Data Quality
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.
Schema Drift
Data Observability
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.
Data Observability
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.
Data Quality
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.
Data Quality
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.
Data Quality
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.
Data Quality
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.
Data Observability
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.
Data Quality
What Is the Best Metaplane Alternative in 2026?
Metaplane is now Metaplane by Datadog. If you picked it to stay off an enterprise observability platform, the acquisition changes the math. An honest comparison with AnomalyArmor: pricing, features, the Datadog question, and when each is the right call.
Data Quality
Why Do Data Teams Use AI to Write Code but Not to Monitor Pipelines?
dbt's 2026 State of Analytics Engineering found that 72% of data teams prioritize AI-assisted coding, but only 24% prioritize AI-assisted pipeline management. That 48-point gap is the reliability problem nobody's talking about: AI is writing the pipelines but not keeping them alive.
Data Quality
What Tools Should I Use for Data Observability in 2026?
The best data observability tools in 2026 are AnomalyArmor, Monte Carlo, Metaplane, Bigeye, Soda, Datafold, Great Expectations, Elementary, Atlan, and DataHub. This guide compares pricing, features, and trade-offs to help you choose.
Data Quality
How Do I Monitor Schema Changes in a Data Warehouse?
Schema changes in a data warehouse are monitored by polling INFORMATION_SCHEMA, using event-driven triggers, or comparing column hashes. This guide covers detection methods with SQL examples for Snowflake, Databricks, and PostgreSQL, plus a comparison of automated tools.