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First, you need to manually export the data from Wrike. Log in to your Wrike account and navigate to the project or data set you want to export. Use the export option to download the data in a format supported by AWS services, such as CSV or Excel. Ensure that your data is structured correctly for further processing.
Set up your local environment to handle the exported file. Ensure you have the AWS Command Line Interface (CLI) installed and configured with your AWS credentials. This setup will allow you to upload the file to S3 and interact with AWS services from your local machine.
Create an S3 bucket if you haven’t already. Use the AWS CLI to upload the exported Wrike file to this bucket. The command looks like this:
```
aws s3 cp /path/to/your/exportedfile.csv s3://your-bucket-name/
```
Ensure that the S3 bucket has the appropriate permissions for AWS Glue to access the data.
In the AWS Management Console, navigate to AWS Glue and create a new crawler. Configure the crawler to point to the S3 bucket and specify the path where your data file is located. The crawler will scan your data and create a schema in the AWS Glue Data Catalog.
Execute the AWS Glue crawler to populate the Data Catalog. This process involves scanning the S3 data and generating table definitions based on the file format and structure. Once the crawler completes, verify that the metadata accurately reflects your data's schema.
Set up an AWS Glue ETL job to process the data. Choose the appropriate data source from the Data Catalog created by your crawler. Define any transformations or data processing steps needed to prepare the data for analysis or further use. Specify the target location within your S3 bucket where the processed data should be stored.
Run the AWS Glue job and monitor its execution through the AWS Glue console. Ensure that the job completes successfully and that the processed data is stored as expected in your specified S3 location. Check logs for any errors and adjust the job configuration if necessary.
By following these steps, you can manually transfer data from Wrike to AWS S3 and use AWS Glue for data processing, 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.
Wrike is an American project management application service provider which is based in San Jose, California. It is a cloud based association and project management tool that assists users to manage projects from start to finish, providing full visibility. Wrike is entirely a cloud-based project management platform for teams of 20+ which is suitable for both large program and SMBs. Wrike ransaks to discard complexity from work so people and teams can enforce at their best.
Wrike's API provides access to a wide range of data related to project management and collaboration. The following are the categories of data that can be accessed through Wrike's API:
1. Tasks: Information related to tasks such as task name, description, due date, status, and assignee.
2. Projects: Data related to projects such as project name, description, start and end dates, and project status.
3. Users: Information about users such as user name, email address, and user role.
4. Time tracking: Data related to time tracking such as time spent on tasks, time entries, and billable hours.
5. Comments: Information related to comments such as comment text, author, and date.
6. Attachments: Data related to attachments such as attachment name, type, and size.
7. Custom fields: Information related to custom fields such as field name, type, and value.
8. Folders: Data related to folders such as folder name, description, and folder structure.
9. Reports: Information related to reports such as report name, description, and report data.
Overall, Wrike's API provides access to a comprehensive set of data that can be used to enhance project management and collaboration.
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?
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