SQL Server CDC to Snowflake + ClickHouse in Real Time (Fan-Out Demo)
See how to capture SQL Server change data once and fan it out to Snowflake, ClickHouse, and other destinations without creating duplicate source pipelines.
In this DBTA roundtable webinar, Estuary demonstrates a capture-once, fan-out architecture using change data capture (CDC).
The demo starts with an existing SQL Server to Snowflake pipeline for scheduled BI workloads, then adds ClickHouse as a real-time destination using the same captured collections—without re-reading the SQL Server source.
You’ll see how Estuary handles durable collections, batch vs. real-time sync schedules, backfills, schema mapping, merge vs. delta updates, and exactly-once delivery.
⏱️ Chapters
0:00 Introduction
2:08 Capture once, fan out architecture
2:53 Why multiple destinations add complexity
3:44 How the fan-out pattern works
6:40 SQL Server to Snowflake demo
8:37 Sources, collections & destinations
10:11 Inspecting CDC records
13:59 Snowflake pipeline
15:39 Adding ClickHouse for real-time workloads
18:16 ClickHouse ingestion options
18:53 Configuring the ClickHouse connector
20:38 Real-time vs. batch sync
25:14 Publishing & initial backfill
28:59 Real-time data in ClickHouse
30:27 Fan-out architecture complete
31:09 More destinations & migration use cases
32:29 Q&A: transforms, latency & deduplication
🔑 What you'll learn
* How to capture CDC data once and serve multiple destinations
* How Snowflake and ClickHouse can consume the same data at different frequencies
* How to add destinations without re-reading the source database
* How Estuary handles backfills, CDC updates, and exactly-once delivery
🔗 Try Estuary free: https://dashboard.estuary.dev/register
📚 Docs: https://docs.estuary.dev
#ChangeDataCapture #ClickHouse #Snowflake #DataEngineering #RealTimeData
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Learn how to load your data into MotherDuck—cloud-based DuckDB—with Estuary. We’ll cover a little about what makes DuckDB unique before diving into a step-by-step demo. Whether you're building a real-time pipeline from NetSuite, Snowflake, BigQuery, PostgreSQL, or MongoDB to MotherDuck, Estuary lets you do it in minutes — no code required. Find more: - Register at Estuary: https://dashboard.estuary.dev/register - Sign up with MotherDuck: https://app.motherduck.com/?auth_flow=signup - Read Estuary’s docs on MotherDuck: https://docs.estuary.dev/reference/Connectors/materialization-connectors/motherduck/ - Follow along with MotherDuck’s tutorial on working with Estuary: https://motherduck.com/blog/streaming-data-to-motherduck/ Media resources used in this video are from Pexels and the YouTube Studio Audio Library. 0:00 Intro 1:01 DuckDB's features 1:45 MotherDuck 2:16 Connector demo with Estuary 2:57 Setting up AWS resources 3:46 Setting up MotherDuck 4:23 Publishing the connector 4:53 Outro #motherduck #datapipeline
PostgreSQL to Iceberg - Streaming Lakehouse Foundations
Stream Real-Time Data from Postgres to Iceberg with Change Data Capture and Estuary. In this step-by-step tutorial, we demonstrate how to set up and stream real-time data from a PostgreSQL database into Iceberg tables using change data capture (CDC) with Estuary. Learn how to capture, ingest, and materialize data using Estuary's seamless integration. This demo uses a sales database to showcase how changes in a PostgreSQL table are tracked and replicated into an Iceberg table stored in AWS S3. Check out Estuary's Iceberg integration: https://estuary.dev/destination/s3-iceberg/ Join Estuary's community Slack: https://estuary-dev.slack.com/join/shared_invite/zt-86nal6yr-VPbv~YfZE9Q~6Zl~gmZdFQ#/shared-invite/email 00:00 - Introduction: Streaming Data from Postgres to Iceberg 00:18 - Postgres Sales Database Overview 01:08 - Starting Change Data Capture (CDC) with Estuary 02:09 - Materializing Data into Apache Iceberg 04:17 - Backfilling Data into Iceberg 05:21 - Querying Iceberg Tables with Python 06:10 - Conclusion: Demo Recap
Change Data Capture for PostgreSQL with Estuary
In this quick tutorial, Dani demonstrates how to effortlessly set up a PostgreSQL Change Data Capture (CDC) pipeline using Estuary in less than a minute. Watch as he works with a live sales table, showing you just how simple it is to connect your Postgres database and start replicating data in real time. You'll also see how Estuary handles schema capturing and backfills your existing data, making real-time data integration both fast and efficient. #PostgresCDC #changedatacapture Try Estuary today for real-time data pipelines and seamless integration with your databases! - Sign up for a free account: https://dashboard.estuary.dev/register - Join our Slack community: https://estuary-dev.slack.com/join/shared_invite/zt-86nal6yr-VPbv~YfZE9Q~6Zl~gmZdFQ#/shared-invite/email - Make sure to check out our Postgres CDC guide: https://estuary.dev/the-complete-change-data-capture-guide-for-postgresql/ Key things covered: 0:00 – Introduction: Setting up PostgreSQL CDC Pipeline 0:09 – Sales Table Example and Real-Time Updates 0:21 – Creating a Postgres Capture in Estuary 0:33 – Verifying the Sales Table and Schema 0:44 – Backfilling Data and Real-Time Replication
Stream Real‑Time Data to Databricks with Estuary
Learn how to stream real-time data from PostgreSQL into Databricks using Estuary — no code, no maintenance. In this demo, Dani walks through: • Setting up a Databricks SQL Warehouse and generating a personal access token • Capturing the users and transactions tables from PostgreSQL in Estuary • Materializing those tables directly into Databricks using the built-in connector • Monitoring live data replication and verifying the results in Databricks SQL Warehouse Highlights of Estuary: • Real-time change data capture with millisecond latency • Native support for Databricks Unity Catalog and Delta Lake • Zero-code pipeline setup with automatic backfill and continuous sync Why it matters Streaming live data into Databricks unlocks fresh analytics, real‑time dashboards, and feeding ML models with up‑to‑date data — all without complex ETL or scripting 🔗 Learn more & get started: • Official Estuary guide: https://estuary.dev/real-time-fraud-detection-databricks/ • https://estuary.dev/blog/load-data-into-databricks/ • Start building for free at: https://dashboard.estuary.dev/register If you have questions or need help, jump into our community Slack or check the docs. #databrickstutorial #databricks 00:00 - Introduction 00:49 - Materializing Data to Databricks 01:45 - Verifying Data in Databricks 02:04 - Conclusion
Estuary Overview
Discover the power of Estuary, a platform built to make creating real-time data pipelines easy. In this overview, we’ll show you how Estuary helps you move data from source to destination in real time, with no coding required. 🌐 Check out our website to learn more about Estuary: https://www.estuary.dev/ ➡️ Start building your pipelines for free now: https://dashboard.estuary.dev/register if you’re curious for more, check out our docs or jump into our community Slack to ask questions! 📚 Explore our docs for detailed guides and tutorials: https://docs.estuary.dev/ 💬 Join our Slack community to connect with developers and ask questions: https://estuary-dev.slack.com/ #Estuary #RealtimeETL #DataStreaming #DataOps #dataengineering
Seamless Data Integration, Unlimited Potential
Discover the simplest way to connect and move your data.Get hands-on for free, or schedule a demo to see the possibilities for your team.