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Begin by exporting your data from Airtable. Navigate to the base you want to export and choose the table. Click on the 'View' dropdown, then select 'Download CSV'. This action will download your table data as a CSV file to your local machine.
Open the downloaded CSV file and ensure that the data is clean and well-structured. Check for any inconsistencies or formatting issues that might cause problems during import. Make sure all necessary columns and rows are properly aligned and that there are no missing headers or data.
Log in to your Starburst Galaxy account. If you don't have one, you'll need to create an account and set up your environment according to the Starburst Galaxy documentation. Make sure you have the necessary permissions to create and manage data tables.
Use the Starburst Galaxy SQL editor to create a new table that matches the structure of your CSV data. This involves defining the table schema with the correct data types for each column. Use the `CREATE TABLE` SQL statement to define the table structure in your Starburst Galaxy instance.
Transfer the CSV file to a location accessible by Starburst Galaxy. You can use cloud storage like AWS S3, Google Cloud Storage, or Azure Blob Storage, depending on what is supported by your Starburst Galaxy setup. Upload the CSV file to your chosen cloud storage service.
Use the SQL `COPY` command or an equivalent data ingestion command in Starburst Galaxy to load the data from the CSV file into the newly created table. Specify the file path in your cloud storage service and configure any necessary parameters like delimiter and header options to match the CSV format.
After loading the data, execute a few SQL queries in Starburst Galaxy to verify that the data has been imported correctly. Check for data accuracy, completeness, and consistency. Run `SELECT` queries to ensure that all records are present and that there are no obvious errors or omissions.
By following these steps, you can effectively move data from Airtable to Starburst Galaxy 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.
Airtable is a cloud collaboration service.
Airtable's API provides access to a wide range of data types, including:
1. Tables: The primary data structure in Airtable, tables contain records and fields.
2. Records: Each row in a table is a record, which contains data for each field.
3. Fields: Each column in a table is a field, which can contain various data types such as text, numbers, dates, attachments, and more.
4. Views: Airtable allows users to create different views of their data, such as grid view, calendar view, and gallery view.
5. Forms: Airtable also allows users to create forms to collect data from external sources.
6. Attachments: Users can attach files to records, such as images, documents, and videos.
7. Collaborators: Airtable allows users to collaborate with others on their data, with different levels of access and permissions.
8. Metadata: Airtable's API also provides access to metadata about tables, fields, and records, such as creation and modification dates.
Overall, Airtable's API provides a comprehensive set of data types and features for users to manage and manipulate their data in a flexible and customizable way.
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:
