--- title: "Lineage: Root Cause Analysis for the Data Engineer" url: "https://atlan.com/demos/rca-for-the-data-engineer/" excerpt: "Accelerate root cause analysis with Atlan's comprehensive lineage and data quality integrations, quickly pinpointing issues and boosting data reliability." description: "Learn how Atlan helps data engineers trace issues upstream using lineage, announcements, orchestration tools, and custom metadata. See how impact reports and SQL transformation visibility support faster troubleshooting and root cause identification. This feature enables proactive resolution of dashboard or pipeline issues." format: "Video" duration: "PT3M6S" video: "https://videos.ctfassets.net/nwa1c00rtgxb/2NRFefJFg0RS1WV8YRLcPr/5de1d7490524852e53fa6641b5de1e01/Root_cause_analysis_for_the_data_engineer.mp4" thumbnail: "https://images.ctfassets.net/nwa1c00rtgxb/369DObrK9loQp5ZP2wyiro/a7eec7bb155565f050d9c46cc8c4e9e2/lineage-root-cause-analysis-for-the-data-engineer.webp" content_purpose: ["Product Overview"] target_persona: ["Data Engineer"] journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C4 - Expansion"] use_case_context: ["Training"] product: ["Enterprise Data Graph - Data Lineage"] published: "2025-09-23" updated: "2026-06-02" content_type: "video transcript" transcript_source: "sheet-chaptered" --- > Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/ # Lineage: Root Cause Analysis for the Data Engineer Transcript of the video at https://atlan.com/demos/rca-for-the-data-engineer/. Each heading is the time (mm:ss) that part starts. ## 00:00 — Starting root cause analysis from a Tableau dashboard In this walkthrough, we will be conducting root cause analysis as a data engineer. In this case, we have received notification that there is a data issue with a tableau dashboard. We start first at the dashboard inside Tableau. In just looking at the dashboard, we can see that there is an issue on one of the graphs, and that is the days since prior order visualization. Since we have installed the Atlan Chrome extension, we click on the Alin icon to see the information regarding the Asset. Here, on the asset sidebar, we are able to click on the lineage icon to see the information, giving us an immediate understanding of where the data is coming from. Those that are displayed show only one level up and we need to investigate further upstream so we click on the view graph hyperlink. This opens a new window, taking us directly to Atlan and the lineage of the dashboard. Atlan natively connects with Airflow, Monte Carlo, and Soda, bringing in your data quality and orchestration status into Atlan. Additionally, since Atlan has open APIs, creating announcements or pushing data quality status to custom metadata can be created from other data quality and orchestration tools. We will start with the example where we are automatically creating an announcement from our orchestration tool when there is a failure. ## 01:00 — Finding failures through orchestration flags and custom metadata When we travel upstream of the asset by clicking on the plus icons, we can immediately see a red indicator on an upstream data asset. This red indicator represents an Issue type announcement on the asset. We are easily able to identify an upstream data source that may be causing the issue on the dashboard. Clicking on the asset brings up the data profile and we immediately see the issue that our orchestration failed on this data set, and gives us a spot to immediately start investigating the root cause of the dashboard failure. Now, we will take a look at how easy it is to find possible root causes for our dashboard when data quality is pushed to custom metadata. Back at the source asset, instead of working our way through the lineage graph, we will instead click on the view impacted assets icon. Here, we will switch to upstream assets. This will immediately give us a table of all the upstream assets. In this view, by scrolling to the right, all custom metadata fields will appear where there is at least a value on one of the upstream assets. In this case, we look for our data quality custom metadata field. ## 02:00 — Tracing the issue through native lineage and SQL Here, we can easily navigate to find where we have a data quality issue. From this table itself, we can immediately access the asset to do any further investigation. If none of the native integrations or custom integrations have been set up, Atlan can still provide a great route to improving root cause analysis through the native lineage that is generated. Back at our original dashboard asset, we move upstream of the asset itself as before. Once we reach the point of our presentation layer inside Snowflake, we will click on the lineage process prior to it. In this case, we can see the sequel that is creating the view. Here, we immediately see how the data is being transformed, and can be a way to find the cause of the issue right from Atlan if there are issues found in the sequel. Additionally, by downloading the impact report for all upstream assets, it can be shared and used to quickly identify all possible assets that may be the cause of the issue. By simply clicking on the download button, it will create the download needed and can be shared with others to start their investigation on the root cause of the issue. With that, we conclude our walkthrough of how Atlan, whether through native connections, custom scripting, or through base functionality, can improve your root cause analysis process.