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Connect MailerLite to PostgreSQL

Connect MailerLite 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 MailerLite API pipeline.

MailerLite export job

First sync scheduled

MailerLite Selected accounts

Configured job

PostgreSQL Destination table

Account MailerLite · 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 MailerLite data to PostgreSQL

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize MailerLite data in PostgreSQL

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

Source

MailerLite Emailing data

INaccounts PMcampaigns CUcontacts BLlists

Catchr pipeline IN PM CU Extract · normalize · load

PostgreSQL catchr_data.MailerLite_raw

TableScopeStatus

accountsSelectedReady

campaignsSelectedReady

contactsSelectedReady

listsSelectedReady

Data freshness

Keep your PostgreSQL tables up to date

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

MailerLiteConfigured account

PostgreSQLMailerLite 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

accounts326 updated campaigns102 appended Warehouse up to date

Analysis

Unify MailerLite 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 accounts · Automation Step Email Click Rate Emailing

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

Automation Step Email Click Rateaccounts

ad_spendMarketing

lifecycle_stageCRM

active_usersProduct

Ready for SQL 5 business fields

MailerLite metrics and dimensions available in PostgreSQL

Access 54 metrics and 137 dimensions from MailerLite connectors including Campaign Open Rate, Campaign Click Rate, Campaign Click To Open Rate, Campaign Sent, Campaign Unsubscribe Rate, Form Conversion Rate, Date, Campaign Name, Campaign Status, Campaign Type, Automation Name, Subscriber Source, Subscriber Country and many more...

Metrics availables

137

Dimensions availables

View all MailerLite Metrics metrics and dimensions

Connect MailerLite to PostgreSQL in minutes

Move your data from MailerLite 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 MailerLite account

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

Connect now

Client A · Connected sources

5 sources ready

MailerLite 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 MailerLite email campaign performance in SQL.

This example summarizes delivery, opens, clicks, and engagement rates by email campaign.

30-day window

SELECT
  campaign_name,
  SUM(sent) AS sent,
  SUM(delivered) AS delivered,
  SUM(opens) AS opens,
  SUM(clicks) AS clicks,
  SAFE_DIVIDE(
    SUM(opens),
    SUM(delivered)
  ) AS open_rate,
  SAFE_DIVIDE(
    SUM(clicks),
    SUM(delivered)
  ) AS click_rate
FROM `your_project.marketing_raw.mailerlite_campaign_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY campaign_name
ORDER BY delivered 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.

Where do I configure a MailerLite export to PostgreSQL?

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

Which parts of MailerLite can be exported to PostgreSQL?

Select concrete MailerLite fields such as Automation Step Email Click Rate, Automation Step Email Clicks Count, Automation Step Email Forwards Count, and Day of the month. You can rename the chosen columns, preview the result, and write them to PostgreSQL tables such as accounts, campaigns, and contacts.

How does Catchr process a large MailerLite backfill into PostgreSQL?

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

How are overlapping MailerLite import windows handled in PostgreSQL?

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

What can I analyze with MailerLite data in PostgreSQL?

With MailerLite data, a PostgreSQL query can summarize delivery, opens, clicks, and engagement rates by email campaign. A PostgreSQL view comparing Automation Step Email Click Rate and Automation Step Email Clicks Count 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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100+ sources

Used by the world's leading companies

Export MailerLite data to PostgreSQL

Centralize MailerLite data in PostgreSQL

Keep your PostgreSQL tables up to date

Unify MailerLite with the rest of your business data

MailerLite metrics and dimensions available in PostgreSQL

Connect MailerLite to PostgreSQL in minutes

Connect your MailerLite account

Choose the PostgreSQL destination

Keep your PostgreSQL tables updated

Review 30-day MailerLite email campaign performance in SQL.

Choose what to load into PostgreSQL.

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