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Begin by logging into your Zoom account and navigating to the Reports section. Depending on your requirements, you can export various types of data such as meeting reports, participant details, or usage reports. Use the "Export" option to download these reports as CSV files. Save these files to a secure location on your local machine.
Ensure your local environment is set up to work with the data files. Install necessary tools such as a text editor for CSV files or Jupyter notebooks for any data manipulation. Verify that you have access to the Databricks CLI or UI, and ensure your files are accessible from the machine you plan to use.
Open the downloaded CSV files and inspect the data. Use a spreadsheet tool or a script to clean and format the data as needed. Remove any unnecessary columns, handle missing values, and ensure that the data types are consistent and suitable for your analysis needs in Databricks.
Access your Databricks environment and navigate to the workspace where you want to store and analyze the data. Make sure you have the necessary permissions to create new folders and upload data files. If you haven�t already, set up a cluster with the appropriate configuration to process your data.
In the Databricks UI, use the "Upload Data" option to transfer your cleaned CSV files into your Databricks workspace. You can upload these files directly to DBFS (Databricks File System) by navigating to the "Data" section, selecting "Add Data," and choosing "Upload File" from your local machine.
Once your data is uploaded, use SQL within Databricks to create external tables that reference the CSV files now stored in DBFS. This involves defining the schema of your data and specifying the path to the files. For instance, you can execute commands in a notebook that creates tables using the syntax:
```sql
CREATE TABLE zoom_data USING CSV OPTIONS (path '/path/to/your/csv', header 'true', inferSchema 'true');
```
Finally, verify that the data has been successfully imported by running queries on your newly created tables. Use SQL or PySpark to perform initial analyses and ensure that the data is correctly structured and complete. Begin your data analysis tasks, leveraging the full capabilities of the Databricks Lakehouse.
By following these steps, you can manually move and utilize your Zoom data within the Databricks Lakehouse environment 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.
Zoom offers a communications platform that connects people through video, voice, chat, and content sharing. It has an easy, reliable cloud platform for video and audio conferencing, collaboration, chat, and webinars across mobile devices, desktops, telephones, and room systems. Zoom unifies cloud video conferencing,simple online meetings, and group messaging into one easy-to-use platform. The company's mission is to create a people-centric cloud service that transforms the real-time collaboration experience and improves the quality and effectiveness of communications.
Zoom's API provides access to a wide range of data related to Zoom meetings, webinars, users, and accounts. The following are the categories of data that can be accessed through Zoom's API:
1. Meetings: Information related to Zoom meetings, such as meeting ID, topic, start and end time, duration, participants, and recording.
2. Webinars: Data related to Zoom webinars, including webinar ID, topic, start and end time, duration, attendees, and recording.
3. Users: Information about Zoom users, such as user ID, name, email address, and account type.
4. Accounts: Data related to Zoom accounts, including account ID, name, email address, and billing information.
5. Reports: Various reports related to Zoom meetings and webinars, such as attendance reports, participant reports, and usage reports.
6. Recordings: Information related to Zoom meeting and webinar recordings, including recording ID, name, duration, and download links.
7. Settings: Data related to Zoom account and meeting settings, such as default meeting settings, user settings, and account settings.
Overall, Zoom's API provides a comprehensive set of data that can be used to analyze and optimize Zoom meetings and webinars, as well as manage Zoom accounts and users.
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:
