixeltable, a multimodal AI database that combines structured and unstructured data with rich, Python-native workflows.

From ingestion to vector search, transcription to snapshots, Pixeltable eliminates painful data plumbing for modern AI teams.

Follow Marcel

Follow Aaron

Chapters

  • 0:00 – Introduction
  • 0:20 – Meet Marcel Kornacker
  • 1:19 – Early career and grad school in databases
  • 2:12 – Joining Google and building F1
  • 3:42 – How F1 used Spanner at Google
  • 4:01 – Starting Apache Impala at Cloudera
  • 6:02 – Why SQL still matters
  • 7:29 – What keeps Marcel fascinated with databases
  • 9:37 – The “SQL is dead” waves and shift to AI
  • 10:21 – Observing pain points in computer vision pipelines
  • 13:02 – Multimodal data challenges and the idea for Pixeltable
  • 16:10 – How Pixeltable handles transformations with computed columns
  • 26:29 – Example: processing video, audio, and transcripts in Pixeltable
  • 33:12 – DAG execution and parallelism explained
  • 37:00 – Transactional guarantees in Pixeltable
  • 39:00 – Iterators and chunking data for search
  • 42:26 – Using embeddings and semantic search
  • 47:05 – Updating data and incremental recomputation
  • 50:06 – Thoughts on RAG and hybrid search
  • 53:14 – Real-world use cases and dataset curation
  • 57:00 – Example: labeling food waste on cruise ships
  • 1:02:00 – Labeling workflows and syncing annotations
  • 1:02:41 – Pixeltable’s roadmap and cloud vision
  • 1:07:10 – How to get involved with Pixeltable
  • 1:09:03 – Closing and where to find Marcel
  continue reading

28 episoder

Artwork
iconDel
 
Manage episode 506779303 series 3579868
Innhold levert av Aaron Francis and Try Hard Studios. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Aaron Francis and Try Hard Studios 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.

Marcel Kornacker, the creator of Apache Impala and co-creator of Apache Parquet, joins me to talk about his latest project: Pixeltable, a multimodal AI database that combines structured and unstructured data with rich, Python-native workflows.

From ingestion to vector search, transcription to snapshots, Pixeltable eliminates painful data plumbing for modern AI teams.

Follow Marcel

Follow Aaron

Chapters

  • 0:00 – Introduction
  • 0:20 – Meet Marcel Kornacker
  • 1:19 – Early career and grad school in databases
  • 2:12 – Joining Google and building F1
  • 3:42 – How F1 used Spanner at Google
  • 4:01 – Starting Apache Impala at Cloudera
  • 6:02 – Why SQL still matters
  • 7:29 – What keeps Marcel fascinated with databases
  • 9:37 – The “SQL is dead” waves and shift to AI
  • 10:21 – Observing pain points in computer vision pipelines
  • 13:02 – Multimodal data challenges and the idea for Pixeltable
  • 16:10 – How Pixeltable handles transformations with computed columns
  • 26:29 – Example: processing video, audio, and transcripts in Pixeltable
  • 33:12 – DAG execution and parallelism explained
  • 37:00 – Transactional guarantees in Pixeltable
  • 39:00 – Iterators and chunking data for search
  • 42:26 – Using embeddings and semantic search
  • 47:05 – Updating data and incremental recomputation
  • 50:06 – Thoughts on RAG and hybrid search
  • 53:14 – Real-world use cases and dataset curation
  • 57:00 – Example: labeling food waste on cruise ships
  • 1:02:00 – Labeling workflows and syncing annotations
  • 1:02:41 – Pixeltable’s roadmap and cloud vision
  • 1:07:10 – How to get involved with Pixeltable
  • 1:09:03 – Closing and where to find Marcel
  continue reading

28 episoder

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