“I have worked with Joe for over a decade and half in variour capacities - as a collaborator, as a peer, and as his manager -- and have thoroughly enjoyed working with him in all capacities. Joe possesses deep technical knowledge in databases, has the customer pulse, and excellent inter-personal skills to navigate and communicate with a wide spectrum of audience - from strategic to technical. Joe has demonstrated flexibility and achieved success in diverse roles - from a field engineer to a product manager, from an individual contributor to a leader of product managers. I was responsible for hiring him into Microsoft for his second stint with the company, and won't blink an eyelid to do it another time, if the opportunity arises.”
Experience
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Publications
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Vector Search Essentials
SQLskills
See publicationThis online course provides a comprehensive introduction to vector search capabilities in SQL Server 2025. Designed for SQL Server DBAs and developers, you’ll learn why traditional search methods fall short for semantic queries and how vector search solves these challenges. Through detailed explanations and demonstrations using a property search scenario, you’ll gain the foundational knowledge needed to evaluate and implement vector search in your own environments.
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Optimizing Your Query Plans with the SQL Server 2014 Cardinality Estimator
Microsoft
See publicationSQL Server 2014 introduces the first major redesign of the SQL Server Query Optimizer cardinality estimation process since version 7.0. The goal for the redesign was to improve accuracy, consistency and supportability of key areas within the cardinality estimation process, ultimately affecting average query execution plan quality and associated workload performance. This paper provides an overview of the primary changes made to the cardinality estimator functionality by the Microsoft query…
SQL Server 2014 introduces the first major redesign of the SQL Server Query Optimizer cardinality estimation process since version 7.0. The goal for the redesign was to improve accuracy, consistency and supportability of key areas within the cardinality estimation process, ultimately affecting average query execution plan quality and associated workload performance. This paper provides an overview of the primary changes made to the cardinality estimator functionality by the Microsoft query processor team, covering how to enable and disable the new cardinality estimator behavior, and showing how to troubleshoot plan-quality regressions if and when they occur.
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SQL Server: Transact-SQL Basic Data Retrieval
Pluralsight
See publicationLearn how to construct SELECT statements to retrieve data from SQL Server databases, applicable for developers from SQL Server 2005 onwards.
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SQL Server 2005 T-SQL Recipes
Apress
See publicationNeed to brush up on specific SQL Server tasks, procedures, or Transact-SQL commands? Not finding what you need from SQL Server books online? Or perhaps you just want to familiarize yourself with the practical application of new T-SQL related features. SQL Server 2005 T-SQL Recipes: A Problem-Solution Approach is an ideal book, whatever your level as a DBA or developer.
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SQL Server 2000 Fast Answers for DBAs and Developers, Signature Edition
Apress
See publicationSQL Server 2000 is the leading relational database for the Windows platform. It’s a full-featured, enterprise-class database server, but its ease of use and maintenance makes it suitable whether youre a junior, part-time, or advanced DBA or developer. SQL Server expert and author Joseph Sack covers all SQL Server 2000 features, so you’ll be able to rely on this book when you’re in the field and need quick, effective solutions.
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SQL Server 2008 Transact-SQL Recipes
Apress
See publicationSQL Server 2008 Transact–SQL Recipes: A Problem–Solution Approach is an example–based guide to the Transact–SQL language that is at the core of SQL Server 2008. Learn to create databases, insert and update data, generate reports, secure your data, and more. Author Joseph Sack takes common Transact–SQL tasks and breaks them down into a problem/solution format that is quick and easy to read so that you can get the job done fast when the pressure is on.
Patents
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System and method for cardinality estimation feedback loops in query processing
Issued 11334538
Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed…
Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed. The feedback optimizer utilizes analysis results to generate change recommendations as feedback for later executions of the queries, or similar queries, performed by a query optimizer of the query host. The query host stores change recommendations, and subsequent queries are monitored for the same or similar queries to which change recommendations are applied to query plans for execution and observance by the query monitors. Change recommendations are optionally viewed and selected via a user interface.
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Seisiun Analytics
Zero-ETL is gaslighting data teams. 🤯 The hype cycle peaked this month. Vendors are screaming "No pipelines! Just insights!" It sounds magical. Like losing weight by eating pizza. 🍕 But if you look under the hood, the reality is messy. Here is the breakdown of the "Zero-ETL" myth: 1. SPEED vs. QUALITY. Yes, point-to-point replication is fast. ⚡ But without a transformation layer, you aren't democratizing data. You are democratizing chaos. "Garbage in, lightspeed garbage out" is not a strategy. 2. THE GOVERNANCE BLACK HOLE. Marketing says: "Seamless integration." Engineers hear: "No schema enforcement." 🤡 Try explaining a raw, untransformed NoSQL dump to a GDPR auditor. They won’t care about your "agility." They care about the fine. 3. THE HIDDEN TAX. You aren't eliminating the work. You are just shifting the technical debt. Instead of cleaning data before it loads, you force analysts to write 500-line SQL queries to clean it at runtime. You save on engineering headcount but blow up your compute bill. 💸 The uncomfortable truth? Zero-ETL is brilliant for operational analytics (checking inventory, logs). But for historical compliance and a Single Source of Truth? It is a disaster waiting to happen. We don't need "Zero" ETL. We need Smart ETL. Is this actual innovation, or just a way for cloud vendors to burn more query credits? ✅ Found this useful? Reshare to save a Data Engineer's sanity. 😆 . . . #DataEngineering #ZeroETL #BigData #TechTrends #Architecture #AI #Datavault #DatavaultBuilder #DataIntegration #Seisiun #Analytics
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C1M
"We're making strategic decisions based on assumptions. CARTA replaces assumptions with observation." Quote from: CARTA: Building the Blueprint for an AI-Ready Workforce How to Capture and Reflect Human Workflows to Architect the Future of Work By John Arnott II Get it here: https://a.co/d/03hdlu0G
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ZapSight
Last quarter, we watched a major retailer scrap their $2M AI pilot. The model worked perfectly. The context didn't. This is what we're seeing everywhere: Great intelligence, terrible plumbing. No data layer. No compliance guardrails. No actual workflow integration. It's why we stopped building "magic boxes" and started building the Operations Layer—agents that actually plug into how your team works, not another dashboard to check. Real numbers from real production: ⚡ 80% faster claims processing 📉 30% reduction in stockouts 🛡️ 99.9% uptime If you're currently stuck between 'cool prototype' and 'production reality,' we want to hear where the friction is. Pavan Sathiraju Agrimika Joshi #MLOps #AIPilots #DataEngineering #retail #USA
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O2O
Stop burning field hours on data cleanup and start turning those hours into shelf space. Erik Fonseca and Tom Blackburn of Turff Analytics unpack how freeing reps from spreadsheets, tightening route planning, and shipping assets directly to stores can boost daily coverage and keep data clean for smarter decisions. The chat runs longer than our usual clips, but every minute is packed with practical ways to stretch your sales budget and strengthen retailer relationships. Set aside a little extra time and watch the full interview to bring these insights to your next campaign.
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Shift AI
This week's Shift Brief: Why 2026 is the year AI moves from adoption metrics to actual ROI. The teams seeing results aren't chasing tools. They're starting with workflow friction, measuring outcomes over usage, and shipping narrow deployments that work. Read it here: https://lnkd.in/eKwjYYCV
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iPullRank
AI search needs better metrics, not vibes. This upcoming webinar goes deep on how to design AI Search metrics that actually support experimentation, led by Patrick Schofield, Lead Relevance Engineer at iPullRank, with Garrett Sussman. Patrick will walk through the technical side of AI-aligned measurement: how to define semantic and content metrics, analyze how they behave across head, mid-tail, and long-tail queries, and turn those patterns into testable hypotheses. The session is grounded in a real internal iPullRank case study, showing how choices around content structure, entity focus, and conceptual depth materially change what you learn, and what you can act on. We're tired of abstract AI “scores” and want frameworks we can use to: 🔺 interpret how systems understand content 🔺 identify patterns worth testing 🔺 design controlled SEO experiments 🔺 adapt templates and editorial guidance by query type 👉 Register now: https://lnkd.in/edSGWev7
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ThoughtSpot
This isn’t just a demo—it’s a hands-on tutorial on how to embed live, interactive analytics directly into your product using the next-Gen ThoughtSpot Embedded. 🎤 Join Nicolas Rentz as he shows how product teams are ditching rigid BI for fully branded, boundaryless intelligence that fits right into their user flow. 📺 Watch now the full product unveil and demo now 👉 https://lnkd.in/eRAAyRsv
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