Basic SQL for pulling and joining ad performance datasets
The use of SQL to extract, filter, aggregate, and combine structured data from advertising platform databases to analyze campaign performance, audience engagement, and return on ad spend (ROAS).
This skill enables direct, unmediated access to granular performance data, bypassing platform UI limitations and generic reports. It allows analysts and marketers to create custom attribution models, perform cohort analysis, and uncover hidden optimization opportunities, directly impacting budget allocation efficiency and campaign ROI.
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8.5 Avg Demand
20%
Avg AI Risk
How to Learn Basic SQL for pulling and joining ad performance datasets
1. Master SELECT, FROM, WHERE, GROUP BY, ORDER BY for basic data extraction and filtering. 2. Understand JOIN types (INNER, LEFT) and the concept of using keys (e.g., campaign_id) to relate tables like 'campaigns' and 'impressions'. 3. Learn basic aggregate functions: SUM(cost), SUM(clicks), COUNT(DISTINCT user_id) to compute core metrics.
1. Move to multi-table JOINs involving 3+ tables (e.g., ads, clicks, conversions) with aliasing. 2. Use subqueries and Common Table Expressions (CTEs) to break down complex logic (e.g., creating a funnel step before joining). 3. Avoid common pitfalls: Cartesian products from missing JOIN conditions, incorrect GROUP BY clauses causing inaccurate aggregations, and NULL value handling in cost/revenue fields.
1. Design and query large-scale, partitioned data warehouse tables (e.g., BigQuery) using PARTITION BY and clustering for cost/performance optimization. 2. Build reusable SQL templates and macros for standardized reporting (e.g., ROAS by geo/device). 3. Architect multi-source data pipelines, joining ad platform data with internal CRM or product analytics data for full-funnel attribution.
Practice Projects
Beginner
Project
Google Ads Campaign Performance Pull
Scenario
Extract last 7 days of search campaign performance data segmented by campaign and ad group, including impressions, clicks, and cost.
How to Execute
1. Identify the relevant table (e.g., `campaign_performance`). 2. Write a SELECT statement with filters for date range and campaign type. 3. Use GROUP BY campaign_id and ad_group_id. 4. Calculate Cost Per Click (CPC) using cost/clicks.
Intermediate
Project
Cross-Platform Conversion Analysis
Scenario
Join Facebook Ads impression data with Google Analytics conversion data on a matched user_id to calculate view-through conversion rates.
How to Execute
1. Write a CTE to pull and aggregate Facebook impressions by user_id and campaign. 2. Write a second CTE to pull GA conversions by the same user_id. 3. Perform a LEFT JOIN from impressions to conversions. 4. Calculate conversion rate as COUNT(conversions) / COUNT(DISTINCT user_id) per campaign.
Advanced
Project
Incrementality Test Dataset Assembly
Scenario
Prepare datasets for a geo-holdout incrementality test by combining ad exposure logs, transaction data, and control group assignments from a data warehouse.
How to Execute
1. Use window functions (ROW_NUMBER, RANK) to deduplicate exposure logs. 2. Create a test vs. control flag using a CASE WHEN statement based on geo segments. 3. Join ad exposures with offline transaction data using probabilistic matching on timestamps and location. 4. Aggregate results at the geo-week level for statistical analysis in a downstream tool.
BigQuery is the industry standard for analyzing large-scale, serverless ad log data. Redshift and PostgreSQL are common in proprietary data stacks. A quality IDE provides syntax highlighting, auto-complete, and query profiling essential for writing and optimizing complex joins.
Methodologies & Paradigms
CTEs over SubqueriesModular SQL ScriptingDimensional Modeling (Star Schema)
Prioritize CTEs for readability and debuggability. Structure queries in logical blocks (e.g., source, filter, join, aggregate). Understand that ad data is often modeled in a star schema with fact tables (impressions, clicks) and dimension tables (campaign, creative, audience).
Interview Questions
Answer Strategy
The question tests ability to count distinct users, aggregate by two dimensions, and rank results. Use a CTE to first compute the unique user count per campaign/creative. Then, use a window function (ROW_NUMBER() or RANK()) partitioned by campaign_id and ordered by the count descending. Finally, filter for rank <= 3. Sample Answer: 'I'll first create a CTE to calculate COUNT(DISTINCT user_id) grouped by campaign_id and creative_id for the last 30 days. Then, I'll use ROW_NUMBER() OVER (PARTITION BY campaign_id ORDER BY distinct_users DESC) to assign a rank within each campaign. The final SELECT filters where rank <= 3.'
Answer Strategy
Tests data skepticism, validation methodology, and ownership. Focus on the process of understanding data lineage, applying filters to clean noise, using assertions (e.g., checking row counts before/after joins), and spot-checking against known business events. Sample Answer: 'My process starts with profiling each source: null rates, key cardinality, and time range coverage. I then build the join incrementally, using a staging CTE for each source with filters applied. After joining, I validate by comparing aggregate totals (e.g., total spend) against a trusted dashboard and by checking for unexpected drops in row count, which often indicate a broken JOIN condition. I document all assumptions and filters in the query comments.'
Careers That Require Basic SQL for pulling and joining ad performance datasets