Query Service: checking your AEP data with SQL after ingestion

Why this matters

Without checking data after ingestion, teams risk deploying workflows or models based on incomplete or incorrect data. You might see expected row counts, but empty fields or unexpected values can silently skew analysis and lead to flawed decisions. A quick SQL query against the dataset reveals discrepancies early—like missing fields or inconsistent formats—before downstream processes start using flawed data.

Relying solely on ingestion logs or metadata doesn’t catch mapping errors or data quality issues. A simple SQL query checks actual content: row counts, field values, and data types. This helps catch mismatches between source and target fields early. Without this verification, mistakes compound, leading to wasted effort, misaligned campaigns, and incorrect insights. Query Service enables real-time validation, ensuring the data that flows into analytics is accurate and trustworthy.

The key ideas

Query Service allows you to run SQL queries directly on datasets stored in the Experience Platform data lake. Each dataset appears as a table, so you can use standard SQL to inspect data, such as checking row counts or looking for missing or unexpected values. This helps verify that data from a new source actually landed correctly after ingestion.

Using SQL in Query Service lets you spot issues early—like empty fields or incorrect mappings—before they impact downstream workflows. A quick query after a new data source goes live can confirm the data structure matches expectations. This simple check catches mapping errors or data quality problems at the earliest stage.

Because Query Service supports PostgreSQL-compatible clients, you can use familiar tools to connect and run queries. Results from these queries can be saved as new datasets for future analysis or reporting. This makes it easy to validate data integrity and maintain confidence in downstream processes. The combination of SQL and Query Service provides a straightforward, reliable way to confirm what actually landed in the system.

How to apply it

After a new data source goes live, run a simple SQL query in Query Service to verify what data actually landed. Start with a basic count of rows to confirm ingestion occurred. Then check for empty fields or unexpected values using standard SQL functions like COUNT(*), WHERE, and IS NULL. This helps catch mapping errors or data quality issues early, before they impact downstream analysis.

Use PostgreSQL-compatible tools to connect to Query Service and execute queries directly. Since each dataset appears as a table, you can explore the schema and sample data with ease. If needed, save the results of your query as a new dataset for future reference or reporting.

A quick sanity check after every new data source ingestion—just a few lines of SQL—can reveal mismatches in field names, missing values, or incorrect data types. This practice ensures that your downstream workflows and analytics are built on accurate, reliable data. Consistent use of Query Service for validation reduces the risk of misinterpretation and supports faster, more confident decision-making.

Mistakes to avoid

Avoid assuming data landed correctly without verification. Relying on ingestion logs alone can miss mapping errors or field mismatches. Use Query Service to run a simple SQL query to check row counts and field values immediately after a new data source goes live. This confirms actual data presence and detects empty fields or unexpected values.

Skipping a quick sanity check delays discovery of issues. For example, a misaligned field name or missing value in a critical column can go unnoticed until downstream processes fail. Run a basic SQL query to validate the dataset structure and content. Save the results as a new dataset to track changes over time. This proactive approach catches mapping mistakes early and ensures data integrity before moving forward.

Quick checklist

• Run a basic SQL query to count the number of rows in the new dataset to confirm ingestion occurred
• Check for empty or null values in key fields to ensure data integrity
• Verify expected column names and data types match the source mapping
• Confirm that no unexpected or invalid values (e.g., out-of-range numbers, malformed strings) appear in the data
• Execute a simple filter query to validate sample records contain expected content
• Save the query results as a new dataset for future reference and auditing
• Use a PostgreSQL-compatible tool to connect and run queries, ensuring compatibility
• Perform this check immediately after the new data source goes live to catch mapping issues early