Can you integrate Adobe Analytics with Google Cloud SQL?

Explore a Adobe Analytics and Google Cloud SQL integration with a Stacksync engineer. Stacksync support for Adobe Analytics and Google Cloud SQL is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.

Can Adobe Analytics and Google Cloud SQL sync in both directions?

Confirm two-way support with Stacksync for the records and fields you need in both systems. Access to a vendor API does not confirm that its Stacksync connector supports write-back.

What should be ready before the Adobe Analytics and Google Cloud SQL initial load?

Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Identify the Adobe Analytics account, edition, environment, and business objects the integration must access. Identify the Google Cloud SQL account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the Adobe Analytics to Google Cloud SQL sync run?

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.

Will existing duplicates merge automatically?

No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.

How do I map Adobe Analytics fields to Google Cloud SQL?

Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.

What should I do if a record fails to sync?

Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.

What does a Adobe Analytics and Google Cloud SQL integration cost?

Use the current Stacksync pricing page and confirm the supported implementation with the team. Scope the required objects, record volume, update frequency, initial load, and support needs when comparing a managed connector with native or custom development.

Which records should the Adobe Analytics and Google Cloud SQL pilot start with?

Start by reviewing Metrics in Adobe Analytics and Proposed metric or analytical result table in Google Cloud SQL (choose its name) in Google Cloud SQL. Check how these records relate in your workflow, then confirm the actual fields and supported operations. Test record matching and one failed or repeated update before adding more records.

Analytics / Database · Two-way sync platform

Adobe Analytics and Google Cloud SQL integration

Plan how Adobe Analytics and Google Cloud SQL should share data across your business. Work with Stacksync engineers on record mapping, system access, and the requirements for running the integration.

Scope your workflow with an integration engineer
Review the systems, records, and updates you need

Integration planning

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Adobe Analytics

Google Cloud SQL

Review the integration scope

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Proposed workflow

Metric or analytical result reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting eventA change involving Adobe Analytics Metrics or the proposed metric or analytical result table in Google Cloud SQL needs a defined result in the other system.

Start with Adobe Analytics Metrics and the proposed metric or analytical result table in Google Cloud SQL. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve entity keys, time zones, aggregation grain, and any currency/unit conversions.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

What to verifyCompare identical time windows and dimensions; test late-arriving data and a recalculated metric.

Review records and field ownership

Proposed record relationships

Records to connect

Use these examples to define record matching and field ownership for your technical review.

Download the mapping worksheet

CSV · No email required

Example record relationships between Adobe Analytics and Google Cloud SQL
Adobe Analytics record	Google Cloud SQL record	Record matching	Field ownership
MetricsProposed record; confirm Stacksync object support.Reporting dataset	Proposed metric or analytical result tableProposed table; choose its name and schema.	Identify a metric by definition/version, dimensions, time window, and entity key.	The analytical model owns the computation; operational systems should receive only approved outputs with freshness context.
UsersProposed record; confirm Stacksync object support.Reporting dataset	Proposed application user or identity tableProposed table; choose its name and schema.	Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact.	Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes.

These relationships do not establish connector availability. Review the required connection and record operations with Stacksync.

Record coverage to review

Use documented coverage where available. Catalog record types are starting points for review and do not confirm Stacksync support.

Adobe Analytics

Connection and object support require review

Record types to review with Stacksync

Record types	Coverage and requirements
Reports Dimensions Metrics Calculated Metrics Segments Date Ranges	Confirm support for this record type and the direction you need.

Discuss Adobe Analytics requirements

Google Cloud SQL

Connection and object support require review

Record types to review with Stacksync

Record types	Coverage and requirements
Tables Instances Databases Schemas Rows Views	Confirm support for this record type and the direction you need.

Discuss Google Cloud SQL requirements

Connection essentials

Confirm Stacksync support and account requirements for undocumented connections. Interface information alone does not establish connector availability.

View setup requirements and limits

Connection requirement	Adobe Analytics	Google Cloud SQL
Integration interface	Analytics 2.0 REST API on analytics.adobe.io for reporting and components; Data Feeds and Data Warehouse for raw hit-level export; Data Insertion and Bulk Data Insertion (CSV) APIs for inbound server-side collection.	Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
Authentication	Confirm the credentials, API plan, and permissions required for Adobe Analytics.	Confirm the credentials, API plan, and permissions required for Google Cloud SQL.
Change detection	Confirm how Stacksync detects changes for this connector and the objects you need.	Confirm how Stacksync detects changes for this connector and the objects you need.
Read access	Confirm with Stacksync	Confirm with Stacksync
Write access	Confirm with Stacksync	Confirm with Stacksync

Enterprise controls

Security and control for your integrations

Explore security controls

Compliance and data transfers

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

Record-level recovery

Inspect sync errors and use retry and revert controls to resolve failed updates.

Read the recovery guide

Implementation

Technical reference

Review setup, record relationships, testing, and recovery for your implementation.

Authentication, permissions and API limits

Connection requirements and limits

Adobe Analytics

Integration interface: Analytics 2.0 REST API on analytics.adobe.io for reporting and components; Data Feeds and Data Warehouse for raw hit-level export; Data Insertion and Bulk Data Insertion (CSV) APIs for inbound server-side collection.
Authentication: Confirm the credentials, API plan, and permissions required for Adobe Analytics.
Change detection: Confirm how Stacksync detects changes for this connector and the objects you need.
Read access: Confirm with Stacksync
Write access: Confirm with Stacksync

Setup requirements

Identify the Adobe Analytics account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Adobe Analytics, including read/write support, authentication, and initial-load limits.

Limitations to check

Confirm Stacksync support for Adobe Analytics and the record types your workflow needs.
Review write-back, deletion handling, update timing, and account limits with the integration team.

Google Cloud SQL

Integration interface: Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management
Authentication: Confirm the credentials, API plan, and permissions required for Google Cloud SQL.
Change detection: Confirm how Stacksync detects changes for this connector and the objects you need.
Read access: Confirm with Stacksync
Write access: Confirm with Stacksync

Setup requirements

Identify the Google Cloud SQL account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Google Cloud SQL, including read/write support, authentication, and initial-load limits.

Limitations to check

Confirm Stacksync support for Google Cloud SQL and the record types your workflow needs.
Review write-back, deletion handling, update timing, and account limits with the integration team.

Prepare Adobe Analytics and Google Cloud SQL access

Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.

Adobe Analytics setup checklist

Identify the Adobe Analytics account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Adobe Analytics, including read/write support, authentication, and initial-load limits.

Google Cloud SQL setup checklist

Identify the Google Cloud SQL account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Google Cloud SQL, including read/write support, authentication, and initial-load limits.

Prepare to go live

Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.

Use the Adobe Analytics and Google Cloud SQL planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

Talk to an engineer · Review current pricing

Record identity and field ownership

Use these data-model references to describe the records your connection needs. They are planning examples; connector availability and supported operations must be established before implementation.

Download the mapping worksheet · CSV, no email required

Reporting dataset

Metrics / Proposed metric or analytical result table in Google Cloud SQL (choose its name)

Plan a metric or analytical result dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Adobe Analytics: Object support to establish
Google Cloud SQL: Your database schema
Record identity: Identify a metric by definition/version, dimensions, time window, and entity key.
Field ownership: The analytical model owns the computation; operational systems should receive only approved outputs with freshness context.

Fields to include

Metric definition
Entity reference
Time window
Value
Computed-at time

Record dependencies: Resolve entity keys, time zones, aggregation grain, and any currency/unit conversions.
Validation: Compare identical time windows and dimensions; test late-arriving data and a recalculated metric.
Recovery: Recompute the intended window before retrying an output; avoid overwriting a newer result with an older computation.

Reporting dataset

Users / Proposed application user or identity table in Google Cloud SQL (choose its name)

Plan a application user or identity dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Adobe Analytics: Object support to establish
Google Cloud SQL: Your database schema
Record identity: Use the immutable user ID within the tenant or directory. Do not equate an application user with a CRM customer contact.
Field ownership: Identity and application owners approve account lifecycle and access changes; synchronize only approved attributes.

Fields to include

Source user ID
Tenant reference
Account status
Group references

Record dependencies: Resolve tenant and group references and establish a protected administrative-account policy.
Validation: Test a renamed login, disabled account, missing group, and a user existing in two tenants.
Recovery: Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.

Compare integration approaches

Choose a method around one example record and the update your business needs. Use Metrics / Proposed metric or analytical result table in Google Cloud SQL (choose its name) to review record matching and confirm Stacksync support for the required operations. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.

Stacksync managed sync

Best fit: Review compatibility with a Stacksync engineer using an example of the records and updates you need.
Operating responsibility: Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
Before you choose: Check record matching: Identify a metric by definition/version, dimensions, time window, and entity key. Verify field coverage, deletion handling, and how changes are detected.

Native vendor integration

Best fit: A vendor-built integration may fit if it supports your Adobe Analytics and Google Cloud SQL record types.
Operating responsibility: Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
Before you choose: First check whether either vendor offers this integration. If available, verify Metrics / Proposed metric or analytical result table in Google Cloud SQL (choose its name), update direction, account tier, and related-record handling.

Custom API or workflow

Best fit: Consider when Adobe Analytics and Google Cloud SQL need a transformation, approval, or action outside a direct record sync.
Operating responsibility: Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
Before you choose: Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.

File or scheduled snapshot

Best fit: Consider for a one-time Adobe Analytics / Google Cloud SQL migration or a reporting need with an explicit freshness window.
Operating responsibility: Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
Before you choose: Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.

Workflow scenarios and expected results

Metric or analytical result reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Metrics or Proposed metric or analytical result table in Google Cloud SQL (choose its name) record needs a defined result in the other system.

Start with Adobe Analytics Metrics and Google Cloud SQL Proposed metric or analytical result table in Google Cloud SQL (choose its name). Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve entity keys, time zones, aggregation grain, and any currency/unit conversions.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

Expected result: Compare identical time windows and dimensions; test late-arriving data and a recalculated metric.

If it fails: Recompute the intended window before retrying an output; avoid overwriting a newer result with an older computation.

Application user or identity reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Users or Proposed application user or identity table in Google Cloud SQL (choose its name) record needs a defined result in the other system.

Start with Adobe Analytics Users and Google Cloud SQL Proposed application user or identity table in Google Cloud SQL (choose its name). Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve tenant and group references and establish a protected administrative-account policy.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

Expected result: Test a renamed login, disabled account, missing group, and a user existing in two tenants.

If it fails: Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.

Compare Adobe Analytics and Google Cloud SQL reporting data

This is an evaluation scenario; connector and operation support require confirmation.

Starting event: An analytical result from Adobe Analytics must be reconciled with or displayed alongside a selected destination dataset.

Select Metrics or Calculated Metrics and record what each row measures, how records are grouped, the timezone, and the reporting cutoff.
Map the analytical entity key to the business record in Google Cloud SQL; an aggregate row is not equivalent to an individual transaction.
If an output will be written back, name its owner and carry a computation timestamp so stale results can be recognized.

Expected result: Totals compare the same time window and level of detail; late-arriving data produces a traceable revision rather than an unexplained overwrite.

If it fails: Recompute the selected window and check entity mapping before sending the result again.

Initial load and acceptance testing

Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.

Metrics / Proposed metric or analytical result table in Google Cloud SQL (choose its name)

Test case

Compare identical time windows and dimensions; test late-arriving data and a recalculated metric.

Expected result

The expected metric or analytical result relationship is preserved with no duplicate action or unintended write.

Users / Proposed application user or identity table in Google Cloud SQL (choose its name)

Test case

Test a renamed login, disabled account, missing group, and a user existing in two tenants.

Expected result

The expected application user or identity relationship is preserved with no duplicate action or unintended write.

Direction and permissions

Test case

Bring an example source record and the intended destination operation to the compatibility review. Confirm the supported route before granting write access.

Expected result

Only an approved, supported direction and permitted fields are written.

Freshness and reconciliation

Test case

Measure source and destination times for the selected records under normal load and a burst. Reconcile IDs and values using the same filters and cutoff.

Expected result

The process meets its agreed freshness target and reconciliation has no unexplained differences.

Failed updates, retries and recovery

Start with the failed record and the destination error, then inspect the source value, field requirements, and access.

Rejected or repeated metric or analytical result change

Investigate

Inspect Adobe Analytics Metrics and Google Cloud SQL Proposed metric or analytical result table in Google Cloud SQL (choose its name), their IDs, and the destination error.

Next action

Recompute the intended window before retrying an output; avoid overwriting a newer result with an older computation.

Rejected or repeated application user or identity change

Investigate

Inspect Adobe Analytics Users and Google Cloud SQL Proposed application user or identity table in Google Cloud SQL (choose its name), their IDs, and the destination error.

Next action

Review access impact before retrying a lifecycle change; reconcile current identity state and retain an approval trail.

A record type or update is unavailable

Investigate

Check the Adobe Analytics and Google Cloud SQL connector guides, account permissions, and any operations marked On Request.

Next action

Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.

Source and destination disagree after a retry

Investigate

Compare current source values, destination validation, identity mappings, and any side effects already completed.

Next action

Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.

Read the Stacksync issues dashboard guide for retry and revert behavior.

Change detection and update delivery

How updates move between Adobe Analytics and Google Cloud SQL

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

Adobe Analytics Google Cloud SQL Direction requires confirmation

Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.

Apply updatesConfirm that Stacksync can create or update the records you need in Google Cloud SQL.

Google Cloud SQL Adobe Analytics Direction requires confirmation

Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.

Apply updatesConfirm that Stacksync can create or update the records you need in Adobe Analytics.

Update timing and record limits

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
Review write-back, deletion handling, update timing, and account limits with the integration team.
Review write-back, deletion handling, update timing, and account limits with the integration team.

FAQ