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Begin by logging into your Wrike account. Navigate to the specific project or data you wish to export. Use Wrike's built-in export functionality to export your data as a CSV file. This option is typically found under the "File" or "Options" menu within the project or report you are viewing.
Once exported, open the CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Check the data for any inconsistencies, missing values, or errors that might need addressing. Ensure the data is clean and formatted correctly to match the schema of your Firebolt database.
Firebolt requires data to be in a specific format for ingestion. Convert your CSV data into a format that Firebolt can easily process, such as Parquet or JSON. Use a tool like Python pandas or Apache Arrow to convert the CSV file into the desired format, ensuring you maintain data integrity during conversion.
If you haven't done so already, sign up for a Firebolt account and create a new database. Follow the onboarding process to configure your database, specifying the resources and storage options that meet your data needs. Create tables with schemas that match the structure of your data to ensure a smooth upload process.
Use the Firebolt console or command-line interface (CLI) to upload your formatted data file. This involves creating an external table that points to your data file, then using a SQL statement to transfer the data from the external table into your target Firebolt table. Ensure that the file is accessible, for example, by storing it in an Amazon S3 bucket that Firebolt can access.
Once the data is uploaded, run a series of SQL queries to verify that the data has been accurately transferred. Compare row counts, check for any discrepancies, and ensure that all fields are correctly populated. This step is crucial to confirm that data integrity is maintained through the transfer process.
If you anticipate needing to move data regularly from Wrike to Firebolt, consider scripting the process using a programming language like Python. Write a script to automate the export, transformation, and upload process, leveraging Firebolt's API and any available Wrike API functionality to streamline future data transfers without relying on third-party connectors.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
