Snowflake is a data warehouse built for the cloud. Read more about Snowflake. Etlworks includes several pre-built Flows optimized for Snowflake.
Flows optimized for Snowflake
|When to use
|When you need to extract data from any source, transform it and load it into Snowflake.
|Bulk load files into Snowflake
|When you need to bulk-load files that already exist in the external Snowflake stage (S3, Azure Blob, GC blob) or in the server storage without applying any transformations. The flow automatically generates the COPY INTO command and MERGEs data into the destination.
|Stream CDC events into Snowflake
|When you need to stream updates from the database which supports Change Data Capture (CDC) into Snowflake in real time.
|Stream messages from a queue into Snowflake
|When you need to stream messages from the message queue which supports streaming into Snowflake in real time.
|COPY files into Snowflake
|When you need to bulk-load data from the file-based or cloud storage, API, or NoSQL database into Snowflake without applying any transformations. This flow requires providing the user-defined COPY INTO command. UnlikeBulk load files into Snowflake, this flow does not support automatic MERGE.
ETL, CDC, and bulk load data into Snowflake
Watch how to create flows to ETL, CDC and bulk load data into Snowflake
How ETL data into Snowflake
A typical Snowflake-optimized flow does the following:
ELT with Snowflake
Etlworks supports executing complex ELT scripts directly in Snowflake, which greatly improves the performance and reliability of the data ingestion.
Reverse ETL with Snowflake
You can use any
Data type Mapping for Snowflake
It is important to understand how we map various JDBC data types for the Snowflake data types.
Load multiple tables by a wildcard name
You can ETL data from multiple database objects (tables and views) into Snowflake by a wildcard name without creating individual source-to-destination transformations.
Setup incremental change replication using a high watermark (HWM)
Using HWM replication you can load only new and updated records into Snowflake.
Automatic creation of the Snowflake stage
Etlworks can automatically create an internal or external Snowflake stage.
Related case studies
Streaming data from 1600+ MySQL databases to Snowflake using CDC
"A typical CDC Flow can extract data from multiple tables in multiple databases, but having a single Flow pulling data from 55000+ tables would be a major bottleneck as it would be limited to a single blocking queue with a limited capacity. It would also create a single point of failure."
Major retail chain
Load data from 600+ SQL Servers behind a firewall
"A major retail chain with stores across the US operates 600+ independent Microsoft SQL Server databases that work behind the firewall. They needed to consolidate data in the Snowflake data warehouse and the expected traffic from each location is tens of millions of records per day."