querying
querying on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on querying in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around querying, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Recursive CTEs: SQL’s Hidden Graph Traversal Engine
Unlock the power of SQL for graph-like data manipulation with Recursive Common Table Expressions (CTEs). This practical guide reveals how CTEs function as SQL’s hidden engine for traversing hierarchies, identifying routes, and detecting cycles—capabilities often overlooked. Discover how to calculate degrees of separation and efficiently analyze complex relational structures. For a deeper dive into the nuances of context management within these workflows, explore "AI Agents Don’t Need More Context — They Need Typed Context."
Finding most recent dates in carious columns of date information
Analyzing game data can quickly become complex, even for seasoned Magic: The Gathering players. If you're seeking to identify your most recently played decks from a spreadsheet with multiple date columns, you’re facing a common challenge. Our AI-native spreadsheet technology empowers you to transform this task from daunting to discoverable. To achieve this, explore utilizing advanced sorting and filtering capabilities, enabling you to rank dates across columns and pinpoint your top ten most recent plays.
I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How.
Unlock data insights effortlessly with a new approach to business intelligence. This guide details how to build an AI data agent—a conversational interface empowering users to explore data and answer critical business questions using natural language, bypassing the need for SQL. Discover a streamlined workflow that transforms data access, fostering quicker decision-making. Learn the step-by-step process, and explore how companies like Mirendil are scaling similar AI infrastructure with significant Google Cloud investments.
Pinecone Introduces Nexus Engine for Compiling Business Context into Structured Data for AI Agents
Pinecone Nexus is now generally available, offering a transformative solution for AI agent development. This “knowledge engine” compiles your enterprise data into a structured layer, empowering agents to query business context directly. Teams can now ingest and curate this vital information once, ensuring reusability across agents, reducing token costs, and improving accuracy. Nexus streamlines workflows and unlocks greater AI efficiency. For those interested in the broader research landscape driving these innovations, explore “AI/ML Research - What Does it Really Take?” on our site.