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Transform Your Object Storage Into a Git-like Repository With Paul Singman @ LakeFS
MP3•Laman utama episod
Manage episode 321576032 series 2881014
Kandungan disediakan oleh Travis Lawrence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Travis Lawrence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode we speak with Paul Singman Developer Advocate at Treeverse / LakeFS. LakeFS is an open source project that allows you to transform your object storage into a Git-like repository.
Top 3 takeaways
- LakeFS enables use cases like debugging to quickly view historical versions of your data at a specific point in time and running ML experiments over the same set of data with branching..
- The current data landscape is very fragmented with many tools available.. Over the coming years there will most likely be consolidation of tools that are more open and integrated.
- Data quality and observability continue to be key components of successful data lakes and having visibility into job runs.
43 episod
Transform Your Object Storage Into a Git-like Repository With Paul Singman @ LakeFS
Building the Backend: Data Solutions that Power Leading Organizations
MP3•Laman utama episod
Manage episode 321576032 series 2881014
Kandungan disediakan oleh Travis Lawrence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Travis Lawrence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode we speak with Paul Singman Developer Advocate at Treeverse / LakeFS. LakeFS is an open source project that allows you to transform your object storage into a Git-like repository.
Top 3 takeaways
- LakeFS enables use cases like debugging to quickly view historical versions of your data at a specific point in time and running ML experiments over the same set of data with branching..
- The current data landscape is very fragmented with many tools available.. Over the coming years there will most likely be consolidation of tools that are more open and integrated.
- Data quality and observability continue to be key components of successful data lakes and having visibility into job runs.
43 episod
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