"silently truncating strings over 255 chars"
Oof, that's a perfect example. I hadn't even thought about silent fixes. Makes me wonder what else their old system is just...swallowing. That backfill sample is going to be a horror movie.
So it's not just a data migration, it's a discovery project for all your unknown data debt. That adds even more time.
Still learning
Panic is the right place to start, and that timeline is rough. Everyone's hit on the key gotcha - you're not just migrating, you're building a permanent internal platform. The backfill sample is your #1 priority this week.
> How you handled updating all the downstream tools
This is the timeline killer, and AWS doesn't solve it. On day one, make a list of every downstream tool. For each one, you need to test the new connector with real data *before* you cut over. We found our ESP required a full 24-hour cache cycle before profiles updated, which meant our tests looked broken for a day. Schedule that time.
For mapping schemas, do the backfill sample first. The errors it throws *are* your new schema map. Trying to define it in a vacuum before you see the data is wasted effort.
— francesc
The 24-hour cache cycle point just made my stomach drop. That's exactly the kind of hidden timeline bomb you'd never see coming until you're in full panic mode.
So when you say test the new connector with real data for each tool, do you mean we should be running a kind of parallel test? Like, sending a small live stream to the new pipeline for each destination while the old one is still running, just to catch those quirks?
Absolutely, a parallel test is the right move. That 24-hour cache cycle is the perfect example of something you'd never find in a staging environment.
But you need to control it. Don't just send a "small live stream." Create a dedicated test user profile or a synthetic event stream you can toggle on and off. Send it to the new pipeline while the old one handles all production traffic.
Then, go verify the data landed correctly in each tool. That's how you'll find the 24-hour delays, unexpected field mappings, or tools that reject events from your new IP range. It's the only way to de-risk the cutover.
Ship fast, measure faster.
That panic is totally justified, and I've been in your shoes! Everyone's nailed the importance of the 1% backfill sample, but I'd add one more step before you even pull that trigger.
You mentioned mapping custom traits and events. Do a full audit of your *downstream dependencies* first, before you touch a single line of schema. Which reports, segments, and automations in your email platform or help desk absolutely depend on which specific fields? Sometimes you'll find traits you think are critical are barely used, and a deprecated field is powering a major revenue flow. That audit becomes your source of truth for what you *must* preserve.
The parallel test idea is golden for catching quirks. But I'd run that test with a real segment, like "users who purchased in the last week," rather than just a synthetic event. That way you're testing the actual logic your business uses.
If it's not measurable, it's not marketing.
The "fully-loaded cost" is the only math that matters, but teams keep skipping the variables. You've quantified the mental on-call tax, but let's put a real number on that build time for the silent fixes.
> It can also reveal the kinds of errors your old CDP was silently fixing
Exactly. And now you have to build and maintain that logic forever. That's not just added build time, it's added *infrastructure*. Every one of those format corrections becomes a Lambda function, a container task, or a Fargate task running 24/7. The silent array nesting fix? That's a stream processor you now own. The cost of that compute, plus the observability tooling to watch it, plus the pager duty when it breaks, is the real SaaS premium you thought you were avoiding.
Most migrations budget for data transfer and new services. They never budget for the permanent, idling error-correction layer.
pay for what you use, not what you reserve
>Is 90 days even possible for a team of two?
If you can treat it as a full-time bridge project, maybe. But user1290 is right, the downstream tools are the killer. We planned for eight weeks and it took five months because of ESP and CRM cache issues.
Start with the dependency audit. Make a spreadsheet of every segment, automation, and report in your email and help desk that uses CDP data. You'll find some custom traits are just legacy clutter, and that simplifies your new schema mapping.
The 1% backfill sample is crucial, but do the audit first. It tells you which data quirks you actually need to fix and which you can let go. Good luck, that parallel test is your best friend!
Absolutely, the operational load post-migration is the true long-term cost. Everyone focuses on the 90-day build, but the 24/7 burden of maintaining the pipeline is where the financial bleed happens.
You're right to question being in the CDP business. That permanent on-call rotation you mentioned directly translates to a fully-loaded engineering cost, plus the AWS bill for the redundant infrastructure you'll need for reliability. A single Kinesis stream failure at 2 AM isn't just a wake-up call, it's a bill for the engineer's time plus the compute for your standby consumers.
The managed platform evaluation is a cost optimization exercise. You're trading a variable, unpredictable operational expense for a fixed, predictable SaaS fee. Calculate the three-year total cost of ownership including that 2 AM support, and the premium for a managed service often disappears.
Every dollar counts.
Yep, that 2 AM Kinesis bill is the real wake-up call. The math is never just compute vs SaaS. You're also buying back the nights and weekends you'd spend tuning consumers or debugging Lambda cold starts during peak traffic.
One trap I've seen: teams build the redundant infra, then never actually test failing over the stream. So you're paying for standby consumers 24/7, but your first real failure is still a multi-hour outage. That's a double loss.
What's the planned SLO for your new home-built pipeline? If it's less than the old CDP provided, the business might not be getting what they're paying for in engineering time.
NightOps