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Connect Chartmogul to PostgreSQL

Connect Chartmogul 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 Chartmogul API pipeline.

Chartmogul export job

First sync scheduled

Chartmogul Selected accounts

Configured job

PostgreSQL Destination table

Account Chartmogul · Client group A

Table daily_dump

Initial import Configured historical window

Schedule Daily · 06:00 UTC

Fields Selected metrics + dimensions

Partition date · eligible field

Illustrative job · configure values for your account Export ready

Used by the world's leading companies

Export Chartmogul data to PostgreSQL

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize ChartMogul data in PostgreSQL

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

Source

ChartMogul Finance data

INcustomers PMsubscriptions CUplans BLrevenue_movements

Catchr pipeline IN PM CU Extract · normalize · load

PostgreSQL catchr_data.ChartMogul_raw

TableScopeStatus

customersSelectedReady

subscriptionsSelectedReady

plansSelectedReady

revenue_movementsSelectedReady

Data freshness

Keep your PostgreSQL tables up to date

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

ChartMogulConfigured account

PostgreSQLChartmogul Metrics_raw

Rows processed Scheduled loads Example 24h

00:0006:0012:0018:00

Latest run Completed 06:00 UTC

New rows +428 4 tables updated

Next run Scheduled 12:00 UTC

customers326 updated subscriptions102 appended Warehouse up to date

Analysis

Unify Chartmogul with the rest of your business data

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

Business data model

model / business_360

Warehouse inputs

QuickBooks customers · ARPA Finance

Marketingcampaign · spend · UTM Ads

CRMcontact_id · lifecycle Sales

Productaccount_id · event_date Usage

Match keys customer · date · campaign

PostgreSQL model business_360 Unified

FieldFrom

customer_idCRM

ARPAcustomers

ad_spendMarketing

lifecycle_stageCRM

active_usersProduct

Ready for SQL 5 business fields

Chartmogul metrics and dimensions available in PostgreSQL

Access 11 metrics and 40 dimensions from Chartmogul connectors including MRR, ARR, ARPA, Customer Churn Rate, MRR Churn Rate, LTV, Date, Customer Company, Customer Status, Customer Country, Customer Currency and many more...

Metrics availables

Dimensions availables

View all Chartmogul Metrics metrics and dimensions

Connect Chartmogul to PostgreSQL in minutes

Move your data from Chartmogul 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 Chartmogul account

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

Connect now

Client A · Connected sources

5 sources ready

Chartmogul 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 Chartmogul subscription revenue in SQL.

This example summarizes recurring revenue, active customers, churned customers, and churn rate by plan.

30-day window

SELECT
  plan_name,
  SUM(mrr) AS mrr,
  SUM(active_customers) AS active_customers,
  SUM(churned_customers) AS churned_customers,
  SAFE_DIVIDE(
    SUM(churned_customers),
    SUM(active_customers)
  ) AS churn_rate
FROM `your_project.marketing_raw.chartmogul_subscriptions_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY plan_name
ORDER BY mrr 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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Finance

Before you schedule the job.

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

What is the setup for sending Chartmogul data to PostgreSQL?

Add Chartmogul in Sources and connect the existing PostgreSQL database in Storage. A datastream defines the route, while the job loads the chosen fields into "subscriptions".

What can I select from Chartmogul before writing to PostgreSQL?

Select concrete Chartmogul fields such as ARPA, ARR, ASP, and Day of the month. You can rename the chosen columns, preview the result, and write them to PostgreSQL tables such as customers, subscriptions, and plans.

Can a Chartmogul-to-PostgreSQL job import historical periods?

Define the first Chartmogul fetch, run frequency, and lookback window in the "subscriptions" job. Each scheduled run replaces data inside the configured period.

Why does the Chartmogul PostgreSQL job need a partition field?

Use a supported date field to partition "subscriptions". Recurring Chartmogul runs can then update the selected window instead of adding another copy of the same period.

Which decisions can a Chartmogul dataset in PostgreSQL support?

With Chartmogul data, a PostgreSQL query can summarize recurring revenue, active customers, churned customers, and churn rate by plan. Model ARPA and ARR in SQL, then expose the result to dashboards, internal tools, or recurring client reports.

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 ?

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100+ sources

Used by the world's leading companies

Export Chartmogul data to PostgreSQL

Centralize ChartMogul data in PostgreSQL

Keep your PostgreSQL tables up to date

Unify Chartmogul with the rest of your business data

Chartmogul metrics and dimensions available in PostgreSQL

Connect Chartmogul to PostgreSQL in minutes

Connect your Chartmogul account

Choose the PostgreSQL destination

Keep your PostgreSQL tables updated

Review 30-day Chartmogul subscription revenue in SQL.

Choose what to load into PostgreSQL.

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Youtube Analytics

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LinkedIn Ads

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Facebook Pages

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X / Twitter Ads

TikTok Ads

Google Merchant Center

Google Analytics

Instagram

TikTok

Shopify

Google My Business

Snapchat Ads

LinkedIn Pages

Google Search Console

Spotify Ads

WooCommerce

Matomo

Klaviyo

Mailchimp

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