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Begin by logging into your Clockify account. Navigate to the section where you can export data, typically found under reports or similar settings. Choose the data range and format you prefer (CSV or Excel is commonly used). Download the exported file to your local machine.
Open the exported file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it is complete and accurate. Clean the data if necessary, removing any unnecessary columns or rows and ensuring consistent data formats, such as date and time.
Since you"re not using third-party connectors, decide on a secure method to transfer the data file from your local system to the environment where you can access Starburst Galaxy. This could be through a secure FTP (SFTP) setup or a direct upload to a cloud storage service accessible from Starburst Galaxy.
Access your Starburst Galaxy account and ensure you have the necessary permissions to create databases and tables. Set up the environment to receive new data, which may involve configuring your workspace or cluster settings.
Using the Starburst Galaxy interface or an appropriate SQL client, create a new table that will hold the Clockify data. Define the table schema based on the structure of your cleaned data file. Ensure data types in the table match the types in your data file (e.g., VARCHAR for text, DATE for date values).
Use the SQL command line or the Starburst Galaxy interface to load your data. If the data is uploaded to a cloud service or accessible via SFTP, use the appropriate SQL commands to ingest the data into the newly created table. For instance, you might use a `COPY` command or similar SQL-based data import functionality provided by Starburst Galaxy.
Once the data is loaded, run a series of queries to verify data integrity. Compare a subset of the data in Starburst Galaxy with the original Clockify export to ensure accuracy. Perform any necessary transformations or data quality checks to confirm that the data is ready for analysis or reporting.
By following these steps, you can move data from Clockify to Starburst Galaxy without using third-party connectors, ensuring a smooth transition with a focus on data accuracy and security.
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.
Clockify is the most popular free time tracker and timesheet app for teams of all sizes. Unlike all the other time trackers, Clockify lets you have an unlimited number of users for free. Clockify is an online app that works in a browser, but you can also install it on your computer or phone. Clockify is largely used by everyone from freelancers, small businesses, and agencies, to government institutions, NGOs, universities, and Fortune 500 companies.
Clockify's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Clockify's API:
1. Time entries: This includes data related to the time spent on tasks, projects, and clients.
2. Projects: This includes data related to the projects being worked on, such as project name, description, and status.
3. Clients: This includes data related to the clients associated with the projects, such as client name, contact information, and billing details.
4. Users: This includes data related to the users who are using Clockify, such as user name, email address, and role.
5. Workspaces: This includes data related to the workspaces created in Clockify, such as workspace name, description, and settings.
6. Reports: This includes data related to the reports generated in Clockify, such as time spent on projects, tasks, and clients.
Overall, Clockify's API provides access to a comprehensive set of data that can be used to track time, manage projects, and generate reports.
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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