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Before starting, ensure you have the necessary command-line tools installed on your machine. You will need an SFTP client (such as OpenSSH) and DuckDB CLI. On most Unix-like systems, you can install OpenSSH and DuckDB using your package manager. For example, on Ubuntu, run `sudo apt install openssh-client` and download the DuckDB CLI from the DuckDB website.
Open a terminal and connect to your SFTP server using the `sftp` command. Use the syntax `sftp username@hostname`. If prompted, enter your password. This will open an SFTP session where you can browse and download files from the server.
Within the SFTP session, navigate to the directory containing the data files you wish to transfer using the `cd` command. Use the `ls` command to list files. Once you find the files you need, use the `get filename` command to download them to your local machine. Repeat this for all necessary files.
Once the files are downloaded, ensure they are in a format compatible with DuckDB, such as CSV or Parquet. If the files require conversion, use a tool like `pandas` in Python to read the original format and write it to a supported format. For example, you can read a JSON file and convert it to CSV in Python using `pandas`.
Open a new terminal session and start the DuckDB command-line interface by typing `duckdb`. This will launch an interactive session where you can execute SQL commands to interact with your database.
Within the DuckDB CLI, create a new table structure that matches your data using the `CREATE TABLE` SQL command. For example, `CREATE TABLE my_table (column1 INTEGER, column2 TEXT);`. Once the table is created, use the `COPY` command to import the data from your local file into the table: `COPY my_table FROM 'path/to/your/data.csv' (FORMAT CSV);`.
After importing the data, verify the successful import by running a simple query in DuckDB, such as `SELECT * FROM my_table LIMIT 10;`. This will display the first few rows of your table, allowing you to confirm that the data has been imported correctly. If everything appears correct, your data has been successfully moved from SFTP to DuckDB without using any third-party connectors.
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 (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.
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:
