Data Engineering - AnomalyArmor (Page 2)

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.

Data Quality

What Is Data Downtime and How Do You Measure It?

Data downtime is the total time your data is missing, inaccurate, or otherwise unusable. This guide explains how to calculate it using TTD and TTR, estimate the dollar cost per hour, reduce it with automated monitoring, and benchmark your team against industry norms.

Data Quality

The 6 Dimensions of Data Quality: Definitions, Examples, and How to Monitor Each

The six dimensions of data quality are accuracy, completeness, consistency, timeliness, validity, and uniqueness. This guide defines each dimension with SQL examples and shows how to monitor all six in production data pipelines.

Data Quality

You Don't Need to Write Data Tests

Data engineers know they should test their pipelines. They just don't have time. The real answer isn't better testing frameworks. It's automated data testing that works without writing a single test.

Data Observability

Data Pipeline Monitoring: How to Stop Silent Failures Before They Hit Production

Data pipeline monitoring catches silent failures that orchestration tools miss. Learn the 5 types of pipeline failures, why Airflow alerts aren't enough, and how to set up monitoring that actually prevents broken dashboards.

Data Observability

Data Observability vs Data Quality: What's the Difference and Do You Need Both?

Data observability monitors pipeline health (freshness, volume, schema changes). Data quality validates the data itself (accuracy, completeness, consistency). Most teams need both.

Data Freshness

Data Freshness Monitoring: How to Detect Stale Data Before It Breaks Dashboards

Data freshness monitoring checks whether your tables are updating on schedule and alerts you when they stop. Learn how to set freshness SLAs, why orchestration alerts aren't enough, and why DIY monitoring breaks down at scale.

Data Observability

Data Anomaly Detection: The Complete Guide for Data Engineers

Data anomaly detection identifies data points that deviate from expected behavior. This guide covers the four types of anomalies, detection algorithms, and how to implement them in Snowflake, Databricks, and PostgreSQL.

Data Engineering

State of Data Engineering 2026

The 2026 State of Data Engineering Survey found that data teams spend 34% of their time on data quality and 26% firefighting. That's 60% of the week reacting to problems. The numbers reveal a vicious cycle: pressure to move fast creates the fires that slow teams down.

Open Source

Why We Open-Sourced Our Database Query Layer

Most data quality tools ask you to trust them with database access. AnomalyArmor's Query Security Gateway is open source, so you can verify exactly what queries we run. Choose from three access levels, Schema Only, Aggregates, or Full, and audit every query attempt.