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Begin by exporting the data you need from Harvest. Log into your Harvest account, navigate to the Reports section, and select the data you wish to export (e.g., time entries, invoices). Use the built-in export feature to download the data in CSV or Excel format.
Once the data is exported from Harvest, ensure it is in a format suitable for AWS S3. If necessary, clean or transform the data using a tool like Excel or a script in Python. Ensure the file is saved in a format supported by AWS Glue, such as CSV, JSON, or Parquet.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you will upload your Harvest data. Ensure the bucket has the necessary permissions for you to upload files and for AWS Glue to access them later.
With your S3 bucket ready, upload the prepared data file. You can do this using the AWS Management Console by clicking the 'Upload' button within your specified bucket and selecting the file from your local system. Alternatively, use the AWS CLI for uploading if you prefer command-line operations.
Navigate to the AWS Glue service in the AWS Management Console. Create a new Glue Crawler that will scan the data in your S3 bucket. Define a database within AWS Glue to store the metadata tables generated by the crawler. Configure the crawler to include the S3 bucket path and select the appropriate IAM role with access permissions.
Execute the Glue Crawler to generate a schema from the uploaded data. The crawler will automatically infer the structure of your data (e.g., columns, data types) and create metadata tables in the AWS Glue Data Catalog. This step is essential for data processing and transformation tasks.
Use AWS Glue ETL (Extract, Transform, Load) jobs to process and transform your data as needed. You can write and execute ETL scripts using Python or Scala in the AWS Glue Console. If needed, query the processed data using AWS Athena, which can directly query data stored in S3 using the schema information from the Glue Data Catalog.
By following these steps, you'll efficiently move data from Harvest to AWS S3 and leverage AWS Glue for any necessary data processing, all without the need for 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.
Harvest is a provider of time tracking and online invoicing services for freelancers and small businesses. Harvest focuses on providing simple to use web-based software for professional services. Customers range from freelancers to creative services businesses, to team within Fortune 500 organizations and non-profits.
Harvest's API provides access to a wide range of data related to time tracking, invoicing, and project management. The following are the categories of data that can be accessed through Harvest's API:
1. Time tracking data: This includes information about the time spent on tasks, projects, and clients.
2. Invoicing data: This includes information about invoices, payments, and expenses.
3. Project management data: This includes information about projects, tasks, and team members.
4. Client data: This includes information about clients, contacts, and projects associated with them.
5. User data: This includes information about users, their roles, and permissions.
6. Reports data: This includes information about various reports generated by Harvest, such as time reports, expense reports, and project reports.
7. Account data: This includes information about the Harvest account, such as account settings, plan details, and billing information.
Overall, Harvest's API provides a comprehensive set of data that can be used to automate various business processes and gain insights into the performance of projects and teams.
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:
