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Connect AdsWizz to PostgreSQL

Connect AdsWizz to PostgreSQL and export selected accounts, metrics, and dimensions to your PostgreSQL project.

Configure the historical window, destination table, and recurring schedule without maintaining a AdsWizz API pipeline.

Used by the world's leading companies

Export AdsWizz data to PostgreSQL

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize AdsWizz data in PostgreSQL

Bring all your AdsWizz data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data freshness

Keep your PostgreSQL tables up to date

Automatically sync new data from AdsWizz so every dashboard, model, and report works with fresh information.

Analysis

Unify AdsWizz with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

AdsWizz metrics and dimensions available in PostgreSQL

Access 59 metrics and 102 dimensions from AdsWizz connectors including Total Impressions, Audio Impressions, Clicks, CTR, Total Conversions, Conversion Rate, Return on Ad Spend, Total Reach - Unique Users, Campaign, Advertiser, Ad, Date, Geo - Country, Device Type, Publisher, Ad format and many more...

59

Metrics availables

102

Dimensions availables

Connect AdsWizz to PostgreSQL in minutes

Move your data from AdsWizz to PostgreSQL with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your AdsWizz account

Authenticate your AdsWizz account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Client A · Connected sources
5 sources ready
AdsWizz 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step two

Choose the PostgreSQL destination

Add the database host and credentials once. Catchr checks the connection so your exports start with a reachable destination.

Destination / PostgreSQL Required fields
Host Required
warehouse.company.net
Username Required
catchr_writer
Password Required
••••••••••••
Database Required
marketing
Step three

Keep your PostgreSQL tables updated

Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.

Export job / PostgreSQL Example
Schema Destination
marketing_raw
Table Per job
linkedin_ads_daily
Recurring schedule Choose a cadence
Every 6 hours
Daily · 07:00
Weekly · Mon
First sync scheduledSelected fields · configured history
07:00 UTC

Review 30-day AdsWizz campaign delivery in SQL.

This example summarizes spend, impressions, clicks, and click-through rate by campaign.

AdsWizz.sql
BigQuery SQL
30-day window
SELECT
  campaign_name,
  SUM(spend) AS spend,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  SAFE_DIVIDE(
    SUM(clicks),
    SUM(impressions)
  ) AS ctr
FROM `your_project.marketing_raw.adswizz_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY campaign_name
ORDER BY spend DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Choose what to load into PostgreSQL.

Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.

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Before you schedule the job.

Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.

Where do I configure an AdsWizz export to PostgreSQL?

Connect AdsWizz as a Catchr source. Add your existing PostgreSQL database, create a datastream, and configure a job that writes the selected fields to the "ad groups" table.

Which parts of AdsWizz can be exported to PostgreSQL?

Select concrete AdsWizz fields such as CPA, Click-Through Conversion Rate, Click-Through Conversions, and Normalized Week of the year. You can rename the chosen columns, preview the result, and write them to PostgreSQL tables such as accounts, advertisers, and campaigns.

How does Catchr process a large AdsWizz backfill into PostgreSQL?

Catchr first processes the historical range configured for AdsWizz. Later runs update the "ad groups" table using the saved frequency and rolling import window.

How are overlapping AdsWizz import windows handled in PostgreSQL?

Catchr uses the selected partition field to refresh the relevant AdsWizz period inside "ad groups". Leaving it blank changes the job to append-only behavior.

What can I analyze with AdsWizz data in PostgreSQL?

With AdsWizz data, a PostgreSQL query can summarize spend, impressions, clicks, and click-through rate by campaign. A PostgreSQL view comparing CPA and Click-Through Conversion Rate can feed agency dashboards or downstream BI models.

Does my PostgreSQL database need to exist before I connect it?

Yes. Connect an existing PostgreSQL database first. The Catchr job creates and populates the destination table you configure inside that database.

Can I load several marketing sources into the same Postgres database?

Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.

Still have a question ? 

Our teams is always here to responds to any question you could have about our data connector. 

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