Materialize

“"That's one of the biggest things. The data is essentially in a centralized place. If you need data for this thing, go grab it. You no longer need to negotiate to say I need this data, can you write this API endpoint for me, and then negotiate how long it takes to get there."”

“"When you're counting transactions, all you care about is what changed in the last twenty-four hours, or even the last hour. There's really no reason to keep this history of everything in memory forever."”

““Given the transaction ID, I want to know everything about the transaction and the user and the funding method.””

“"It never worked, and there was no way for the product to tell us what we had to change."”

“"One of the biggest weaknesses is outage recovery. When one of the streams is out for two hours, you have to go back and figure out what everything looked like. It's easy when you do it offline. When you have to do it online, you essentially hold the world and wait for that stream to come back. And if the outage went for twenty-four hours and it went past your memory and your buffer space, you're in a pretty doomed position."”

“"The only reason streaming joins didn't work is that they don't work when you have twenty. It just doesn't work that way."”

“"I need a new field to indicate whether this is a new user. Go talk to the user team, and that team will tell you they can give you an API endpoint in two months if you're lucky. And then the conversation just stops there."”

““The fraud system needs that context in real time. If we see the same pattern showing up, we need to discover it within seconds, not wait for offline processing thirty or sixty minutes later.””

““At one moment a transaction record gets written into the database and breaks into ten, twenty tables. And then about ten milliseconds later we join them all back into the same original form.””

““We were already paying for one of the largest instances available, and it still could not keep up. Every time we broke the record for our best day, that was when the strain showed up.””

“Every week, we’re closing customers that we wouldn’t even have had the opportunity to contact before using Materialize”

“We decided to look into Materialize, and by that evening we were up and running. And it wasn’t long before we started to see how it could be used in Production.”

“I never have to think about Materialize, which is great for a one-person data engineering team.”

“Only about two weeks worth of work was required to go from first conversation about Materialize to customer-facing location tracking.”

“The audience builder doesn't sound like much, but it powers key components of our hospitality engine and offers platform, which are central to our business.”

“Recommendation quality is a data quality problem. Omnisearch works because the index is never stale — every attribute is up to the second.”

“The agent doesn't reassemble the world anymore. It knows what's being asked, the context is already there, and it just goes and gets the answer.”

“We build real-time audiences, emit change events, and use those events to trigger a chain reaction of offers — all in SQL.”

“We were able to build both a transactional and an event-based system in one go.”

“We can control the world in SQL, using familiar technologies like dbt. We can have sub-second freshness rather than an hour. And we can have query latencies in single-digit milliseconds.”

“After a few months we killed the streaming pipeline approach. The framework worked; our velocity didn't.”

“Getting all those attributes in one place meant point-to-point integrations across hundreds of services. That's slow, manual, throwaway work — every team rebuilding what another team already built.”

“As members go about their day, we can identify the offers they're eligible for and trigger them instantly — nationwide, in real time.”

““Getting Data flowing in from Kafka was a breeze.””

“Materialize is one of the most pleasant vendors that we work with. Fast, thorough support responses make a huge difference.”