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First, ensure you have an AWS account and have created the necessary IAM roles with appropriate permissions to access DynamoDB. Install and configure the AWS CLI on your local machine to interact with AWS services. You may also need to set up an EC2 instance if you plan to run the script from AWS.
Use an SFTP client like `sftp` or `scp` command-line tools to connect to the SFTP server from your local machine or EC2 instance. You will need the hostname, port, username, and authentication method (password or SSH key). Test the connection to ensure you can access the files.
Once connected to the SFTP server, navigate to the directory containing the data files. Use the `get` command to download the data files to your local machine or EC2 instance. If you have multiple files, you can use a loop or wildcard to download all files at once, depending on your SFTP client capabilities.
After downloading the files, write a script in Python or another language that reads and parses the data. Use libraries like `csv` for CSV files or `json` for JSON files. During this step, transform the data into a format that fits the structure of your DynamoDB table, ensuring that you handle data types and attribute names correctly.
Use the AWS SDK for Python (Boto3) or another language to interact with DynamoDB. Implement batch write operations to efficiently insert data into your DynamoDB table. The `batch_write_item` method allows you to insert up to 25 items at a time. Handle exceptions and implement retries for any failed operations to ensure data integrity.
After the data is uploaded, verify that the records have been correctly inserted into DynamoDB. You can do this by querying the table using the AWS CLI or Boto3 to fetch a few records and compare them with the original data files. This step is crucial to ensure the migration was successful.
Finally, automate the entire process using a script or cron job. You can schedule this script to run at regular intervals, ensuring that new data from the SFTP server is consistently moved to DynamoDB. Make sure to include logging and error-handling mechanisms in your script for easier maintenance and debugging.
By following these steps, you can efficiently move data from an SFTP server to DynamoDB 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
