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First, ensure you have SSH access to the SFTP server. You need the server's IP address, username, and password or SSH key. Test your access using an SSH client (like OpenSSH) by running:
```bash
ssh user@server_ip
```
This step ensures you can connect and navigate the SFTP server.
Use the SFTP command-line tool to locate and download the files. Start an SFTP session and navigate to the directory containing the data files:
```bash
sftp user@server_ip
sftp> cd /path/to/data/files
sftp> lcd /local/path/to/store/files
sftp> mget
```
This will download all files from the specified directory on the SFTP server to your local machine.
Ensure you have the necessary tools to process the data on your local machine. This typically involves having a scripting language like Python or Bash installed. For example, you can use Python's built-in CSV module to read and process CSV files.
Process the downloaded files to ensure they are in a format suitable for PostgreSQL. This might involve cleaning the data, handling missing values, or converting data types. Here's a simple Python script example:
```python
import csv
def clean_data(file_path):
with open(file_path, 'r') as infile, open('cleaned_data.csv', 'w', newline='') as outfile:
reader = csv.reader(infile)
writer = csv.writer(outfile)
for row in reader:
# Perform any necessary data cleaning
writer.writerow(row)
clean_data('/local/path/to/store/files/data.csv')
```
Ensure you can access the PostgreSQL database. You need the database host, port, username, password, and database name. Test your connection with a tool like psql:
```bash
psql -h db_host -U db_user -d db_name
```
This verifies that you can connect to the database where you will upload the data.
Before uploading, ensure the PostgreSQL database has the necessary tables to receive the data. Use SQL commands to create tables as needed:
```sql
CREATE TABLE my_table (
column1 datatype,
column2 datatype,
...
);
```
Adjust the table structure based on the data you plan to import.
Load the cleaned data into PostgreSQL. You can use the `COPY` command or a scripting language like Python with a library such as psycopg2:
```python
import psycopg2
conn = psycopg2.connect("dbname=db_name user=db_user password=db_pass host=db_host")
cur = conn.cursor()
with open('cleaned_data.csv', 'r') as f:
cur.copy_expert("COPY my_table FROM STDIN WITH CSV HEADER", f)
conn.commit()
conn.close()
```
This imports the data from the cleaned CSV file into the specified PostgreSQL table.
By following these steps, you can manually transfer data from an SFTP server to a PostgreSQL database 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: