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Start by exporting your Trello board data. Trello provides an option to export boards in JSON format. Navigate to the board you want to export, click on the menu, select "More," and then "Print and Export." Choose the "Export as JSON" option. Save this JSON file locally on your computer.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will store the Trello data. Make sure to give it a unique name and choose a region that suits your requirements. Note the bucket name for future reference.
Once your S3 bucket is set up, upload the Trello JSON file to this bucket. Go to the S3 console, click on your bucket name, and use the "Upload" option to transfer the JSON file. Ensure the file is uploaded successfully and note the file path.
Navigate to the IAM service in your AWS console to create a role that AWS Glue can assume. Choose the "Glue" service from the trusted entity options. Attach policies that provide the necessary permissions for Glue to access the S3 bucket and read/write data. Common policies include `AmazonS3FullAccess` or a more restrictive custom policy.
In the AWS Glue console, create a new crawler to catalog the data in your S3 bucket. Configure the crawler to use the IAM role you just created. Specify the S3 bucket path where the Trello JSON file is stored as the data source. Define where the crawler should store the metadata (in a specific database in the Glue Data Catalog).
Execute the Glue crawler to scan the JSON file and populate the Glue Data Catalog with metadata. This process will create a table schema based on the structure of your Trello JSON file. Once the crawler finishes, review the Data Catalog to ensure the table reflects the data correctly.
After the data is cataloged, create an AWS Glue ETL job to transform the data as needed. Use the Glue Studio or write a PySpark script to process the JSON data, clean it, and convert it into a format suitable for your analysis or downstream processing. Output the transformed data back into another S3 bucket or a different folder within the same bucket.
By following these steps, you can effectively move and process Trello data in AWS without using external connectors, leveraging AWS's native services for a streamlined workflow.
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.
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