Show: Snowflake Summit · Publisher: Snowflake · Host: Snowflake editorial
Episode URL: https://www.youtube.com/watch?v=XwCnOsZMhyI
Publish date: 2026-04-14
Duration: 3098.0s
Default source credibility: MEDIUM — Vendor conference — Snowflake keynotes and product launches are marketing (LOW for claims about own products). Named F500 customer talks with production metrics stay HIGH. Filter aggressively for customer sessions with named speakers and quantified outcomes; skip product demos and partner pitches.
- Data engineers are critical for AI success, enabling organizations to build robust data foundations and streamline complex pipelines.
- Snowflake’s OpenFlow and Horizon Catalog simplify data ingestion and governance, reducing manual tasks and improving data quality.
- Real-world examples from customers like CBOE Global Markets and Indeed demonstrate significant performance and cost improvements with Snowflake solutions.
Extracted quotes
| # | Credibility | Speaker | Org | Timestamp | Topic | Quote |
|---|---|---|---|---|---|---|
| 1 | MEDIUM | CBOE Global Markets (Customer) | CBOE Global Markets | 5:51 | 01-ai-native-landscape | We needed to replace a legacy system that was limited to end-of-day batch processing. But our teams required access to near-real-time market data. With Snowpipe Streaming V2, we’re now able to process more than 100 terabytes of uncompressed data, which comprises more than 190 billion rows every single day. |
| 2 | MEDIUM | Shruy Anand (Product Manager) | Snowflake | 27:15 | 07-adoption-challenges | We were able to create a dynamic table to make sure that my business insight team always had the right information available on a near real-time basis. We also used AI SQL tools like AI redact and AI extract info to make sure that I’m not having any kind of sensitive information in my data as well as I’m extracting and appending any historical and real-time insights to the information and making sure my business team can make the right decisions. |
| 3 | MEDIUM | Lockman Pendicor (Senior Director of the Zealus Data Cloud) | Zeleus | 41:26 | 07-adoption-challenges | We were able to get real time of around 40 data mods and it was so easy of course we worked with the you know snowflake product team openflow product team to you know um you know work you know great for us and with this it was so easy that our engineer just said it’s very difficult to go back to how we were doing it before. |
Per-quote detail
1. CBOE Global Markets — CBOE Global Markets (5:51)
We needed to replace a legacy system that was limited to end-of-day batch processing. But our teams required access to near-real-time market data. With Snowpipe Streaming V2, we’re now able to process more than 100 terabytes of uncompressed data, which comprises more than 190 billion rows every single day.
- Stat: 100 terabytes of uncompressed data, 190 billion rows, daily, measured by CBOE Global Markets.
- Credibility: MEDIUM — Specific metric with denominator, but vendor context.
- Topic tag:
01-ai-native-landscape
2. Shruy Anand — Snowflake (27:15)
We were able to create a dynamic table to make sure that my business insight team always had the right information available on a near real-time basis. We also used AI SQL tools like AI redact and AI extract info to make sure that I’m not having any kind of sensitive information in my data as well as I’m extracting and appending any historical and real-time insights to the information and making sure my business team can make the right decisions.
- Stat: null
- Credibility: MEDIUM — Specific use case with AI SQL tools, but vendor context.
- Topic tag:
07-adoption-challenges
3. Lockman Pendicor — Zeleus (41:26)
We were able to get real time of around 40 data mods and it was so easy of course we worked with the you know snowflake product team openflow product team to you know um you know work you know great for us and with this it was so easy that our engineer just said it’s very difficult to go back to how we were doing it before.
- Stat: null
- Credibility: MEDIUM — Specific use case with OpenFlow, but vendor context.
- Topic tag:
07-adoption-challenges
Extracted 2026-04-14T14:07:45 via scripts/podcast_mine.py (MLX mlx-community/Qwen2.5-32B-Instruct-4bit).