Product News

Sync Snowflake data continuously with Snowflake Stream Triggers | Census

Katy Yuan
Katy Yuan June 13, 2022

Katy is a Product Marketing Manager at Census who loves diving into startups, SaaS technology, and modern data platforms. When she's not working, you can find her playing pickleball or Ultimate Frisbee.

Data warehouses are becoming more than passive central data stores– they’re actively interacting with the other technologies you use to automate workflows and personalize experiences for your customers.

Snowflake is advancing the functionality of the modern cloud data warehouse with the ability to call external services, using External Functions. Combined with Snowflake’s ability to capture changes to tables and views natively via table streams, you can use these External Functions for a more efficient, cost effective way to orchestrate your reverse ETL pipelines.

In sync with Snowflake Summit in Las Vegas today, we’re excited to announce support for Snowflake Stream Triggers to kick off a reverse ETL sync only when your data changes, saving customers time and credits.

This is especially useful for data that's updated on an irregular frequency but needs to be synced ASAP.

Why it matters

Using Snowflake Stream Triggers will save you Snowflake credits when you need to extract data as quickly as possible from your Snowflake warehouse, but data is updated more frequently than your batch-based, orchestrated data pipelines. If you have low latency use cases and spiky workloads that insert and update data in your data warehouse, you can have Census listen for Snowflake tables and view changes to kick off a sync – instead of polling continuously for changes.

Snowflake’s external functions don’t need to wake up a logical warehouse to run, and are significantly less credit-intensive than running an extra small warehouse continuously to poll frequently for changes. We estimate that using Snowflake Stream Triggers to activate Census syncs can be 10-100x cheaper than traditional orchestration methods.

For batch workflows, Census has a full-featured API to trigger syncs and integrations with your favorite orchestrators like Airflow, Prefect, and Dagster. In these scenarios, Census will wake up your logical data warehouse only when a series of upstream transformations have completed. No need for complex orchestration? No worries – you can set Census on a schedule, and similarly, Census will run regularly and auto-suspend its warehouse when done.

However, what if you need to forward events to destinations so you can trigger time-sensitive workflows like Slack alerts and abandoned cart campaigns? In these cases, you need a fast-path for data that is being streamed into Snowflake and updated continuously. With Snowflake Stream Triggers, Census listens for any changes to your streams and only turns on the logical warehouse when there’s a valid change to sync to your destinations.

In conclusion

Historically, reverse ETL pipelines are either orchestrated explicitly or poll for changes on a regular schedule. If you needed data streams synced as quickly as possible from Snowflake, your best option was to poll your data warehouse frequently to check for changes – keeping a logical data warehouse running and spending credits the entire time.

With Snowflake Stream Triggers for Census, that’s no longer the case. We believe this is a strictly superior way to accomplish continuous syncs that's both cheaper and faster than previous methods.

Interested in trying it out? Get a demo or start your free trial now.

Meet us at Snowflake Summit Booth #1829

If you're at Snowflake Summit this week, see us at Booth #1829 or join our mixers tomorrow:🥞 Data Stack Breakfast with Mixpanel Tuesday June 14th, 7:30 - 9:00 AM PDT

🥂 Happy Hour with Airbyte and DeepnoteTuesday June 14th, 6:30 - 9:30 PM PDT

If you can't make it, we're giving out some sweet raffle prizes anyway 🙌

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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: