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Start by exporting the data you need from Confluence. Navigate to the space or page you want to export and use Confluence’s built-in export feature. You can export the content in formats like XML or HTML, which are suitable for further processing.
Once you have your exported data, transform it into a CSV format, which is commonly used for data manipulation. If the export is in XML, you can use Python or another scripting language to parse the XML and convert it into CSV. For HTML exports, you may need to clean the data to extract relevant tables or text before converting to CSV.
Set up your Databricks environment, ensuring that you have a running Databricks cluster with access to a storage account where you will upload your CSV file. Make sure you have the necessary permissions to write data to this storage.
Upload the transformed CSV file to a cloud storage system that your Databricks environment can access. Common options include AWS S3, Azure Blob Storage, or Google Cloud Storage. Use the respective CLI tools or web interfaces to securely upload your file.
In Databricks, create an external table that points to the CSV file in the cloud storage. Use SQL commands in a Databricks notebook to define the schema of your data and specify the location of the CSV file. This step makes the data accessible to Databricks without physically moving it into the Databricks file system.
Use SQL commands in Databricks to load the data from the external table into a Delta Lake table. Delta Lake provides ACID transactions and scalable metadata handling, which is beneficial for managing your data efficiently within Databricks.
Finally, verify the integrity and consistency of the data loaded into the Delta Lake table. Run queries to ensure that the data has been accurately transported, and perform any necessary data cleaning operations to address discrepancies or formatting issues.
By following these steps, you can efficiently move data from Confluence to Databricks Lakehouse without relying on 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.
Confluence defines your reason for being so you can form actionable business strategies and it can share performance results and customer insights with stakeholders. Confluence presents your business vision and help your team understand your strategic plan. It is your remote-friendly team workspace where knowledge and collaboration meet. Confluence is purpose-built for teams which requires a secure and reliable way to collaborate on mission-critical projects. Confluence sites are entirely protected by privacy controls and data encryption, and meet industry-verified compliance standards.
Confluence's API provides access to a wide range of data, including:
1. Pages: Confluence pages are the primary unit of content in the platform, and the API allows developers to create, read, update, and delete pages.
2. Spaces: Spaces are containers for pages and other content, and the API provides access to space metadata, permissions, and other settings.
3. Users and groups: The API allows developers to manage users and groups, including creating, updating, and deleting them.
4. Comments: Confluence pages can have comments, and the API provides access to comment metadata and content.
5. Attachments: Pages can have attachments, such as images or documents, and the API allows developers to manage attachments.
6. Labels: Labels are used to categorize content in Confluence, and the API provides access to label metadata and allows developers to add or remove labels from pages.
7. Search: The API provides a search endpoint that allows developers to search for pages, spaces, and other content in Confluence.
Overall, Confluence's API provides access to a wide range of data that developers can use to build custom integrations and applications that extend the functionality of the platform.
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:
