Hi HN, We're Frank, Abhilash, and Arun from Castled Data(https://castled.io). Castled is a marketing automation platform built directly on top of modern data warehouses like Snowflake, BigQuery, Redshift, and Postgres. Here is a quick demo:
We started our journey by building an open-source Reverse ETL solution to make the warehouse data actionable to marketers. However, after talking to 100s of marketers, we realised that modern B2C marketers needed to use billions of customer data points from the data warehouse to run personalised marketing campaigns. While Reverse ETL could sync all this data to marketing tools like Braze, Iterable, etc., the traditional marketing solutions were fundamentally not designed to store and process so much data.
The restrictions surfaced in the form of illogical data volume-based pricing and restrictive data retention - usually just a few months. Moreover, copying the data to multiple external tools created more complications w.r.t security audits(e.g., SOC2, GDPR, etc.) and other data privacy protocols. The data engineers also faced numerous issues maintaining the Reverse ETL pipelines, which often failed due to API timeouts, rate limits, etc.
That's when we changed course to build a warehouse-native marketing solution. Since we do not store any customer data with us, marketers could finally use all the data from the data warehouse to engage their customers without any tradeoffs. The headless approach also enabled us to offer marketers a more rational pricing model.
Marketing automation solutions have always been built on top of databases, which allowed transactional queries and point lookups. So solving the same use cases on top of a data warehouse that only supports analytical workloads would typically be a significant challenge. But luckily for us, 95% of the B2C marketing use cases required bulk access patterns, which data warehouses were uniquely designed to solve and even enabled us to offer capabilities that were not possible with traditional solutions.
One major challenge was supporting real-time campaigns, where we had to react to product events in sub-second latency using data from Snowflake. This required point lookups in Snowflake on each event, which was not scalable. We had to solve it using "contextual" data caches on our side. But we expect the advent of technologies like Unistore to make it easier to support use cases that require low-latency lookups on top of the data warehouse.
We have been building Castled for a year. Until now, we have enabled many businesses to solve their marketing data problems by replacing the combination of Reverse ETL + B2C marketing solutions with Castled. So, if you have a data problem in marketing, feel free to sign up and try our product for free - no credit card required. We have our solution hosted at https://castled.io.