3/28/2024 0 Comments Extract transform load data![]() ![]() If you are using a CSV Format for loading large datasets into the Snowflake, consider configuring a Format to split the document into smaller files: Snowflake can load files in parallel, also transferring smaller files over the network can be faster. Snowflake can load data from CSV, JSON, and Avro Formats so you will need to create one of these and set it as a destination Format. When configuring a Connection for Amazon S3, Azure Storage, or Google Storage, which will be used as a stage, it is recommended that you select GZip as the value for the Archive file before copying field: Google Cloud Storage connection for loading data from the Google Cloud external stage.Azure storage connection for loading data from the Azure external stage.S3 connection for loading data from the S3 external stage.The Server Storage connection for loading data from the internal Snowflake stage.Read how Etlworks flow automatically creates the named Snowflake stage.ĭepending on how you prefer to stage files with CDC events for loading data into the Snowflake create one of the following connections: Alternatively, you can configure the Stage name at the transformation level. When creating a Snowflake Connection, set the Stage name. You will need a source Connection, a Connection for the stage ( Amazon S3, Azure Blob, Google Cloud Storage, or Server Storage), and a Snowflake Connection. Here's how you can extract, transform, and load data in Snowflake: Step 1. Note that Etlworks flow does not create the bucket or blob. For loading data from the external stage in AWS S3, Azure Blob, or Google Cloud Storage, the Amazon S3 bucket, Google Storage bucket, or Azure blob needs to be created. Read how Etlworks flow automatically creates the named Snowflake stage.ģ. Stage refers to the location where your data files are stored for loading into Snowflake. COPY INTO requires a named internal or external stage. Etlworks uses the Snowflake COPY INTO command to load data into Snowflake tables. The Stage name is set for the Snowflake connection or Transformation (the latter overrides the stage set for the Connection). The Snowflake data warehouse is active.Ģ. Cleans up the remaining files, if needed.ġ.If configured MERGEs data in the source with the data in Snowflake.Dynamically generates and executes the Snowflake COPY INTO command.Checks to see if the destination Snowflake table exists, and if it does not - creates the table using metadata from the source.Copies files into Snowflake stage (local file system, Azure Blob, Amazon S3, or Google Cloud storage).Compresses files using the gzip algorithm.Creates CSV, JSON, Avro, or Parquet files.Automatically creates a named Snowflake stage if it does not exists.Using Snowflake-optimized Flows, you can extract data from any of the supported sources, transform, and load it into Snowflake.Ī typical Snowflake Flow performs the following operations: Unlike Bulk load files into Snowflake, this flow does not support automatic MERGE. This flow requires providing the user-defined COPY INTO command. When you need to bulk-load data from the file-based or cloud storage, API, or NoSQL database into Snowflake without applying any transformations. When you need to stream messages from the message queue which supports streaming into Snowflake in real time. Stream messages from a queue into Snowflake When you need to stream updates from the database which supports Change Data Capture (CDC) into Snowflake in real time. The flow automatically generates the COPY INTO command and MERGEs data into the destination. 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. When you need to extract data from any source, transform it and load it into Snowflake. It is recommended that you use a Snowflake-optimized Flow to ETL data in Snowflake. It is, however, important to understand that inserting data into Snowflake row by row can be painfully slow. Therefore, you can use the same techniques you would normally use to work with relational databases in the Etlworks Integrator. Snowflake is a column-based relational database. ![]()
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