Castled – Marketing automation using customer data from Snowflake
Hi HN! We are excited to share something we have been building for the last 12 months. Castled is a warehouse-native marketing platform built directly on cloud data warehouses like Snowflake, BigQuery, Redshift, and Postgres. Castled allows you to directly use the customer data in your data warehouse and engage them across different channels like Email, Sms, WhatsApp, push, and In-app - without having to copy the data to another tool. 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…
Castled – Marketing automation using customer data from Snowflake launched on February 6, 2023 with 8 votes, #1,489 of 3,829 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
What it does
In the maker’s words, at launch
Hi HN! We are excited to share something we have been building for the last 12 months. Castled is a warehouse-native marketing platform built directly on cloud data warehouses like Snowflake, BigQuery, Redshift, and Postgres. Castled allows you to directly use the customer data in your data warehouse and engage them…
Does a similar job
all alternatives →- CDCastled Data – Customer engagement software on top of data warehouses2023 · ▲10
Hi HN, We're Frank, Abhilash, and Arun from Castled Data(https://castled.io). Castled is a Customer Engagement platform built directly on top of cloud data warehouses like Snowflake, BigQuery, Redshift, and Postgres. In essence, Castled is identical to Braze and Iterable with a crucial difference - we don't keep a copy of your customer data. Instead, we enable marketers to create audiences directly on their cloud data warehouse and engage them across channels like Email, Sms, WhatsApp, Push, and In-app notifications - no SQL knowledge, no engineering favours required. Here is a…
PylarDec 2025 · pylar.ai · ▲502Hey HN! We're Hoshang & Vishal, the team behind Pylar - a governed access layer between databases and LLMs. We previously led data and AI and we kept seeing the same problem across teams using LLMs internally: agents are great with unstructured data, but the moment you want them touching your actual systems of record — Snowflake, Postgres, CRMs, product DBs — everything becomes fragile, risky, or outright unsafe. Two issues show up every single time: 1. Agents over-querying They don’t understand cost. They’ll happily generate queries that blow up your warehouse bill. 2. Accidental data…
Snowflake Native Amplitude2024 · amplitude.com · ▲201Self-serve product analytics built for the data cloud
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Source: Show HN post ↗ · launch text quoted in part
