A data analyst describes how AI tools enabled a nontechnical product manager to build complex, client‐ready dashboards spanning hundreds of data sources. After validating the results and finding few errors, the author realizes that core analytics tasks once thought AI‐proof are now automated.

• A nontechnical PM used an AI tool to build a client-ready dashboard across hundreds of data sources with minimal hallucinations, in minutes instead of weeks.
• The data analyst's role shifted from hands-on querying/dashboard-building to validation, governance, schema design, and writing guardrails.
• Core analytics tasks once assumed automation-proof (writing queries, wrangling data, building visualizations) are now being handled by AI.
• Remaining human value lies in strategy: choosing metrics, designing experiments, prompt design, bias detection, and communicating results.

This TLDR covers Accenture’s $4.2 billion cybersecurity deal to buy Dragos, runZero, and NetRise for OT security, plus the hidden risks of free VPNs and streaming apps turning into residential proxies. It also looks at Cisco’s phased move to cloud SSE, Amazon’s push against human-in-the-loop AI governance, OpenAI’s new spend controls, the launch of enterprise-managed OAuth for MCP, Microsoft’s messy AI rollout, and an internal Copilot-powered analytics agent.

• Accenture is spending $4.2B to buy Dragos, runZero, and NetRise, betting on consolidated OT security as a growth market.
• Infoblox found 65%+ of cloud customers' networks are making DNS calls to residential-proxy domains (500B+ queries/month in 2026), showing free VPNs and streaming apps are quietly turning corporate devices into proxy infrastructure.
• Amazon Security argues human-in-the-loop approval doesn't scale for AI agents and is shifting to identity-based, risk-scored permissions instead of manual checks.
• Microsoft is shipping default-on Copilot features in Windows/Office faster than IT can build governance for them, creating friction, while internal tools like Cisco's SSE migration (18% fewer tickets) and Qubot's natural-language data queries show more controlled rollouts working better.

Rill’s Metrics SQL lets you define business metrics once and query them using plain SQL across dashboards, notebooks, and AI agents. It compiles metric views into optimized OLAP queries, handling grouping, filters, time functions, and security automatically.

• Define a metric once in a YAML-backed metrics view and query it as plain SQL from dashboards, notebooks, and AI agents alike, instead of redefining it in dbt, Looker, Python, etc.
• A parser/compiler/executor pipeline rewrites simple queries like "SELECT country, revenue FROM revenue_metrics" into full OLAP SQL with correct GROUP BY, joins, and filters automatically.
• Security (row-level and column permissions) and SQL-injection-safe parameter binding are enforced consistently at the executor stage for every consumer.
• Works across multiple backends (ClickHouse, Snowflake, DuckDB, Druid) and can leverage existing database optimizations like materialized views and indexes for performance.

This article lists 30 important interview questions for Business Intelligence Engineering roles, focusing on skills relevant in the AI era. It aims to help both candidates and interviewers navigate the evolving landscape of data and analytics.

• The article compiles 30 BI engineering interview questions specifically framed around how AI is reshaping the role, not just traditional BI skills.
• It pushes beyond tool-specific knowledge (SQL, Tableau, Power BI) to test how candidates reason through data governance, integrity, and security issues.
• It frames machine learning familiarity as now a baseline expectation for BI engineers, not a separate specialty.
• It stresses evaluating soft skills like translating technical findings into business insights for cross-functional teams as equally important as technical chops.

Since the inception of SQL in 1974, there has been a recurring dream to replace data analytics developers with tools that simplify the querying process. Each decade has seen innovations that aim to democratize data access, yet the complex intellectual work of understanding business needs and making informed decisions remains essential. Advances like AI can enhance efficiency but do not eliminate the crucial human expertise required in data analytics.

• SQL was designed in 1974 by IBM's Chamberlin and Boyce specifically to let non-programmers query data in plain English, yet this self-service dream has resurfaced every decade (OLAP in the 80s, semantic layers now) without ever eliminating the need for developers.
• AI can now generate SQL and build analytical models faster than ever, but it automates only the mechanical coding, not the judgment calls about which metrics or definitions actually matter.
• The real bottleneck was never syntax or tooling but the intellectual work of understanding business context — a gap AI accelerates around but doesn't close.