Hi everyone, I'm deep into an evaluation project for our ad platform stack and have hit a research hurdle I'm hoping the community can help with.
We're standardizing our marketing data into Snowflake and Microsoft Ads is the next, and seemingly most complex, source to bring in. The goal is clean, reliable cost data for TCO analysis and cross-channel reporting. I've been scouring documentation and vendor materials, but the practical, day-to-day realities aren't always clear there.
Before we commit to a development path or a third-party tool, I need to thoroughly understand the landscape. I have a series of questions about the various ingestion methods:
**Regarding the direct Microsoft Ads API approach:**
* What's the real stability like for the Budget, Campaign, and Keyword Performance reports? We need at least daily pulls without unexpected schema changes or data gaps.
* For those who built a custom connector, what was the actual implementation timeline from kickoff to reliable production data? Were there hidden hurdles in authentication or rate limiting that slowed things down?
* How granular and consistent is the cost data? Does it tie back accurately to invoices at the account level?
**Regarding ETL/ELT platforms (like Fivetran, Stitch, etc.):**
* Do these vendors typically handle Microsoft Ads as a fully managed source, or is it more of a "bring your own report" configuration?
* How do they manage the data transformation *before* Snowflake? Are we locked into their cost data model, or can we easily map to our existing fact tables?
* What are the typical review cycles for their connector updates when Microsoft changes its API?
**Regarding using the Ads Data Hub or other Microsoft first-party tools:**
* Is there a viable path from these tools into a Snowflake instance we own, or do they lock you into a specific ecosystem?
* What's the implementation process like from the procurement and legal side? Are there lengthy contract negotiations or restrictive data usage terms to watch for?
**My core concerns are:**
* **Total Cost of Ownership:** Weighing the build (ongoing maintenance, developer time) versus buy (subscription fees, potential per-row charges) models.
* **Exit Strategy:** If we start with a third-party ETL tool, how portable is the pipeline logic? Is it vendor-locked?
* **Timeline:** We need a clear comparison between the time-to-value for each approach.
Any insights from your own implementations—what worked, what broke, and what you wish you'd known before starting—would be incredibly valuable. Even pointing out which questions I should be asking but have missed would be a huge help.
Having built a few of these pipelines, I can speak to the direct API route. The stability for the core reports like Budget and Campaign Performance is generally good, but you'll encounter schema drift a few times a year, usually with the addition of new fields rather than breaking changes to existing ones. The real stability risk is in the more granular entities, like Keyword Performance, where attribution windows and feature deprecations can subtly alter data semantics.
On implementation, a basic proof-of-concept connector is deceptively quick, maybe two weeks. Reaching "reliable production data" is a different beast. The hidden hurdle is the error handling and backfill logic for when the API's internal aggregation lags, which it does. You need a robust idempotent retry mechanism for your daily pulls. The OAuth flow is standard, but the rate limits, particularly for large accounts with detailed historical queries, will force you into a multi-day batch pattern for initial loads.
Cost data granularity is precise at the campaign level and below, tying to invoices accurately. The inconsistency arises from how and when Microsoft applies post-campaign adjustments, like fraud clawbacks, which can appear days later and require you to overwrite previous data. Your pipeline must handle these late-arriving facts without duplicating spend.
Latency is the enemy