Export Data Quality Results to ADLS Gen2 Using Ataccama ONE

Exporting Data Quality Results to ADLS Gen2 with Post-Processing in Ataccama ONE 

Running data quality checks in Ataccama ONE provides real-time visibility into data health, but the real value comes when those results are stored and reused. By exporting data quality results to Azure Data Lake Storage Gen2 (ADLS Gen2), organizations can turn one-time checks into a long-term data quality audit trail. 

This approach supports data quality reporting, trend analysis, and enterprise data governance, making it easier to track performance over time and feed downstream dashboards. 

What is a Post-Processing Plan in Ataccama ONE? 

A post-processing plan in Ataccama ONE is a configuration within ONE Desktop that runs automatically after a data quality monitoring project completes. It processes outputs such as rule violations, validity scores, and data quality metrics, and determines how those results are stored or exported. 

By default, results are discarded. However, replacing the default step with a file writer enables data quality export to cloud storage, allowing teams to persist results in a structured format. 

Why Export Data Quality Results to ADLS Gen2? 

Exporting data quality results to a data lake is a best practice in modern data quality management and Azure data engineering. 

Key benefits include: 

• Build a centralized data quality audit trail 

• Enable automated data quality reporting 

• Support trend analysis across multiple runs 

• Integrate with BI dashboards and analytics tools 

• Strengthen enterprise data governance frameworks 

Using ADLS Gen2 ensures scalable and secure cloud data storage for data quality results. 

Step-by-Step: How to Export DQ Results to ADLS 

 Part 1 — Configure the ADLS Gen2 Connection in Ataccama ONE Web 

Step 1 — Go to Catalog → Sources → Create. Give the source a meaningful name such as 

adls-dq-results. 

Step 2 — Add Connection. Select File System → Azure Data Lake Storage Gen2 as the connection type. 

Step 3 — Fill in connection details. Enter your storage account name, container name (e.g. 

dq-results), and paste your ADLS Shared Key — found in Azure Portal → Storage Account → 

Security + Networking → Access Keys. 

Step 4 — Test and Save. Click Test Connection. If green, click Save and Publish. Note the connection 

name — you will reference it in the plan. 

Part 2 — Build the Post-Processing Plan in ONE Desktop 

Step 1 — Open your Monitoring Project. Connect ONE Desktop to your Ataccama ONE server and 

open the relevant monitoring project. 

Step 2 — Create a new Component. Right-click Components → New Component. Name it 

Export_DQ_to_ADLS. 

Step 3 — Set the Output Location. Use the pattern export/${date}/${catalogItemName}/ 

— this becomes the folder structure inside your ADLS container. 

Step 4 — Replace Trash with Text File Writer. Edit the component, delete the Trash step, and add a 

Text File Writer step in its place. 

Step 5 — Configure the Text File Writer. Set separator to comma, enable Write Header, set encoding to 

UTF-8. On the Columns tab, map these fields: rule_name, dataset, attribute_name, 

passed_count, failed_count, total_count, validity_percent, run_date. 

Step 6 — Point the output to ADLS. Set the file path using the resource notation: 

resource://adls-dq-results/dq-results/export/${date}/dq_results.csv 

Step 7 — Publish the Component. Right-click → Publish → set State to Implemented → click Publish. 

Part 3 — Run and Verify 

Step 1 — Trigger the Monitoring Project. In Ataccama ONE Web, open your Monitoring Project and 

click Run Monitoring. 

Step 2 — Check the Export tab. Once complete, navigate to the Export tab. The presence of Output.csv 

confirms the post-processing plan executed successfully. 

Step 3 — Verify in Azure Portal. Go to Storage Account → Containers → dq-results. You should 

see the file at: 

export/YYYY-MM-DD/dq_results.csv 

ADLS Connection Config Reference (DPE Runtime) 

If you need to configure the ADLS connection at the Data Processing Engine level via Global Runtime 

Configuration, use this XML block: 

xml 

<config class=”com.ataccama.dqc.azure.config.AzureGen2Contributor”> 

<azureGen2Connections> 

<azureGen2Connection 

name=”adls-dq-results” 

storageAccount=”YOUR_STORAGE_ACCOUNT” 

containerName=”dq-results” 

sharedKey=”YOUR_STORAGE_ACCOUNT_KEY” 

authenticateUser=”false” 

/> 

</azureGen2Connections> 

</config> 

Then reference the connection in your file path as 

resource://adls-dq-results/export/dq_results.csv 

Conclusion 

Exporting data quality results from Ataccama ONE to ADLS Gen2 transforms monitoring into a scalable and reusable process. Instead of losing valuable insights after each run, organizations can store and analyze data quality metrics over time. 

With a properly configured post-processing plan, every execution automatically contributes to a centralized data quality repository in the data lake. This enables better decision-making, supports compliance requirements, and strengthens long-term data governance strategies. 

Whether you are building dashboards, enabling audit trails, or improving data reliability, integrating Ataccama ONE with Azure Data Lake Storage Gen2 provides a strong foundation for enterprise data quality solutions.