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Start by logging into your Azure portal. Create a storage account if you haven't already. Within this storage account, create a blob container where your data files will reside. Upload the files you need to move to PostgreSQL into this container.
Ensure you have the Azure CLI installed on your local machine for accessing Azure Blob Storage. Also, install the PostgreSQL client (such as `psql`) for interacting with your PostgreSQL database. These tools will enable command-line interactions with both Azure and PostgreSQL.
Open your command-line interface and log in to Azure using the Azure CLI by typing `az login`. Follow the instructions in the browser to authenticate your account. This will allow you to access your Azure resources from the command line.
Use the Azure CLI to download data from your Azure Blob Storage to your local machine. You can do this with the command:
```
az storage blob download --container-name --name --file --account-name
```
Replace ``, ``, ``, and `` with your specific details. This command will download the blob to your specified local path.
Once the data is downloaded, prepare it for import into PostgreSQL. Ensure the data is in a format that PostgreSQL can import, such as CSV. If necessary, clean or transform the data to match the structure of your target PostgreSQL table.
Connect to your PostgreSQL database using the `psql` command or another PostgreSQL client. Create a table to match the structure of your data if it doesn’t exist already. Use SQL commands to define the table schema:
```sql
CREATE TABLE your_table_name (
column1 datatype,
column2 datatype,
...
);
```
With the table ready, use the `COPY` command to import your data file into PostgreSQL. This command is efficient for bulk loading data:
```sql
COPY your_table_name FROM '/local-file-path-to-your-data' DELIMITER ',' CSV HEADER;
```
Ensure that the local file path is accessible from your PostgreSQL server and that the file format matches what PostgreSQL expects.
By following these steps, you'll manually transfer data from Azure Blob Storage to a PostgreSQL database using command-line tools without relying on 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.
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: