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Connect App Store Connect to Snowflake

Connect App Store Connect to Snowflake and export selected accounts, metrics, and dimensions to your Google BigQuery project.

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

App Store Connect export job

First sync scheduled

App Store Connect Selected accounts

Configured job

Snowflake Destination table

Account App Store Connect · 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 App Store Connect data to Snowflake

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize App Store Connect data in PostgreSQL

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

Source

App Store Connect Application store data

INapps PMapp_versions CUdownloads BLinstalls

Catchr pipeline IN PM CU Extract · normalize · load

Snowflake catchr_data.App Store Connect_raw

TableScopeStatus

appsSelectedReady

app_versionsSelectedReady

downloadsSelectedReady

installsSelectedReady

Data freshness

Keep your Snowflake tables up to date

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

App Store ConnectConfigured account

SnowflakeApp Store Connect 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

apps326 updated app_versions102 appended Warehouse up to date

Analysis

Unify App Store Connect 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 apps · Engagement Count Application store

Marketingcampaign · spend · UTM Ads

CRMcontact_id · lifecycle Sales

Productaccount_id · event_date Usage

Match keys customer · date · campaign

Snowflake model business_360 Unified

FieldFrom

customer_idCRM

Engagement Countapps

ad_spendMarketing

lifecycle_stageCRM

active_usersProduct

Ready for SQL 5 business fields

App Store Connect metrics and dimensions available in Snowflake

Access 14 metrics and 157 dimensions from App Store Connect connectors including Impressions, Tap, Product Page View, Downloaded Count, Install Count, Review Rating, App Name, Date, Downloaded Source Type, Downloaded Territory, Downloaded Device, Engagement Campaign and many more...

Metrics availables

157

Dimensions availables

View all App Store Connect Metrics metrics and dimensions

Connect App Store Connect to Snowflake in minutes

Move your data from App Store Connect to Snowflake 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 App Store Connect account

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

Connect now

Client A · Connected sources

5 sources ready

App Store Connect 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 Snowflake destination

Add the account host, user, and database schema. Use a password for compatible users or a PEM private key for service accounts and stricter policies.

Destination / Snowflake Required fields

Authentication Choose one

Password

Key pair

Host Required

xy12345.eu-west-1

Username Required

catchr_service

Database Required

MARKETING.RAW

Private key passphrase Optional

••••••••••••

Private key Required for key pair

-----BEGIN ENCRYPTED PRIVATE KEY----- MIIE6TAbBgkqhkiG9w0BBQMw... -----END ENCRYPTED PRIVATE KEY-----

Step three

Keep your Snowflake 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 App Store Connect app growth performance in SQL.

This example summarizes downloads, installs, revenue, and install rate by application.

30-day window

SELECT
  app_name,
  SUM(downloads) AS downloads,
  SUM(installs) AS installs,
  SUM(revenue) AS revenue,
  SAFE_DIVIDE(
    SUM(installs),
    SUM(downloads)
  ) AS install_rate
FROM `your_project.marketing_raw.app_store_connect_app_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY app_name
ORDER BY downloads DESC;

Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Choose what to load into Snowflake.

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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Emailing

Before you schedule the job.

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

Can Catchr send App Store Connect records directly to Snowflake?

Add App Store Connect in Sources and connect the existing Snowflake database in Storage. A datastream defines the route, while the job loads the chosen fields into "app versions".

How much control do I have over App Store Connect fields in Snowflake?

Select concrete App Store Connect fields such as Engagement Count, Impressions, Page View, and Day of the month. You can rename the chosen columns, preview the result, and write them to Snowflake tables such as apps, app versions, and downloads.

How should I configure recurring App Store Connect imports in Snowflake?

Define the first App Store Connect fetch, run frequency, and lookback window in the "app versions" job. Each scheduled run replaces data inside the configured period.

How do I prevent duplicate App Store Connect rows in Snowflake?

Use a supported date field to partition "app versions". Recurring App Store Connect runs can then update the selected window instead of adding another copy of the same period.

How can a team use App Store Connect data after it reaches Snowflake?

With App Store Connect data, a Snowflake query can summarize downloads, installs, revenue, and install rate by application. Model Engagement Count and Impressions in SQL, then expose the result to dashboards, internal tools, or recurring client reports.

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

Yes. Connect an existing Snowflake 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 Snowflake 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.

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100+ sources

Used by the world's leading companies

Export App Store Connect data to Snowflake

Centralize App Store Connect data in PostgreSQL

Keep your Snowflake tables up to date

Unify App Store Connect with the rest of your business data

App Store Connect metrics and dimensions available in Snowflake

Connect App Store Connect to Snowflake in minutes

Connect your App Store Connect account

Choose the Snowflake destination

Keep your Snowflake tables updated

Review 30-day App Store Connect app growth performance in SQL.

Choose what to load into Snowflake.

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