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- Log into Looker: Sign in to your Looker account.
- Create or Choose a Report: Identify the report that you want to export to Google Sheets.
- Refine Your Data: Apply any filters or adjustments to ensure the data is exactly what you need for your Google Sheet.
- Run the Report: Execute the query to generate the report.
- Export Options: Click on the "Gear" icon or "Explore Options" and find the export option.
- Choose Format: Select the format in which you want to export your data. For this purpose, choose CSV as it is easily imported into Google Sheets.
- Download the File: Click on the export button to download the CSV file to your local machine.
- Open Google Sheets: Go to Google Sheets and sign in with your Google account.
- Create a New Sheet: Click on the “+” button to create a new sheet or open an existing one where you want to import the data.
- Go to File: In your Google Sheet, click on "File" in the top menu.
- Import Data: Select "Import" from the dropdown menu.
- Upload the CSV File: In the import window, go to the "Upload" tab and either drag your CSV file into the space provided or click "Select a file from your device" to upload the exported CSV file from Looker.
- Choose Import Options: Once the file is uploaded, a dialog will appear with several options. You can choose to create a new spreadsheet, insert new sheets into the current spreadsheet, replace the current sheet, or append the data to the current sheet. Select the option that best fits your needs.
- Customize Settings: You may also have options to select the separator character (typically a comma for CSV files), which is important for correctly parsing the data.
- Import: Click on the "Import Data" button to finalize the import process.
- Check the Imported Data: Ensure that the data looks correct in the Google Sheet. Verify that the columns and rows are aligned properly and that the data types are as expected.
- Clean Up: If there are any headers or footers that were imported from Looker that you do not need, delete them.
- Format the Data: Apply any necessary formatting to the Google Sheet to make the data presentable and easy to work with.
- Open Script Editor: In your Google Sheet, go to "Extensions" > "Apps Script".
- Write the Script: Use Google Apps Script to write a script that fetches the CSV data from a specific URL (you can obtain a permanent link to your Looker report if your Looker setup allows it) and imports it into your Google Sheet.
- Trigger the Script: Set up a time-driven trigger to run the script at regular intervals.
- Save Your Work: Make sure to save your Google Sheet.
- Share the Sheet: If you need to share the imported data with others, click on the "Share" button and set the appropriate permissions.
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.
Looker is a Google-Cloud-based enterprise platform that provides information and insights to help move businesses forward. Looker reveals data in clear and understandable formats that enable companies to build data applications and create data experiences tailored specifically to their own organization. Looker’s capabilities for data applications, business intelligence, and embedded analytics make it helpful for anyone requiring data to perform their job—from data analysts and data scientists to business executives and partners.
Looker's API provides access to a wide range of data categories, including:
1. User and account data: This includes information about users and their accounts, such as user IDs, email addresses, and account settings.
2. Query and report data: Looker's API allows users to retrieve data from queries and reports, including metadata about the queries and reports themselves.
3. Dashboard and visualization data: Users can access data about dashboards and visualizations, including the layout and configuration of these elements.
4. Data model and schema data: Looker's API provides access to information about the data model and schema, including tables, fields, and relationships between them.
5. Data access and permissions data: Users can retrieve information about data access and permissions, including which users have access to which data and what level of access they have.
6. Integration and extension data: Looker's API allows users to integrate and extend Looker with other tools and platforms, such as custom applications and third-party services.
Overall, Looker's API provides a comprehensive set of data categories that enable users to access and manipulate data in a variety of ways.
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:
