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Ensure your Excel file is well-structured. Each column should have a clear header, and the data should be free from unnecessary formatting or merged cells. Save the file in a CSV format since Starburst Galaxy can easily ingest CSV files.
Open your Excel file and choose 'Save As' or 'Export' from the menu. Select CSV (Comma delimited) (.csv) as the file format. Ensure that the CSV file has no extra commas or special characters that could disrupt data integrity.
Log in to your Starburst Galaxy account. Navigate to the workspace where you want to upload your data. Ensure you have the necessary permissions to perform data uploads.
If your Starburst Galaxy instance supports direct S3 integration, set up an Amazon S3 bucket to temporarily store your CSV file. Navigate to the AWS Management Console, create a new bucket, and set proper access permissions.
If you are using an S3 bucket, upload your CSV file to the bucket. In Starburst Galaxy, configure access to this S3 bucket using the 'Add Data Source' option. If direct file upload is possible, use the provided interface to upload your CSV directly.
Use the Starburst Galaxy SQL editor to create an external table that maps to your CSV file. Write a SQL query that defines the table structure matching your CSV format, specifying the data types for each column. For example:
```sql
CREATE TABLE my_table (
column1_name data_type,
column2_name data_type,
...
)
WITH (
external_location = 's3://your-bucket/your-file.csv',
format = 'CSV'
);
```
Once the table is created, run a simple query to ensure the data has been imported correctly. For instance, use `SELECT FROM my_table LIMIT 10;` to view the first ten rows. Verify that the data types and values are accurate, and address any discrepancies by adjusting the table schema or CSV format as needed.
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:
