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Begin by setting up a secure connection to your SFTP server. Use command-line tools such as `sftp` or `scp` to securely connect to the server using your credentials. Ensure that you have the necessary permissions to access the data files you need to transfer.
Once connected to the SFTP server, navigate to the directory containing the data files. Use the `get` command to download the required files to your local system. This ensures you have a local copy of the data for further processing before uploading it to Teradata Vantage.
With the data now on your local system, prepare it for import into Teradata Vantage. This might involve converting the file format to CSV or text if it's not already in a compatible format. Ensure the data is clean, with no missing values or errors, and matches the schema of the Teradata table you plan to load it into.
Before loading data, define the schema of the table in Teradata Vantage where the data will reside. Use the Teradata SQL Assistant or BTEQ to execute SQL commands that create the table structure. Ensure the data types and column definitions match those of the source data.
Set up a connection to your Teradata Vantage system using the Teradata SQL Assistant, BTEQ, or any other native Teradata tool that allows SQL execution. Ensure you have the necessary credentials and permissions to access and modify the database.
Utilize Teradata's FastLoad utility to load the data from your local system into Teradata Vantage. FastLoad is designed for high-speed data loading and can handle large volumes efficiently. Create a FastLoad script specifying the source file, target table, and field mappings, and then execute it to transfer the data.
After the data load is complete, verify that the data transfer was successful. Run SQL queries in Teradata Vantage to check row counts, sample data, and ensure there are no discrepancies. Compare these with the source data to confirm accuracy and completeness, and address any issues or errors that arise.
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:
