--- title: "Data Observability | Datadog" description: "Detect, resolve, and optimize data quality and pipeline issues before they corrupt AI models or undermine business decisions — all in one place." locale: en canonical_path: "products/observability/data-observability/" --- # Ensure trust in your data from pipelines to prompts _Data Observability_ Detect, resolve, and optimize data quality and pipeline issues before they undermine AI models and impact business decisions — all in one place. [Ensure trust in your data from pipelines to prompts](https://web-assets.dd-static.net/42588/1781535965-do-hero-1.mp4) ![Ensure trust in your data from pipelines to prompts](https://web-assets.dd-static.net/42588/1781565602-jobs_landing_page_dark-3.png) Try Data Observability Free ## End-to-end observability across the full data lifecycle Safeguard the data your business depends on. Catch data issues early, resolve them faster, and optimize pipeline cost and performance. - **Detect Data Quality & Pipeline Issues Early** — Catch data quality issues and pipeline failures before they reach stakeholders, undermine AI models and impact business decisions. ![Detect Data Quality & Pipeline Issues Early](https://web-assets.dd-static.net/42588/1780683952-pinpoint-1.svg) - **Pinpoint Root Causes With End-to-End Lineage** — Resolve data incidents faster with full visibility into upstream services and downstream AI models and BI tools. ![Pinpoint Root Causes With End-to-End Lineage](https://web-assets.dd-static.net/42588/1780685351-pipeline-x-2.svg) - **Control Costs With Optimization Recommendations ** — Eliminate wasted spend and improve pipeline efficiency with actionable recommendations for jobs, clusters, and queries. ![Control Costs With Optimization Recommendations ](https://web-assets.dd-static.net/42588/1780685337-money-time-2.svg) - **Break Down Silos Across Data & DevOps** — Connect data health, pipeline execution, application traces, and infrastructure metrics so Data and DevOps teams resolve incidents together, not in parallel. ![Break Down Silos Across Data & DevOps](https://web-assets.dd-static.net/42588/1780683605-ci-arrow-1.svg) ## Observability beyond the data layer Datadog doesn't just monitor your data stack. It connects data health to job execution, infrastructure metrics, and application performance in context — so you're not just finding where data broke, you're understanding why. ![Observability beyond the data layer](https://web-assets.dd-static.net/42588/1781120699-data-observability-callout.png) ### Built for every team that touches data From pipelines to prompts, get the visibility your team needs to detect issues early, resolve them faster, and ensure trust in the data your stakeholders depend on. ##### Data Engineers: Be the first to know when data fails * Detect critical data issues with ML-powered anomaly detection that learns from your data’s seasonality, trends, and direct annotations. * Identify root causes with end-to-end lineage and detailed job execution traces – without jumping between tools * Get real-time alerts on failed, delayed and long-running jobs across Databricks, Spark, Airflow, and dbt * Pinpoint root causes with end-to-end lineage and detailed job execution traces – without jumping between tools * Route incidents to the right owner instantly with alert routing and escalation tools via Software Catalog ![Be the first to know when data fails](https://web-assets.dd-static.net/42588/1780429325-row_count_darkmode-2.png) ##### Data Platform Engineers: Control costs without sacrificing performance * Correlate job runs with infrastructure metrics and logs to separate code issues from platform bottlenecks * Identify overprovisioned clusters and inefficient queries — then act on optimization recommendations before costs spike or SLAs slip * Inventory and monitor jobs and clusters across every account and workspace in a centralized view * Map dependencies with data and code lineage across Quality Monitoring and Data Streams Monitoring to trace platform bottlenecks to the source ![Control costs without sacrificing performance ](https://web-assets.dd-static.net/42588/1780429319-jobs_landing_page_dark-2.png) _2026 GARTNER® DATA OBSERVABILITY GUIDE_ ## Highlighted in Gartner’s 2026 Data Observability Market Guide [Read the Full Report](https://www.datadoghq.com/resources/gartner-reprint-data-observability-market-guide/) ## Everything you need to deliver reliable data ### Detect silent data issues ![Detect silent data issues](https://web-assets.dd-static.net/42588/1781552661-do-feature1-1.png) - **Intelligent anomaly detection** — Detect data quality issues before stakeholders do with anomaly detection that learns from the data in your stack – adapting to seasonality, trends, and user annotations - **Schema change tracking** — Eliminate silent failures with automatic schema change detection across your most critical tables and warehouses. - **Real-time pipeline monitoring** — Catch pipeline failures first with real-time monitoring of failed, delayed, and long-running jobs. ### Accelerate remediation: Accelerate Remediation ![Accelerate Remediation](https://web-assets.dd-static.net/42588/1781552661-do-feature2-1.png) - **End-to-end lineage** — Trace and resolve issues across the data lifecycle — from upstream applications and pipelines through warehouses to BI dashboards and AI. - **Job execution traces** — Reduce mean time to resolution by correlating job execution traces, logs, and run history with data anomalies ### Control costs & improve performance: Control Costs & Improve Performance ![Control Costs & Improve Performance](https://web-assets.dd-static.net/42588/1781552661-do-feature3-1.png) - **Cost insights & savings recommendations** — Reduce platform spend with job and cluster cost-level visibility - **Performance optimization recommendations** — Improve pipeline performance to meet every SLA with actionable recommendations for queries, code, and cluster configuration ### Bring data & DevOps together: Bring Data & DevOps Together ![Bring Data & DevOps Together](https://web-assets.dd-static.net/42588/1781552661-do-feature4-1.png) - **Correlated telemetry ** — Distinguish data failures from platform bottlenecks by correlating data health and job runs with infrastructure, logs, and application telemetry. - **Centralized view for data & DevOps** — Break down Data and DevOps silos with shared dashboards, alerts, and workflows all in one place. - **1000+ integrations** — Eliminate blind spots by connecting 1,000+ integrations across applications, services, and infrastructure and integrating with OpenLineage for custom, open-source workloads. ## Priced for only what you monitor Detect data quality and pipeline issues across your most critical technologies with a modular, usage-based plan. #### Quality Monitoring : Quality Monitoring Quality Monitoring $16 / per monitored table, per month³ key features - Out-of-the-box monitors for row count, freshness, uniqueness, and nullness - ML-powered anomaly detection that adapts to seasonality, trends, and annotations - End-to-end table and column-level lineage from upstream sources to downstream BI and AI systems - Platform correlation with Data Streams Monitoring and Jobs Monitoring - OpenLineage support for custom and open-source workloads Try Free #### Jobs Monitoring: Clusters Databricks and Apache Spark $0.05 / per host, per hour¹ key features - Monitor Databricks and Spark jobs running on K8s, Amazon EMR and Google Dataproc - Detect failing and long-running jobs - Quickly pinpoint and resolve the root cause of failed and long-running jobs - Reduce costs by optimizing misallocated clusters and inefficient jobs Try Free #### Serverless Databricks Contact us key features - Monitor Databricks serverless jobs - Detect failing and long-running jobs - Quickly pinpoint and resolve the root cause of failed and long-running jobs - Recommendations to optimize code and queries Let's talk #### Orchestrators Airflow, dbt, and custom jobs $0.1 / per job, per hour key features - Monitor Airflow DAGs and tasks; dbt jobs and models; custom jobs - Detect failing and long-running jobs - Quickly pinpoint and resolve the root cause of failed and long-running jobs - Correlate orchestration failures with data quality and infrastructure signals Try Free From basic plans to bespoke offerings, Datadog offers the right level of support & services for any organization. ¹ Billed annually or $0.072 on-demand ² Billed annually or $0.072 on-demand ³ Billed annually or $24 on-demand ## Frequently Asked Questions about Datadog Data Observability ### What is Datadog Data Observability? Data Observability is Datadog's solution for ensuring the reliability of your data while optimizing the performance and cost of data pipelines. It unifies Quality Monitoring and Jobs Monitoring in one place, so data and DevOps teams can detect, investigate, and resolve data issues together. ### Is Data Observability available in all regions? Data Observability is available in all AP, EU, and US data centers. ### Do I need other Datadog products to use Data Observability? No. Quality Monitoring and Jobs Monitoring are standalone products and do not require an Infrastructure Monitoring subscription. ### What integrations are supported? [Data Observability integrates with](https://www.datadoghq.com/product/platform/integrations/) including Snowflake, Databricks, BigQuery, Redshift, Apache Spark, Airflow, dbt, and more. OpenLineage support extends visibility to custom and open-source workloads. ### Can I purchase Quality Monitoring and Jobs Monitoring separately? Yes. Each product is flexible and can be purchased independently or together depending on your needs. ### Is there a free trial? Yes. You can [try Data Observability free](https://us5.datadoghq.com/signup_corp) for 14 days with no commitment required. ### What is Quality Monitoring? Quality Monitoring tracks the health of data across your data lakes and warehouses. It automatically detects data quality issues — like freshness, volume, and nullness — before bad data cascades into downstream AI, applications, and analytics. ### Which data warehouses does Quality Monitoring support? Quality Monitoring supports Snowflake, Databricks, BigQuery, Redshift, and Iceberg Tables (AWS Glue). OpenLineage support extends coverage to custom and open-source data sources. ### What is a monitored table? Datadog lets you sync as many tables as you want from your warehouse or database without counting them as monitored tables. A table only becomes a monitored table once you add a monitor — a quality check like freshness, nullness, or row count — to a table, view, materialized view, or column. ### How are custom SQL monitors counted compared to standard table monitors? **Standard monitors**: All standard monitors — freshness, row count, nullness, uniqueness — on a given table count as just one monitored table, no matter how many you add or whether they're applied to the table or to individual columns within it. **Custom SQL monitors**: Each unique custom SQL monitor adds one additional monitored table, on top of any standard monitor count. **Example**: A table with a freshness monitor and a row count monitor (1 monitored table, since both are standard) and three custom SQL monitors (3 more) results in 4 monitored tables. ### How does Quality Monitoring billing work? Quality Monitoring is billed per monitored table, per month at $16, billed annually or $24 on-demand. You only pay for the tables you actively monitor. ### What out-of-the-box monitors are available? Quality Monitoring includes out-of-the-box monitors for row count, freshness, uniqueness, and nullness. You can also create custom SQL monitors, set manual thresholds, and use GROUP BY monitors to monitor data at a granular level. ### Can Quality Monitoring detect issues that affect AI models? Yes. With end-to-end lineage, Quality Monitoring traces data issues from upstream warehouse tables through to downstream BI dashboards and AI models — so you can catch data quality problems before they degrade AI outputs or business decisions. ### What is Jobs Monitoring? Jobs Monitoring tracks the performance and reliability of your data pipelines. It automatically detects failures, slowdowns, and cost anomalies before they impact downstream workflows. ### Which platforms does Jobs Monitoring support? Jobs Monitoring supports Databricks (AWS, Azure, and Google Cloud), Apache Spark on Kubernetes, Amazon EMR, Google Dataproc, Apache Airflow, and dbt. ### How does Jobs Monitoring billing work? Jobs Monitoring uses usage-based pricing. Clusters are billed per host, per hour. Serverless and Orchestrators are billed per job, per hour. Annual and on-demand rates are available. ### What is the difference between Clusters, Serverless, and Orchestrators? Clusters covers Databricks and Spark jobs running on infrastructure like Kubernetes, Amazon EMR, and Google Dataproc. Serverless covers Databricks serverless jobs that run without dedicated infrastructure. Orchestrators covers workflow tools like Airflow DAGs and tasks, and dbt jobs and models. ### How does Jobs Monitoring help reduce pipeline costs? Jobs Monitoring surfaces cost insights and optimization recommendations at the job and cluster level, helping teams identify overprovisioned clusters and inefficient queries before they drive up spend or cause SLA breaches. ### Can I correlate job failures with infrastructure and data quality issues? Yes. Jobs Monitoring correlates job execution traces and logs with infrastructure metrics and data quality signals in one place — so data and DevOps teams can distinguish code issues from platform bottlenecks and resolve them faster, together. ## Resources & Learning  Guides, research, and technical content to help teams build, evaluate, and operate AI agents with confidence. - [Data Observability Overview](https://docs.datadoghq.com/data_observability/) — Learn how Data Observability (DO) helps data teams improve the reliability of data for analytics and AI applications and optimize the performance and costs of data pipelines. - [Understanding data lineage](https://www.datadoghq.com/blog/data-lineage/) — Discover why lineage is essential to fostering the healthy data ecosystems that form the basis of successful analytics, as well as to ensuring security and compliance. - [Detect issues and optimize spend with Databricks serverless job monitoring](https://www.datadoghq.com/blog/databricks-serverless-jobs-datadog/) — Discover how to use Datadog to detect and alert on issues in your serverless data jobs, and optimize your jobs down to the query level. - [Monitor Airflow with Datadog](https://www.datadoghq.com/blog/how-to-monitor-airflow-with-datadog/) — Discover how you can use Datadog to monitor all this telemetry in a single consolidated view alongside telemetry from the rest of your infrastructure and services.