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Improve the usefulness of your dbt models | Census

Boris Jabes
Boris Jabes August 26, 2020

Boris is the CEO of Census. Previously, he was the CEO of Meldium, acquired by LogMeIn. He is an advisor and alumnus of Y Combinator. He enjoys nerding out about data and technology, 8-bit graphics, and helping other startup founders.

I’m excited to announce that we’ve added a new integration for dbt projects in Census. With our new native dbt support, you can take advantage of shared query logic across all your models,  and create “last-mile” models that are automatically synced into apps like Salesforce, Marketo, etc. This is the first data modeling integration we’ve ever built in Census and we think it’s the first of its kind for dbt too.

We’ve mentioned dbt as an emerging piece of technology in the modern data stack. For those of you who aren’t familiar, dbt is a tool that helps you write & run SQL transforms in your data warehouse. The key benefit of using dbt is that your SQL is stored in version control, which means there’s a history of all changes. This makes it much easier for teams to collaborate. The other key feature of dbt is that you can create shared logic for your SQL code (e.g. you can build intermediate views on your data or you can write macros for repetitive parts of your queries). All of this makes it an ideal tool for the modern data stack where you can maintain all the code that cleans & prepares your data for action.

Census can now materialize select dbt models directly into your external tools like Salesforce, Marketo, Customer.io, etc.

As of today in Census, you can connect to a dbt project stored on GitHub, select models whose output you’d like to make available for publishing, and take advantage of our sync engine to get the data into the hands of your business teams. Let’s walk through what this looks like in practice.

  1. Connect to GitHub. When you go to the Models tab, you’ll be prompted with a choice to either store & edit SQL directly in Census or connect to GitHub and select a repository where you keep your dbt data models. You don’t need to create a new project for Census – you can keep all your transform logic in one repository.
  2. Select models for Census. By default when you connect a new dbt project, Census will only look for models that have been tagged with the keyword census. This way you can decide which models or groups of models should be available for Census to sync into other applications (most of us don’t want to sync our internal staging transforms). You can configure this selector for your project (e.g. path:/models/publish).
  3. Sync models to destinations. The best part about our integration is that you don’t have to think about managing yet another materialization schedule. We automatically respect your existing setup, whether your dbt runner is in-house or in dbtCloud. The models you expose to Census are treated as ephemeral for our syncs so they can reference your other models without having to think about their materialization. In other words, you can think of Census as responsible for materializing these models (incrementally) into your external tools like Salesforce, Customer.io, etc.

As you can see in this video, it's easy to connect existing dbt projects with Census & to take advantage of our integration. If you would like to migrate your existing Census models to a dbt project, reach out to our team or your dedicated CSM.

PS: We plan to do a lot more with dbt in the months to come so stay tuned :-)

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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: