Problem Overview and Scope Deanonymization of user data enabled by large language models creates a privacy risk that demands concrete technical framing before we design defenses. Deanonymization, or re-identification, is the process of linking anonymized records back to real identities; large language models (LLMs) change the calculus because they can

Overview: LLM Threat Landscape Building on this foundation, the LLM threat landscape demands that you treat large language models as complex, multi-surface systems rather than isolated algorithms. In the first pass we often think about model accuracy and latency, but security expands that view to confidentiality, integrity, availability, and misuse.

B-Tree Structure Building on the foundation we set earlier about indexes and page layouts, we’ll examine how a B-tree organizes keys and pointers to optimize disk-based lookup and range scans. A B-tree (balanced tree) stores many keys per node so that tree height stays small even for very large data

Load and inspect time series Building on this foundation, the first practical step is getting your data into a well-structured DataFrame so you can reason about trends, seasonality, and noise. Start by treating the timestamp column as the primary axis for your metrics: convert it to a timezone-aware datetime index

Define churn and business goals Building on this foundation, we need a concrete, actionable definition of customer churn before we design models or interventions. Customer churn refers to the loss of a banking relationship that materially reduces future revenue or engagement—this includes account closures, dormancy beyond a business-defined window, and

Why reliability matters for LLMs When an LLM request fails at 99th-percentile latency or returns a hallucinated answer, the impact goes beyond a single user annoyance — it damages trust, increases operational cost, and creates regulatory risk. How do you ensure an LLM-based feature doesn’t become the single point of

Why SQL for Data Engineering When you’re preparing for technical interviews, the sharpest tool in your kit is often a clear, fast query written in SQL that solves a real business problem. Recruiters and hiring managers for data engineering roles expect you to move beyond toy examples and demonstrate fluency

Overview and Objectives Building on this foundation, we frame why a production-ready evaluation system matters for modern LLM development: experiments without rigorous tracking are noisy, non-reproducible, and hard to scale. Native LLM Evaluation needs to live where your data and compute already are, so you can compare model outputs against

Map funnel steps and events Building on this foundation, start by treating the conversion funnel as a precise map of user intent: identify the discrete funnel steps you care about, then agree on the exact events that signal each step. Conversion funnel and funnel steps should appear immediately in your