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Begin by logging into your HubPlanner account and navigating to the data section you wish to export. Use the built-in export feature to download the required data in a CSV or Excel format. Ensure that the data file is saved to a location on your local machine that you can easily access.
Open the exported data file in a spreadsheet application like Excel. Conduct a thorough review to ensure data accuracy and consistency. Cleanse the data by removing any duplicates, correcting errors, and standardizing formats (e.g., date formats). This step is crucial to prevent errors during the import process into Starburst Galaxy.
Starburst Galaxy typically works well with data in CSV or JSON format. If your data is not in one of these formats, convert it accordingly. Use the spreadsheet application or a scripting language like Python to transform the data, ensuring it aligns with the schema and data types required by Starburst Galaxy.
Ensure you have access to your Starburst Galaxy account. Familiarize yourself with the Starburst Galaxy interface and identify the database and table structure where you intend to load the data. If necessary, create new tables or schemas to accommodate the incoming data from HubPlanner.
Write a script to load your data into Starburst Galaxy. This can be done using SQL commands within the Starburst Galaxy console or a local SQL client. The script should include commands to insert the data from your CSV or JSON file into the corresponding tables in Starburst Galaxy, taking care to map the columns correctly.
Run the data loading script within the Starburst Galaxy environment. This step will transfer your cleansed and formatted data from your local machine into the Starburst Galaxy database. Monitor the process for any errors or warnings and make necessary adjustments to the script if needed.
After the data loading process is complete, perform a series of checks to verify that the data has been accurately and completely transferred. Compare key data points between the original HubPlanner export and the data now residing in Starburst Galaxy. Run queries to ensure the data is accessible and correctly integrated within the Starburst Galaxy environment.
By carefully following these steps, you can manually transfer data from HubPlanner to Starburst Galaxy without the use of 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.
Hubplanner is a tool to plan, schedule, report and manage your entire team.
Hubplanner's API provides access to a wide range of data related to resource management and project planning. The following are the categories of data that can be accessed through Hubplanner's API:
1. Resource data: This includes information about the resources available for project planning, such as their names, roles, skills, and availability.
2. Project data: This includes information about the projects being planned, such as their names, start and end dates, budgets, and milestones.
3. Task data: This includes information about the tasks that need to be completed for each project, such as their names, descriptions, start and end dates, and assigned resources.
4. Time tracking data: This includes information about the time spent on each task by each resource, as well as the overall time spent on each project.
5. Reporting data: This includes information about the progress of each project, such as the percentage of completion, the budget spent, and the remaining budget.
Overall, Hubplanner's API provides access to a comprehensive set of data that can be used to optimize resource management and project planning.
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:
