How to load data from SFTP to Snowflake destination

Learn how to use Airbyte to synchronize your SFTP data into Snowflake destination within minutes.

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Set up a SFTP connector in Airbyte

Connect to SFTP or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Snowflake destination for your extracted SFTP data

Select Snowflake destination where you want to import data from your SFTP source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the SFTP to Snowflake destination in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync SFTP to Snowflake destination Manually

1. Log in to the SFTP server: Use an SFTP client or command-line tool to connect to the SFTP server with the appropriate credentials.

2. Locate the data files: Navigate to the directory where the data files you want to move are stored.

3. Export the data (if necessary): If the data isn't already in a flat-file format (CSV, JSON, etc.), export it to a format that Snowflake can ingest.

4. Compress the data (optional): To speed up the transfer, you can compress the data files using a tool like `gzip`.

Snowflake can directly load data from cloud storage services like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Choose the one that is most suitable for your Snowflake account and region.

If using Amazon S3:

1. Install AWS CLI: Install and configure the AWS Command Line Interface (CLI) with the necessary permissions to access your S3 bucket.

2. Upload to S3: Use the AWS CLI to upload the data files from your local machine to the appropriate S3 bucket using the `aws s3 cp` or `aws s3 sync` command.

If using Google Cloud Storage:

1. Install Google Cloud SDK: Install and configure the Google Cloud SDK with the necessary permissions to access your Cloud Storage bucket.

2. Upload to GCS: Use the `gsutil cp` command to upload the data files from your local machine to the appropriate Cloud Storage bucket.

If using Azure Blob Storage:

1. Install Azure CLI: Install and configure the Azure Command Line Interface (CLI) with the necessary permissions to access your Blob Storage container.

2. Upload to Blob Storage: Use the `az storage blob upload` command to upload the data files from your local machine to the appropriate Blob Storage container.

Once the data is in cloud storage, you can load it into Snowflake using the COPY INTO command.

1. Log in to Snowflake: Use the Snowflake web interface or a SQL client that supports Snowflake to log in to your account.

2. Create a File Format: Define a file format that matches the format of your data files:

   ```sql

   CREATE FILE FORMAT my_file_format

   TYPE = 'CSV'

   FIELD_DELIMITER = ','

   SKIP_HEADER = 1

   NULL_IF = ('\\N');

   ```

3. Create a Stage: Create a stage object that points to the location of the files in the cloud storage:

   ```sql

   -- For Amazon S3

   CREATE STAGE my_stage

   URL = 's3://mybucket/data/'

   FILE_FORMAT = my_file_format

   CREDENTIALS = (AWS_KEY_ID = 'my_aws_key_id' AWS_SECRET_KEY = 'my_aws_secret');

   -- For Google Cloud Storage

   CREATE STAGE my_stage

   URL = 'gcs://mybucket/data/'

   FILE_FORMAT = my_file_format

   CREDENTIALS = (GCS_CREDENTIALS = 'my_gcs_credentials_json');

   -- For Azure Blob Storage

   CREATE STAGE my_stage

   URL = 'azure://myaccount.blob.core.windows.net/mycontainer/data/'

   FILE_FORMAT = my_file_format

   CREDENTIALS = (AZURE_SAS_TOKEN = 'my_azure_sas_token');

   ```

4. Create a Target Table: Create a table in Snowflake to hold the data:

   ```sql

   CREATE TABLE my_table (

     column1 STRING,

     column2 STRING,

     ...

   );

   ```

5. Copy Data into Snowflake: Use the COPY INTO command to load the data from the stage into the Snowflake table:

   ```sql

   COPY INTO my_table

   FROM @my_stage

   FILE_FORMAT = (FORMAT_NAME = my_file_format);

   ```

6. Verify the Data Load: After the COPY INTO command completes, verify that the data has been loaded correctly:

   ```sql

   SELECT * FROM my_table LIMIT 10;

   ```

After the data has been successfully loaded into Snowflake, you may want to clean up any temporary files or data that is no longer needed.

1. Remove Temporary Files: If you created any temporary files during the process, make sure to delete them from your local machine and cloud storage.

2. Close SFTP Connection: Log out from the SFTP server to ensure security.

3. Review Snowflake Costs: Loading data into Snowflake may incur costs, so review your usage and consider setting up resource monitors to manage expenses.

Remember to handle any sensitive credentials securely and to rotate them periodically. Also, consider automating this process for recurring data transfers using scripting and scheduling tools.

How to Sync SFTP to Snowflake destination Manually - Method 2:

FAQs

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 (Secure File Transfer Protocol) is a secure way to transfer files between two computers over the internet. It uses encryption to protect the data being transferred, making it more secure than traditional FTP (File Transfer Protocol). SFTP is commonly used by businesses and organizations to transfer sensitive data such as financial information, medical records, and personal data. It requires authentication using a username and password or public key authentication, ensuring that only authorized users can access the files. SFTP is also platform-independent, meaning it can be used on any operating system, making it a versatile and reliable option for secure file transfers.

SFTP provides access to various types of data that can be used for different purposes. Some of the categories of data that SFTP's API gives access to are:  

1. File data: SFTP's API allows users to access and transfer files securely over the internet. This includes uploading, downloading, and managing files.  
2. User data: SFTP's API provides access to user data such as usernames, passwords, and permissions. This allows users to manage and control access to their files and folders.  
3. Server data: SFTP's API gives access to server data such as server logs, server configurations, and server status. This allows users to monitor and manage their server resources.  
4. Security data: SFTP's API provides access to security data such as encryption keys, certificates, and security policies. This allows users to ensure that their data is secure and protected from unauthorized access.  
5. Network data: SFTP's API gives access to network data such as IP addresses, network configurations, and network traffic. This allows users to monitor and manage their network resources.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up SFTP to Snowflake Data Cloud as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from SFTP to Snowflake Data Cloud and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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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