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Begin by ensuring you have a Snowflake account set up. Create a dedicated database and warehouse for your data import. Configure the necessary roles and privileges for users who will access this data. Ensure that you have the Snowflake CLI (SnowSQL) installed and configured on your local machine to execute SQL commands.
Use a command-line SFTP client (such as OpenSSH SFTP) to connect to the SFTP server. Ensure you have the necessary credentials (username, password, or key-based authentication) to access the SFTP server. Test the connection to ensure you can access and list files in the SFTP directory.
Once connected to the SFTP server, navigate to the directory containing the data files you need to transfer. Use the SFTP `get` command to download these files to your local machine or a server where you can perform further processing. Ensure the files are downloaded in a format compatible with Snowflake, such as CSV or JSON.
Inspect the downloaded data files to ensure they are correctly formatted and contain the necessary data. If needed, clean or transform the data using scripting languages like Python or shell scripts. Ensure the data is free from errors and aligns with the table schema in Snowflake.
Use the SnowSQL command-line tool to upload your data files to a Snowflake stage. First, create an external stage within your Snowflake environment using the `CREATE STAGE` command. Next, use the `PUT` command in SnowSQL to upload your data files to this stage. This step temporarily stores the files in Snowflake for further processing.
With the data files in the Snowflake stage, use the `COPY INTO` command to load the data into your target Snowflake table. Ensure that the table schema matches the data format. Use options within the `COPY INTO` command to handle any specific needs such as file format specifications or error tolerances.
After loading the data, perform a verification step by running queries to ensure the data has been loaded correctly and completely. Check row counts and spot-check data quality. Once verified, clean up by removing the files from the Snowflake stage using the `REMOVE` command to free up space. Additionally, delete or archive the local copies of the data files if no longer needed, ensuring secure handling of sensitive data.
By following these steps, you can transfer and load data from SFTP to Snowflake manually without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
SFTP is basically based on the SSH2 protocol, which uses a binary encoding of messages over a secure channel. SFTP Bulk contains the setup guide and reference information for the FTP source connector. SFTP Bulk fetches files from an FTP server matching a folder path and defines an optional file pattern to bulk ingest files into a single stream. It incrementally loads files into your destination from an FTP server based on when files were last added or modified.
SFTP Bulk's API provides access to a wide range of data related to file transfer and management. The following are the categories of data that can be accessed through the API:
1. File Transfer Data: This includes information related to the transfer of files such as file name, size, transfer status, and transfer time.
2. User Data: This includes user-related information such as user ID, username, and password.
3. Server Data: This includes server-related information such as server name, IP address, and port number.
4. Security Data: This includes security-related information such as encryption algorithms used, authentication methods, and access control policies.
5. Error Data: This includes information related to errors that occur during file transfer such as error codes, error messages, and error descriptions.
6. Audit Data: This includes information related to auditing and compliance such as user activity logs, file transfer logs, and security logs.
Overall, SFTP Bulk's API provides access to a comprehensive set of data that can be used to monitor, manage, and secure file transfers.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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