Blog
Data observability, explained
Guides, the five pillars, and practical playbooks for catching broken data before your stakeholders do.
Datadog Data Observability Pricing: What Jobs Monitoring, Database Monitoring and Data Streams Cost
Datadog prices data observability per host per hour of pipeline compute, from 0.05 dollars. What that meter covers, what it misses, and where Metaplane sits.
Read articleMetaplane Pricing: What Each Tier Costs and Where the Limits Bite
Metaplane publishes tier limits but no dollar figure. What Free, Pro and Enterprise include, and why the custom SQL monitor ceiling stays in single digits.
Read articleTableau Data Management Pricing: What Tableau Catalog and Data Quality Warnings Actually Cost
Tableau stopped selling Data Management as an add-on in September 2024. What that changed, why the meter counts seats rather than tables, and when the upgrade is worth it.
Read articleSnowflake Data Quality Tools for Standard Edition: Best Options and Pricing Without Enterprise
Data metric functions need Enterprise Edition, so Standard accounts are locked out. Six options compared on what they cover, what you build, and what they bill.
Read articleData Quality Monitoring Pricing on BigQuery, Snowflake, Databricks and dbt: What the Native Meters Actually Charge
What native data quality monitoring really costs on five platforms: four of the five meters charge you more for checking more often.
Read articleBest Data Observability Tools for BigQuery: BigQuery Monitoring Tools Compared on Price, Coverage and Alerting
Seven options for monitoring BigQuery compared on how they bill, the native limits that end most native-only plans, and whether alerts reach your on-call.
Read articleBest Data Observability Tools for Databricks: Databricks Monitoring Tools Compared on Price, Coverage and Alerting
Six options for monitoring Databricks compared on how they bill, what the native Unity Catalog checks refuse to watch, and whether alerts reach your on-call.
Read articleBest Data Observability Tools for dbt: dbt Monitoring Tools Compared on Price, Coverage and Alerting
Seven credible options for monitoring a dbt project compared on how they bill, how much YAML each table costs, and whether a failing test reaches on call.
Read articleBest Data Observability Tools for Snowflake: Monitoring Tools Compared on Price, Coverage and Alerting
Seven credible options for monitoring Snowflake compared on how they bill, what each one configures per table, and whether an alert reaches on call.
Read articleData Observability Pricing Models: Per Table vs Per Monitor vs Credit Contracts
The four meters data observability tools bill on, which vendors use each one, the sub-limits that decide your tier, and how to normalize competing quotes.
Read articleData Quality Alerts for Power BI and Tableau: Best Tools for Freshness and Null Rate Monitoring
What Power BI data alerts and Tableau data quality warnings can genuinely catch, the documented limits on each, and where freshness and null rate alerts belong.
Read articledbt Lineage Graph: How to Visualize End-to-End Lineage From Source to Dashboard
The three places a dbt lineage graph comes from, what dbt docs and dbt Catalog each show, the parsing gaps, and how to cover what dbt cannot see.
Read articleData Lineage Vendor Quotes: What Information to Prepare First
Lineage vendors quote by sales call. The four numbers to count first, the seat question that moves price most, and how to normalize quotes across years.
Read articleData Quality Checks in Airflow: SQL Check Operators Explained
How the six Airflow SQL check operators work, with runnable examples, the defaults that quietly let bad data through, and the three limits to plan around.
Read articleGreat Expectations in 2026: What Happened to GX Cloud
GX Cloud was acquired by FICO and pulled from public availability on June 1, 2026. GX Core is Apache-2.0 and stewarded by Fivetran. What that means for buyers.
Read articleAWS Glue Data Quality: DQDL Rules and Anomaly Detection
How AWS Glue Data Quality works: DQDL rules, rule recommendations, analyzers and ML anomaly detection, plus its documented limits.
Read articleData Quality Checks in Databricks: Expectations, DQX and SQL
The working code for Lakeflow expectations, DQX, Unity Catalog anomaly detection, and data profiling, plus the documented limit that decides which one fits.
Read articleHow to Detect Anomalies in Snowflake with SQL
The working SQL for SNOWFLAKE.ML.ANOMALY_DETECTION, why the default flags one percent of every table, how much history it needs, and the limits to design for.
Read articleData Warehouse Incident Detection: The 4 Signals That Work
Most data incidents happen with every pipeline job green. The four signals that actually catch a broken warehouse table, and how fast detection should be.
Read articleColumn Level Lineage in dbt: How to Get It, What It Misses
dbt column level lineage lives in dbt Catalog on Enterprise plans only, and stops at the project boundary. What it covers and the options on dbt Core.
Read articleData Quality Dimensions: The 6 That Matter, With Examples
Completeness, accuracy, consistency, timeliness, validity, uniqueness: what each dimension measures, how to calculate it, and which four you can automate.
Read articleBuild vs Buy Data Observability: The Real Cost of DIY
What an in-house data monitoring stack actually costs: two engineer quarters to parity, ongoing maintenance, and the warehouse compute bill nobody budgets for.
Read articleWhat Is Data Observability? A Plain-English Guide
What data observability is, why it matters, and how the 5 pillars work together to catch broken data before your stakeholders ever see it.
Read articleThe 5 Pillars of Data Observability, With Examples
Freshness, volume, schema, distribution, and lineage: what each pillar of data observability covers, what breaks when it is missing, and how to monitor it.
Read articleData Observability vs Monitoring - What Is the Difference?
Monitoring tells you a known metric crossed a threshold. Data observability tells you why your data broke and what it affects. How they differ and overlap.
Read articleData Downtime - What It Costs and How to Reduce It
Data downtime is the time your data is wrong, missing, or late. Learn how to measure it, why it is expensive, and the practices that bring it down.
Read articleHow to Monitor Data Quality - A Practical Playbook
A step-by-step playbook for data quality monitoring: what to measure, how to set thresholds that do not spam you, and how to route alerts your team will act on.
Read articleWhat Is Data Lineage? Why Every Data Team Needs It
Data lineage is the map of how data flows from source to dashboard. Learn what column-level lineage is, why it matters for incidents, and how it is generated.
Read articleAlert Fatigue in Data Teams - How to Cut the Noise
Alert fatigue is why teams ignore their data alerts. Here is how ML-tuned thresholds, alert grouping, and severity routing turn noise into signal.
Read articleData Observability on Snowflake - A Setup Guide
How to set up data observability on Snowflake without blowing your compute budget: metadata-first monitoring, freshness SLAs, lineage, and low-noise alerts.
Read articleData Contracts: What They Are and How to Implement Them
What a data contract is, what belongs in one, how to enforce it in CI and at ingestion, and why contracts do not replace data observability.
Read articleData Quality Framework: How to Build One in 5 Steps
A data quality framework you can implement in two weeks: the 6 dimensions, how to tier tables, which rules to automate, and how to measure results.
Read articleHow to Choose a Data Observability Tool: A Buyer Checklist
The seven checks that decide whether a data observability tool works on your stack, an honest tool-to-team-profile map, and what to measure during a trial.
Read articleData Observability Architecture: The Layers and a Reference Design
The five layers of a data observability architecture, how lineage is actually derived, and an honest build-versus-buy comparison for data teams.
Read articleData Quality Metrics: The Ones Worth Tracking, With Formulas
The data quality metrics that actually change decisions, how to calculate each one, the traps inside them, and how to build a scorecard people read.
Read articleData Quality for AI: Monitoring the Pipelines Behind RAG and Agents
Stale embeddings, silent parsing regressions, sources that stop syncing: the AI data failures classic BI monitoring misses, and how to catch them.
Read articleData Quality Checks in Snowflake: DMFs, Cost, and Anomaly Detection
Snowflake data metric functions, expectations, and native anomaly detection: the SQL, the edition requirements, the credit cost, and where they stop.
Read articleData Observability vs Data Governance: The Difference, and How They Work Together
Governance sets the rules for data. Observability checks the data is healthy right now. The difference, where they overlap, and which to invest in first.
Read articleBigQuery Anomaly Detection: How to Do It in SQL, and Where It Stops
How to detect anomalies in BigQuery with ML.DETECT_ANOMALIES: real SQL for ARIMA_PLUS, KMEANS, and PCA, and where warehouse-wide monitoring takes over.
Read articleHow to Reduce Data Monitoring Costs on Snowflake
Most surprise Snowflake monitoring bills come from scanning rows on every check. Read metadata instead, right-size the warehouse, and space out checks.
Read articleAmazon Redshift Monitoring: Data Quality, Freshness, and Anomalies
How to monitor data quality in Amazon Redshift: freshness and volume from system tables, schema drift, anomalies, and where native tools stop.
Read articleColumn-Level Lineage: What It Is and Why Incidents Need It
Column-level lineage maps how each column flows from source to dashboard, how it is generated from SQL, and why it cuts incident triage time.
Read articledbt Cloud Data Observability Features: What You Get and What Is Missing
The four observability features dbt Cloud actually ships, the gaps they leave, and when it makes sense to add a data observability tool alongside dbt.
Read articleDatabricks Data Quality Monitoring: Anomaly Detection and What It Misses
How Databricks anomaly detection and Lakehouse Monitoring work, what they cost, how they differ from DLT expectations, and the gaps they leave.
Read articleData Catalog vs Data Lineage: Do You Need Both?
A catalog helps people find and understand data. Lineage traces how it flows and what breaks. Where they overlap, and which to buy first.
Read articlePut it into practice
Connect a warehouse and get all five pillars monitoring from one read-only connection. Transparent pricing, no credit card.