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Data Engineering

November 1, 2025

10 min read

Great Expectations: The Complete Guide to Ensuring Data Quality in Modern Data Pipelines

In a world where decisions are increasingly **data-driven**, one bad dataset can derail an entire analytics effort or machine learning model. We often focus on **building pipelines** but neglect to ensure that what flows through them --our data-- is actually **trustworthy**. That’s where **Great Expectations (GX)** steps in. > Great Expectations is an open-source framework for validating, documenting, and profiling data to ensure consistency and quality across your data systems. This guide will walk you through **everything you need to know** about Great Expectations -- from fundamental concepts to hands-on examples, all the way to production-grade integrations.

Building an Enterprise-Grade Agentic Analytics Platform

In the age of AI-driven analytics, many organisations are seduced by the idea of “just plug an LLM to your warehouse and ask anything”. Most teams do not pay attention to the massive engineering effort required to make the conversational analytics work in production, at scale and with real enterprise data. To succeed in production, you need more than a chat interface — you need an architecture built to understand semantics, learn from usage, secure retrieval, and enforce governance. In this post we’ll walk you through a blueprint for such a platform, anchored around three key layers: - A Custom Data Understanding Layer that interprets structure, semantics, and business use-cases - A Learning & Retrieval Layer that evolves and retrieves context-aware information - A Secured Retrieval & Execution Stage that ensures safe, performant, governed answers - We’ll also highlight why these capabilities matter, what pitfalls to avoid, and how to build each layer effectively.

Secure & Governed Agentic Analytics with datatoinsights.ai: How to Build Trust at Scale

The shift from dashboards and manual queries to autonomous analytics agents is well underway. But as organisations rush to adopt “agentic analytics” — systems that reason, query, act — they often stumble on a critical dimension: trust, governance and security. Industry research confirms this: for example, the consultancy McKinsey & Company observes that agentic systems “introduce novel internal risks … unless the principles of safety and security are woven in from the outset.” [(McKinsey & Company) ](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders?utm_source=chatgpt.com) At datatoinsights.ai, we’ve built our platform not just for semantic intelligence and business agility (as covered in our previous blogs) but with governance, security and operational guardrails baked-in. This blog explains how we deliver that, and why it matters.

Why Agentic AI Analytics Struggle on Real Production Data & How to Fix It

The promise of **agentic analytics** — AI systems that understand natural language, query data, generate insights, and even take actions — is incredibly powerful. However, as many data leaders will attest, the excitement often fades once these systems meet **real production data**, **real business logic**, and **real users**. As Tellius notes in *“10 Battle Scars from Building Agentic AI Analytics,”* the biggest challenges appear not in demos, but in production environments. One of the most common root causes of failure is **missing semantic awareness** — raw, messy data, vague business definitions, and unclear logic that derail even the smartest models. In this post, we’ll: - Explore **why agentic analytics struggle** in real-world environments - Highlight **key failure modes** seen across the industry - Offer a **practical checklist** for practitioners - Answer SEO-friendly questions like: - *What is agentic analytics?* - *Why is a semantic layer critical?* - *How can organisations succeed in production?*

Traditional BI Is Fading — How datatoinsights.ai Powers Smart, Semantic Analytics on the Go

For years, business intelligence (BI) tools delivered dashboards and reports that helped organisations monitor what happened. But as business environments evolve with faster data, more complexity, and higher expectations — traditional BI is showing its age. Studies now argue that legacy BI isn’t just struggling — in many respects it’s already outdated. [(RTInsights)](https://www.rtinsights.com/traditional-business-intelligence-isnt-dying-its-dead/) In contrast, platforms like datatoinsights.ai are built from the ground up for the demands of today: semantics, conversation, mobility, real-time, and business context. In this post we’ll: - Explore the core limitations of traditional BI - Explain the new demands on analytics in the enterprise - Show how datatoinsights.ai meets those demands - Outline practical steps to transition successfully

Sashank Dulal

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