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Begin by exporting the data from Trello. Trello allows you to export board data in JSON format. Open the Trello board you want to export, click on the menu (three dots) in the top-right corner, select "More," and then choose "Print and Export" to download the JSON file of the board.
Once you have the JSON file, parse the data to extract the relevant information. Use a JSON parser tool or write a script in a programming language like Python to read the JSON file and convert it into a structured format such as CSV, which can be more easily imported into Oracle.
Convert the parsed JSON data into CSV format. Each card in the Trello board can represent a row in your CSV file, and the card's properties (e.g., name, description, labels) can become columns. Ensure the CSV file structure aligns with the schema of your Oracle Database table.
Before importing the CSV file, ensure your Oracle Database is ready to receive the data. This involves creating a table with the appropriate columns and data types that match the structure of your CSV file. Use SQL commands via Oracle SQL Developer or SQL*Plus to create the necessary table.
Use Oracle's SQL*Loader utility to import the CSV data into your Oracle Database. Create a control file that specifies how SQL*Loader should interpret the CSV file, including details like field delimiters and data types. Run the SQL*Loader command in the terminal to start the import process.
After importing, verify that the data has been correctly transferred to the Oracle Database. Use SQL queries to check that the data in the Oracle table matches the data in the original Trello board. Look for any discrepancies or errors that may have occurred during the import.
If regular data transfers are needed, consider automating the process by creating scripts for each step. Use a scripting language like Python or Bash to automate the JSON parsing, CSV conversion, and SQL*Loader execution. Schedule the script to run at desired intervals using cron jobs or task schedulers.
By following these steps, you can manually transfer data from Trello to an Oracle 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.
Trello is a web-based, Kanban-style, list-making application and is a subsidiary of Atlassian. Originally created by Fog Creek Software in 2011, it was spun out to form the basis of a separate company in 2014 and later sold to Atlassian in January 2017. The company is based in New York City.
Trello's API provides access to a wide range of data related to boards, cards, lists, members, and organizations. Here are the categories of data that Trello's API gives access to:
- Boards: Information about boards, including their name, description, URL, and members.
- Cards: Details about individual cards, such as their name, description, due date, and attachments.
- Lists: Information about lists, including their name, position, and cards.
- Members: Data related to members, such as their name, email address, and avatar URL.
- Organizations: Details about organizations, including their name, description, and members.
In addition to these categories, Trello's API also provides access to data related to actions, checklists, labels, and more. With this data, developers can build custom integrations and applications that interact with Trello in a variety of ways. For example, they can create custom reports, automate workflows, or build dashboards that display Trello data in real-time.
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:
