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EP 43: Stephen Pace from Snowflake
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محتوای ارائه شده توسط Digital Wildcatters. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Digital Wildcatters یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Stephen Pace, Principal Sales Engineer at Snowflake, joins hosts John and Bobby in this episode of the Energy Bytes Podcast to explore how Snowflake's cutting-edge AI tools are transforming data management in the energy sector. With his extensive background, including a journey from Shell to Snowflake, Stephen shares insights into the evolution of Snowflake’s multicloud capabilities and AI tools like Snowpark and Snowpipe Streaming. These innovations are solving scalability and real-time processing challenges in large-scale industrial applications, offering energy companies new ways to enhance data strategies and streamline operations. Stephen also provides a look into Snowflake’s future developments and its continued integration of AI and machine learning. Based in Houston, Stephen’s expertise in data management makes him a key player in helping organizations maximize the potential of Snowflake’s platform.
Digital Wildcatters brings the energy community together through events, cutting-edge content, and powerful tools. Join our online community at collide.io. Engage with experts, level up your career, and ask Collide AI your toughest technical questions.”
Click here to watch a video of this episode.00:00 - Intro
4:09 - What is Snowflake
6:32 - Snowflake's Architecture
9:52 - Snowflake’s Growth
10:32 - Snowflake’s Pricing
12:48 - Snowflake’s Performance Index
15:36 - Snowflake’s Snowpark Optimized Servers
17:26 - Snowflake’s Data Sharing
18:58 - Autosuspend
20:03 - Snowflake’s Managed Service
21:54 - Success Stories
23:58 - Will Snowflake Get into the Vector Database Space
29:43 - How Snowflake is Enabling AI
33:59 - Using Language Models to Extract Data
36:58 - Breaking Down Data Silos
39:18 - Storing Structured and Semi-Structured Data
41:45 - From Easy to Use to Production Worthy
47:04 - Snowpark and Pandas
50:04 - Multi-Compute and Parallel Processing
51:26 - Observability
52:45 - Snowpipe
58:10 - Favorite Use Case
1:01:50 - Customer Feedback
1:03:49 - Native Connectors
1:06:38 - Google
1:07:23 - Lightning Round
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