Data Quality
What Is the Best Anomalo Alternative in 2026?
Anomalo's unsupervised ML is the real thing. Ask what it costs, what it explains, and whether you have the history to feed it.
What Is the Best Great Expectations Alternative in 2026?
Great Expectations is free to install and expensive to maintain. What the alternatives change, and how to port your suites without losing them.
How to Put a Data Quality Agent on Your Snowflake or Databricks Warehouse
Setting up a data quality agent on Snowflake or Databricks takes one connection, not a test-writing project. Connect the warehouse, let the agent profile your tables and build its knowledge base, and it starts monitoring and investigating on its own. Here's the walkthrough.
Data Quality Agents Compared: Monte Carlo, Acceldata, and the Agent-Native Approach
Monte Carlo, Acceldata, and Datadog's Metaplane all ship data quality agents now. Most are a language model bolted onto a legacy dashboard. Here's an honest comparison of the agent approaches and where an agent-native tool fits.
What Is a Data Quality Agent?
A data quality agent is software that watches your warehouse, investigates problems, and explains them on its own, instead of making you check a dashboard. Here's what that means, how it differs from rule-based monitoring, and why the category showed up in 2026.
OKF vs RAG for Data Warehouse Context: Why We Chose a Living Knowledge Base
Google's Open Knowledge Format (OKF) formalizes the "LLM Wiki" pattern: give an agent a curated markdown knowledge base instead of RAG over raw chunks. We've run a variation of that for warehouse context since 2025. Here's why a living knowledge base beats RAG, and where RAG still wins.
Citations for Your Data Incidents: Introducing EvidenceCapsule
When an AI agent answers "why is this table stale?" the hardest question is whether you can trust the answer. EvidenceCapsule is a typed investigation output where every claim cites the row it came from, so you can click through and verify.
How AI-Native Data Observability Changes Incident Response
Most data observability tools shorten the time to know something broke. AI-native observability shortens the time to know what broke, what depends on it, and what to do next. Here is what that workflow actually looks like during a real incident.
How Much Does Data Observability Cost in 2026?
Data observability costs range from $0 open-source (you pay in engineering time) to five-figure enterprise contracts. This guide breaks down every pricing model, the hidden costs nobody quotes, and a total-cost formula you can run on your own warehouse before any sales call.
How to Catch Silent dbt Test Failures Before They Hit Dashboards
dbt tests fail silently more often than they fail loudly. Warn-severity tests, skipped models, freshness gaps, and tests that only run in CI all let bad data through without paging anyone. This guide covers the patterns that hide failures and how to catch them.
What Is the Best Bigeye Alternative in 2026?
Bigeye is an enterprise data observability platform priced for enterprise procurement. An honest comparison with AnomalyArmor: what Bigeye does well, where its pricing and sales model is a mismatch for mid-market teams, and when each tool is the right call.
What Is the Best Monte Carlo Alternative in 2026?
Monte Carlo is the enterprise standard, with enterprise pricing to match. In March 2026 it cut 30% of staff. An honest comparison with AnomalyArmor: what Monte Carlo actually costs, what the restructuring means for buyers, and when each tool is the right call.
A Data Observability Tool That Works From Inside Your AI Assistant
Cold outreach converts at 0% when prospects must hand warehouse credentials to a stranger's SaaS. We shipped a Claude Code plugin that runs against a real demo warehouse with no signup, API key, or credentials. How we built it on existing infra, and what we chose not to build.