In today’s business environment, data is the new oil, and marketing teams are the most active refineries. Every ad impression, every user click, every conversion contains valuable insights. But traditional data analysis approaches are either too slow (Excel requires manual processing) or too expensive (professional BI tools cost thousands per seat).
DuckDB solves this pain point perfectly — it combines SQL’s ease of use with columnar storage performance, embeds directly into applications without requiring a separate server. This makes it the ideal technology stack for building lightweight marketing data products.
An e-commerce marketing team faced these challenges before implementing DuckDB:
Problem 1: Manually aggregating ad data from Google Ads, Facebook, WeChat Ads, etc., took over 3 hours daily Problem 2: Reports were often inconsistent due to different data source formats Problem 3: Management wanted real-time ROI visibility, but yesterday’s data wasn’t available until noon the next day
DuckDB provided fundamental solutions to all three problems.
Use DuckDB’s httpfs and parquet plugins to read data directly from various sources without pre-importing:
-- Directly read Google Ads CSV (auto-infer schema)
CREATE TABLE google_ads AS
READ_CSV('https://ads.google.com/export/data.csv',
HEADER TRUE, AUTO_DETECT TRUE);
-- Read nested JSON from Facebook ads
CREATE TABLE facebook_ads AS
SELECT
campaign_id,
json_extract(data, '$.spend') as spend,
json_extract(data, '$.clicks') as clicks,
json_extract(data, '$.conversions') as conversions
FROM 'facebook_export.json';
Integrate multi-source data through CTEs and views:
-- Create marketing performance view (core SQL)
CREATE VIEW marketing_performance AS
SELECT
campaign_id,
platform,
SUM(impressions) as impressions,
SUM(clicks) as clicks,
SUM(conversions) as conversions,
SUM(cpc_cost) as cost,
1.0 * SUM(clicks)/SUM(impressions) as ctr,
1.0 * SUM(conversions)/SUM(clicks) as cvr,
1.0 * SUM(cpc_cost)/SUM(conversions) as cpv
FROM marketing_data
WHERE date BETWEEN DATEADD('day', -90, CURRENT_DATE) AND CURRENT_DATE;
-- Find channels with best ROI
SELECT
platform,
SUM(cost) as total_cost,
SUM(conversions) as total_conversions,
1.0*SUM(cost)/SUM(conversions) as avg_conversion_cost,
RANK() OVER (ORDER BY 1.0*SUM(cost)/SUM(conversions)) as roi_rank
FROM marketing_performance
GROUP BY platform
ORDER BY avg_conversion_cost ASC;
For daily reports, use materialized views for sub-second response:
-- Create daily dimension materialized view
CREATE MATERIALIZED VIEW daily_marketing_summary AS
SELECT
DATE(date) as event_day,
platform,
SUM(impressions) as daily_impressions,
SUM(clicks) as daily_clicks,
SUM(conversions) as daily_conversions,
SUM(cost) as daily_cost
FROM marketing_data
GROUP BY DATE(date), platform;
-- Refresh nightly
REFRESH MATERIALIZED VIEW daily_marketing_summary;