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Adventures in DevOps, Will Button, and Warren Parad द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Adventures in DevOps, Will Button, and Warren Parad या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal
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How to build in Observability at Petabyte Scale

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Manage episode 504857106 series 2529949
Adventures in DevOps, Will Button, and Warren Parad द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Adventures in DevOps, Will Button, and Warren Parad या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

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We welcome guest Ang Li and dive into the immense challenge of observability at scale, where some customers are generating petabytes of data per day. Ang explains that instead of building a database from scratch—a decision he says went "against all the instincts" of a founding engineer—Observe chose to build its platform on top of Snowflake, leveraging its separation of compute and storage on EC2 and S3.

The discussion delves into the technical stack and architectural decisions, including the use of Kafka to absorb large bursts of incoming customer data and smooth it out for Snowflake's batch-based engine. Ang notes this choice was also strategic for avoiding tight coupling with a single cloud provider like AWS Kinesis, which would hinder future multi-cloud deployments on GCP or Azure. The discussion also covers their unique pricing model, which avoids surprising customers with high bills by providing a lower cost for data ingestion and then using a usage-based model for queries. This is contrasted with Warren's experience with his company's user-based pricing, which can lead to negative customer experiences when limits are exceeded.

The episode also explores Observe's "love-hate relationship" with Snowflake, as Observe's usage accounts for over 2% of Snowflake's compute, which has helped them discover a lot of bugs but also caused sleepless nights for Snowflake's on-call engineers. Ang discusses hedging their bets for the future by leveraging open data formats like Iceberg, which can be stored directly in customer S3 buckets to enable true data ownership and portability. The episode concludes with a deep dive into the security challenges of providing multi-account access to customer data using IAM trust policies, and a look at the personal picks from the hosts.

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297 एपिसोडस

Artwork
iconसाझा करें
 
Manage episode 504857106 series 2529949
Adventures in DevOps, Will Button, and Warren Parad द्वारा प्रदान की गई सामग्री. एपिसोड, ग्राफिक्स और पॉडकास्ट विवरण सहित सभी पॉडकास्ट सामग्री Adventures in DevOps, Will Button, and Warren Parad या उनके पॉडकास्ट प्लेटफ़ॉर्म पार्टनर द्वारा सीधे अपलोड और प्रदान की जाती है। यदि आपको लगता है कि कोई आपकी अनुमति के बिना आपके कॉपीराइट किए गए कार्य का उपयोग कर रहा है, तो आप यहां बताई गई प्रक्रिया का पालन कर सकते हैं https://hi.player.fm/legal

Share Episode
We welcome guest Ang Li and dive into the immense challenge of observability at scale, where some customers are generating petabytes of data per day. Ang explains that instead of building a database from scratch—a decision he says went "against all the instincts" of a founding engineer—Observe chose to build its platform on top of Snowflake, leveraging its separation of compute and storage on EC2 and S3.

The discussion delves into the technical stack and architectural decisions, including the use of Kafka to absorb large bursts of incoming customer data and smooth it out for Snowflake's batch-based engine. Ang notes this choice was also strategic for avoiding tight coupling with a single cloud provider like AWS Kinesis, which would hinder future multi-cloud deployments on GCP or Azure. The discussion also covers their unique pricing model, which avoids surprising customers with high bills by providing a lower cost for data ingestion and then using a usage-based model for queries. This is contrasted with Warren's experience with his company's user-based pricing, which can lead to negative customer experiences when limits are exceeded.

The episode also explores Observe's "love-hate relationship" with Snowflake, as Observe's usage accounts for over 2% of Snowflake's compute, which has helped them discover a lot of bugs but also caused sleepless nights for Snowflake's on-call engineers. Ang discusses hedging their bets for the future by leveraging open data formats like Iceberg, which can be stored directly in customer S3 buckets to enable true data ownership and portability. The episode concludes with a deep dive into the security challenges of providing multi-account access to customer data using IAM trust policies, and a look at the personal picks from the hosts.

💡 Notable Links:
🎯 Picks:
  continue reading

297 एपिसोडस

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