Define Story and Audience Data visualization and banking data collide when your goal is persuasion, not decoration. Right up front, decide whether your visualization exists to inform a daily operator, to convince an executive, or to trigger a compliance workflow—because that choice drives how you aggregate, filter, and present numbers.

Privacy-first goals and use cases Building on this foundation, our design starts from a simple premise: keep user speech and derived data on the device unless the user explicitly consents to sharing. Privacy-first and on-device should be front-loaded into every architectural decision because these constraints change trade-offs for latency, model

Define objectives and threat model Building on this foundation, start by stating concrete goals for the Agentic AI you plan to build: what capabilities it must deliver, what behaviors are strictly disallowed, and which operational constraints will govern runtime. Front-load measurable targets so those goals are actionable—latency SLOs, acceptable PII

Choose database type and engine Building on this foundation, the first technical decision you make—picking a database type and database engine—shapes schema flexibility, operational complexity, and cost for months or years. Choose carefully: the wrong database type locks you into query patterns that are expensive to change, and the wrong

Dataset and schema overview Building on this foundation, the first step is to make the shape of your movie industry data explicit so you can ask performant, correct questions with SQL. We start by inventorying entities you’ll query most often — titles, people, releases, box office, ratings, and distribution —

Why Robotic Tone Matters Robotic tone undermines trust fast, and in developer-facing content that loss is costly. When ChatGPT or your documentation reads like a checklist with no human context, readers hesitate to follow instructions, ask clarifying questions, or adopt your tooling. Building on this foundation, we need to treat

Why operational metadata matters How do you detect a latent schema change, attribute a spike in downstream errors to a specific ETL job, and automate the safe backfill—all before a customer files a ticket? That problem is exactly why operational metadata belongs at the center of modern data engineering. By

What Is a Vector Database Building on this foundation, imagine searching by meaning instead of exact text matches — that’s the core purpose of a vector database and why vector search has become central to modern ML-driven applications. A vector database stores numeric vectors (dense arrays of floating-point numbers) that

Define production requirements and metrics Defining clear production requirements and metrics up front prevents the familiar trap of shipping a model that performs well in the lab but fails under real-world constraints. Start by translating business outcomes into measurable production requirements: what must the system guarantee for users, what costs