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Begin by setting up a secure connection to the SFTP server from your local machine or server. Use an SSH client or command-line tool like `sftp` or `ssh` to log in. Ensure you have the necessary credentials (username and password or SSH keys) and that your IP is whitelisted if required. Execute a test login to verify access.
Identify the directory on the SFTP server where the bulk data files are stored. Use SFTP commands to navigate to this directory and download the files to your local system. You can use the `get` command to download files individually or `mget` for multiple files. Ensure you download all necessary files required for your data migration.
Once the data files are downloaded, parse them locally. Identify the file format (e.g., CSV, JSON, XML) and use appropriate tools or scripts (such as Python scripts with libraries like `pandas` for CSVs or `json` for JSON files) to read the data. Transform the data as needed to match the structure required by MongoDB. This could involve data cleaning, restructuring, or reformatting.
Ensure MongoDB is installed on your destination server or local machine. Install MongoDB tools such as `mongoimport`, which are necessary for importing data into MongoDB. These tools are typically included in the MongoDB database package, but you can also download them separately from the MongoDB website.
Access your MongoDB instance using the MongoDB shell or GUI tools like MongoDB Compass. Create a database and the necessary collections where the data will be stored. Plan the schema and indexes if needed, based on the data structure you prepared in the previous step. Ensure the MongoDB instance is configured properly for connections.
Use the `mongoimport` tool to load the transformed data files into MongoDB. This command-line tool allows you to import files directly into your MongoDB collections. Specify the database, collection, and file to import. For instance, use a command like `mongoimport --db yourdb --collection yourcollection --file yourfile.json --jsonArray` for JSON files. Adjust the parameters according to your file format and data structure.
Once the data import is complete, verify the integrity of the data within MongoDB. Use queries to check the count of documents and sample data entries to ensure the data matches the source files. Perform any necessary data validation checks to confirm that the import process was successful and that the data is ready for use. Address any discrepancies by checking logs and re-importing data if necessary.
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:
