Engineering

Integration platforms are an old idea | Census

Brad Buda
Brad Buda February 25, 2022

Brad is a co-founder of Census who doubles as a software developer in San Francisco. Prior to founding Census, he co-founded Meldium, a YC-backed startup helping teams & companies manage accounts & passwords for cloud apps. San Francisco, California, United States

Integration platforms have gained popularity since Web 2.0 and, thanks to REST APIs and webhooks, the ecosystem has developed over the last decade so much to the point that Zapier is a household name. Unfortunately, most integrations evolved without consideration for other evolutions happening in parallel—namely the accessibility and power of a data warehouse as a central source of truth.

At Census, we didn’t break any new ground by saying System A should be able to talk to System B, even if they aren’t designed to do so. So what makes Census different?

3 key tenets make Census different from legacy integration platforms:

  1. All data should be marshaled through a central source of truth
  2. The best central source of truth is a data warehouse
  3. Integrate data, not events

Let's break these down:

Marshal data through a central source of truth: Data teams achieve scale through simplicity and manageability. We talk to a lot of companies who did the easy thing–started connecting SaaS apps directly to each other via point to point–and are now tangled up in a web of undocumented and unmanageable integrations.

The best central source of truth is a data warehouse: The SQL/relational ecosystem is arguably the richest in all of computing (right up there with TCP/IP), and while SQL has its warts, it's pragmatic, widely used, and going stronger than ever.

SQL is standards-based while still being extensible and dynamic, widely supported, easy to hire for, and inspectable. And there are a tremendous number of tools you can bring to bear on things that speak SQL.

Integrate data, not events: Legacy iPaaS are designed to capture changes that originate in System A and forward them as actions to System B. This model is contained by what A and B decide to generate and consume. Operational Analytics is different in that it’s data-oriented, not action-oriented.

The ELT approach nails this. You read high-fidelity data out of your source systems and into your warehouse before you perform any transformations, even trivial ones.

Census, and Operational Analytics by way of reverse ETL integration, is the logical extension of this—instead of trying to actuate the destination system to get it to some desired state and being constrained by that system's levers and buttons, just load the data you want into that system's tables directly.

Salesforce is successful not because it’s a great CRM, but because it’s a good-enough CRM that's programmable. Most SaaS applications are much less programmable, and nobody wants to learn dozens of different, proprietary SaaS programming languages.

The good news is you can reprogram your SaaS today, and you can do it using a tool you already have (your warehouse) combined with Census to operationalize your data and to put exactly the bits you want into your SaaS tools directly.

This was the aha! moment our team had way back when we were writing our first lines of code, and our conviction in this idea has only grown stronger since.

Census is a deeply transformative technology and our customers are just beginning to tap that power. Want to be a part of this transformation? I'm hiring engineers of all levels - come join me and let's be part of this together.

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For years, working with high-quality data in real time was an elusive goal for data teams. Two hurdles blocked real-time data activation on Snowflake from becoming a reality: Lack of low-latency data flows and transformation pipelines The compute cost of running queries at high frequency in order to provide real-time insights Today, we’re solving both of those challenges by partnering with Snowflake to support our real-time Live Syncs, which can be 100 times faster and 100 times cheaper to operate than traditional Reverse ETL. You can create a Live Sync using any Snowflake table (including Dynamic Tables) as a source, and sync data to over 200 business tools within seconds. We’re proud to offer the fastest Reverse ETL platform on the planet, and the only one capable of real-time activation with Snowflake. 👉 Luke Ambrosetti discusses Live Sync architecture in-depth on Snowflake’s Medium blog here. Real-Time Composable CDP with Snowflake Developed alongside Snowflake’s product team, we’re excited to enable the fastest-ever data activation on Snowflake. Today marks a massive paradigm shift in how quickly companies can leverage their first-party data to stay ahead of their competition. In the past, businesses had to implement their real-time use cases outside their Data Cloud by building a separate fast path, through hosted custom infrastructure and event buses, or piles of if-this-then-that no-code hacks — all with painful limitations such as lack of scalability, data silos, and low adaptability. Census Live Syncs were born to tear down the latency barrier that previously prevented companies from centralizing these integrations with all of their others. Census Live Syncs and Snowflake now combine to offer real-time CDP capabilities without having to abandon the Data Cloud. This Composable CDP approach transforms the Data Cloud infrastructure that companies already have into an engine that drives business growth and revenue, delivering huge cost savings and data-driven decisions without complex engineering. Together we’re enabling marketing and business teams to interact with customers at the moment of intent, deliver the most personalized recommendations, and update AI models with the freshest insights. Doing the Math: 100x Faster and 100x Cheaper There are two primary ways to use Census Live Syncs — through Snowflake Dynamic Tables, or directly through Snowflake Streams. Near real time: Dynamic Tables have a target lag of minimum 1 minute (as of March 2024). Real time: Live Syncs can operate off a Snowflake Stream directly to achieve true real-time activation in single-digit seconds. Using a real-world example, one of our customers was looking for real-time activation to personalize in-app content immediately. They replaced their previous hourly process with Census Live Syncs, achieving an end-to-end latency of <1 minute. They observed that Live Syncs are 144 times cheaper and 150 times faster than their previous Reverse ETL process. It’s rare to offer customers multiple orders of magnitude of improvement as part of a product release, but we did the math. Continuous Syncs (traditional Reverse ETL) Census Live Syncs Improvement Cost 24 hours = 24 Snowflake credits. 24 * $2 * 30 = $1440/month ⅙ of a credit per day. ⅙ * $2 * 30 = $10/month 144x Speed Transformation hourly job + 15 minutes for ETL = 75 minutes on average 30 seconds on average 150x Cost The previous method of lowest latency Reverse ETL, called Continuous Syncs, required a Snowflake compute platform to be live 24/7 in order to continuously detect changes. This was expensive and also wasteful for datasets that don’t change often. Assuming that one Snowflake credit is on average $2, traditional Reverse ETL costs 24 credits * $2 * 30 days = $1440 per month. Using Snowflake’s Streams to detect changes offers a huge saving in credits to detect changes, just 1/6th of a single credit in equivalent cost, lowering the cost to $10 per month. Speed Real-time activation also requires ETL and transformation workflows to be low latency. In this example, our customer needed real-time activation of an event that occurs 10 times per day. First, we reduced their ETL processing time to 1 second with our HTTP Request source. On the activation side, Live Syncs activate data with subsecond latency. 1 second HTTP Live Sync + 1 minute Dynamic Table refresh + 1 second Census Snowflake Live Sync = 1 minute end-to-end latency. This process can be even faster when using Live Syncs with a Snowflake Stream. For this customer, using Census Live Syncs on Snowflake was 144x cheaper and 150x faster than their previous Reverse ETL process How Live Syncs work It’s easy to set up a real-time workflow with Snowflake as a source in three steps:

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