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Begin by exporting the data from Wrike. Log into your Wrike account, navigate to the project or task data you wish to export, and use Wrike's built-in export functionality. Typically, you can export data as a CSV or Excel file. Save the exported file to a secure local directory on your machine or a server you have access to.
Once you have the exported file, review the structure and cleanliness of the data. Ensure that the data is formatted correctly, with consistent columns and no missing headers. If necessary, use a spreadsheet tool or script to clean and format the data appropriately to match the schema you plan to use in your Databricks Lakehouse.
Log into your Databricks account and set up a new workspace if needed. Ensure that you have the necessary permissions to create new clusters and upload data. Configure a cluster with the appropriate specifications (e.g., Spark version, instance types) that suits your data processing needs.
Use the Databricks UI to upload your prepared data file to the Databricks File System (DBFS). Navigate to the "Data" tab in your Databricks workspace, and select "Add Data." Choose the option to upload files directly from your local directory. This will make the data accessible for processing within Databricks.
In a new Databricks notebook, use PySpark or Scala to read the uploaded file from DBFS into a Spark DataFrame. For example, if using PySpark, you can use `spark.read.csv("dbfs:/path/to/yourfile.csv", header=True)` to load the CSV file into a DataFrame, ensuring to specify if the file contains headers.
Perform any necessary transformations or cleansing operations on the DataFrame to prepare it for analysis or storage. This could include operations like filtering, joining with other datasets, or changing data types. Use Spark SQL or DataFrame operations to manipulate the data as required.
Finally, write the transformed DataFrame to a table in your Databricks Lakehouse. Use the `write` method to save the DataFrame in a suitable format such as Delta Lake, which supports ACID transactions and scalable metadata handling. For example, you can use `dataframe.write.format("delta").save("/path/to/delta-table")` to store the data in Delta Lake format.
By following these steps, you can effectively transfer and process your data from Wrike to Databricks Lakehouse without relying on external 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
