Right-time Data Integration for Snowflake
Hosted by: Dani & Ben Rogojan (Seattle Data Guy)
Choosing the right ingestion strategy for Snowflake can dramatically influence your latency, cost, and operational overhead. In this live 1-hour session, Dani and Ben Rogojan will break down the modern ingestion landscape and help you understand when batch, micro-batch, serverless, or real-time streaming pipelines make the most sense.
This webinar is designed for data engineers, architects, and Snowflake users who want a clearer framework for making ingestion decisions, without the guesswork.
More videos
How to Stream Data into Snowflake
Ingest data into a Snowflake warehouse using real-time Snowpipe Streaming or using batch COPY INTO commands. Estuary makes Snowflake integration simple with pre-built no-code connectors. Following along? Find the copy/pasteable commands in Estuary’s Snowflake docs: https://docs.estuary.dev/reference/Connectors/materialization-connectors/Snowflake/ - Set up your first data pipeline for free at Estuary: https://dashboard.estuary.dev/register/?utm_source=youtube&utm_medium=social&utm_campaign=snowflake_ingestion - Learn more about Estuary’s Snowflake capabilities: https://estuary.dev/solutions/technology/real-time-snowflake-streaming/ - Read the complete guide to Snowpipe Streaming: https://estuary.dev/blog/snowpipe-streaming-fast-snowflake-ingestion/ - Discover how Snowflake fared in Estuary’s Data Warehouse Benchmark: https://estuary.dev/data-warehouse-benchmark-report/ - Download Snowflake Ingestion Playbook: https://estuary.dev/snowflake-ingestion-whitepaper/ FAQ 1. What is the fastest way to load data into Snowflake? Snowpipe Streaming with row-based ingestion. In Estuary, you can enable it per table using Delta Updates. 2. Why use key pair authentication for Snowflake? It provides strong security, short-lived tokens, and is Snowflake’s recommended approach for service integrations like Estuary. 3. Can I mix real-time and batch ingestion in the same pipeline? Yes. With Estuary’s Snowflake connector, you can run some tables in batch (COPY INTO or Snowpipe) and others in real time with Snowpipe Streaming. Media resources used in this video are from Pexels, Canva, and the YouTube Studio Audio Library. 0:00 Introduction 1:05 Snowflake concerns 1:51 Ingestion options 3:23 Beginning the demo 3:47 Create Snowflake resources 4:28 User auth setup 5:17 Estuary connector config 6:44 Customization options 8:07 Wrapping up
What’s Next for Data Warehouses? Lessons from Our Benchmark and Emerging Trends
Dani and Ben talks about key findings on performance ceilings, cost traps, and failure modes, and explore the major trends reshaping data warehouse architecture, including: - Separation of Compute & Storage – How Snowflake Gen2, Databricks serverless, and open table formats like Iceberg are changing the game. - Lakehouse Reality Check: What’s working for teams adopting Iceberg, schema evolution patterns, and lake-native pipelines. - Flexibility Over Centralization: Moving beyond “one warehouse to rule them all.
Create a Webhook-to-Snowflake Data Pipeline
Create a complete data pipeline in 3 minutes that captures Square (or any other platform's) webhooks and materializes to Snowflake. With Estuary Flow, you can create endpoints to receive webhook data without setting up and maintaining your own server. Try it out for free at → https://dashboard.estuary.dev/register Ready for more? - See our site: https://estuary.dev/ - Learn more about webhooks: https://estuary.dev/blog/webhook-setup/ - Read our webhook capture docs: https://docs.estuary.dev/reference/Connectors/capture-connectors/http-ingest/ - Or our Snowflake materialization docs: https://docs.estuary.dev/reference/Connectors/materialization-connectors/Snowflake/ 0:00 Intro 0:19 Set up webhook capture 1:17 Configure webhook in Square 2:06 Create Snowflake materialization 2:48 Outro
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.