“I worked with Jason for 9 years at CNET/CBS Interactive with several years on the same team. Jason is a solid, versatile and inventive senior engineer with deep systems knowledge on Linux and Windows and strong data architecture skills on MySQL, SQL Server and NoSQL. A great team player, he was always ready to help, and with his hands-on attitude and ability to keep up-to-date with both new and in-use technologies, he was often our go-to person for systems and database questions. If I ever start a company, Jason would be my first call as the "data guy".”
Licenses & Certifications
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MongoDB for DBAs
10gen
Issued -
MongoDB for Developers
10gen
Issued -
Certified MySQL 5.0 Developer
Sun Microsystem Inc.
IssuedCredential ID 230012013 -
Certified MySQL 5.1 Cluster Administrator
Sun Microsystems Inc
IssuedCredential ID 230012007 -
Certified MySQL 5.0 Database Administrator
Sun Microsystems Inc.
IssuedCredential ID 225037628 -
Certified MySQL 5.1 Database Administrator
Sun Microsystem Inc.
IssuedCredential ID 229976855 -
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Marc Brooker
People often ask me about the differences in architecture between Amazon Dynamo (the 2007 SOSP paper), DynamoDB (the AWS serverless NoSQL database), and Aurora DSQL (the AWS serverless SQL databases). I captured the key points on durability, high availability, programming model, and trade-offs in my blog: https://lnkd.in/gkMG_tVD TL;DR: Despite the shared name, Dynamo and DynamoDB have little in common architecturally, and make very different trade-off decisions.
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Dani Mitchell
For those starting out with Bedrock AgentCore Observability or who have heard the name but not looked too much into it, here is a high level summary I created to figure it out myself! 🧠 AgentCore Observability lets you trace, debug, and monitor AI agents' performance in production with detailed visualizations of each step in your agent workflow! Here's what you get out of the box: Built-in monitoring: 🤖 Agent Runtime: Key metrics (session count, latency, duration, token usage, error rates) flow automatically to specialized CloudWatch Gen AI dashboards 🔧 Gateway & Memory & Built-in tools: Default metrics/logs generated where applicable, plus spans for Memory when enabled. 🤖 Bring AgentCore Observability to your self hosted agents also! Data flows intelligently: 📈 CloudWatch Gen AI Dashboard: Agent runtime gets purpose-built trace visualizations, span metrics, and error breakdowns 📈 Standard CloudWatch: All metrics, spans, and logs stored here - accessible via console, CLI, or SDKs 📈 Third-Party Integration: OTEL-compatible format means easy integration with your existing monitoring stack 📈 Flexible Storage: Route logs to S3 or Data Firehose as needed (Memory & Gateway) The magic? You get comprehensive observability across your entire AI ecosystem - from high-level agent performance to granular workflow steps - all flowing to the right dashboards automatically! 🪄 As always, I try to do my best to capture many details from a high level view but you can see it action from my awesome colleagues Amanda Lester and Ishan Singh in this short video! --> https://lnkd.in/d-c_uZvA 🚀 Bonus:🚀 Here is the dive deep blog! https://lnkd.in/dk2ekhmt #AWS #AgentCore #AgenticAI #AgentObservability #AgentCoreGateway
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Gunnar Morling
"Build a Data Lakehouse with Apache Iceberg, Polaris, Trino & MinIO" Enjoyed reading this two-part blog post series by Gilles Philippart, teaching how to run a Data Lakehouse locally, using open-source technologies: - MinIO for object storage - Apache Iceberg as the open table format - Apache Polaris as the metadata catalog - Trino as the query engine. The second part adds Apache Kafka and Flink for streaming data ingest. I also learned about JR, a CLI utility for creating randomized Kafka test data. Everything running locally on your machine via Docker Compose, which is great for learning and experimenting. Really neat, definitely recommend to check it out! 👉 https://lnkd.in/e73fSnPS
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