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Start by logging into your Plausible Analytics account. Navigate to the dashboard where your website data is displayed. This is the starting point for accessing the data you want to move to Google Sheets.
Within the Plausible dashboard, look for the export option. Plausible provides an export feature that allows you to download your analytics data in CSV format. This option is usually found in the settings or data view section. Click on 'Export' and download the CSV file to your computer.
Go to Google Sheets by visiting the URL: sheets.google.com. Log in with your Google account if you are not already logged in. You will need to create a new spreadsheet to import your Plausible data.
In Google Sheets, click on the 'Blank' option to create a new spreadsheet. This will serve as the destination for your Plausible data.
In the new spreadsheet, click on 'File', then choose 'Import'. Select the 'Upload' tab, and then click the 'Select a file from your device' button. Choose the CSV file you downloaded from Plausible, and import it. In the import settings, ensure you choose the correct options for delimiter detection (usually comma for CSV files) and decide if you want to replace, append, or insert data.
Once the data is imported, review the spreadsheet to ensure that all data has been correctly placed in the appropriate columns. You may need to adjust column widths, apply formatting to dates and numbers, or add headers for clarity.
If you frequently need to move data from Plausible to Google Sheets, consider using Google Apps Script to automate the process. You can write a script to fetch data from Plausible's API and update your Google Sheet automatically on a schedule. This step involves programming knowledge and is optional for users comfortable with scripting.
By following these steps, you can successfully transfer your Plausible data to Google Sheets 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.
Appreciable Analytics is an open-source project dedicated to making web analytics more privacy-friendly. Our goal is to reduce corporate surveillance by providing an alternative web analytics tool that doesn't come from the AdTech world. Trusted by thousands of paying customers. We are completely independent, self-funded, and bootstrapped. The legal entity is incorporated in Estonia, while our team works remotely and flexibly.
Plausible's API provides access to a variety of data related to website traffic and user behavior. The following are the categories of data that can be accessed through Plausible's API:
1. Site Metrics: This category includes data related to the overall performance of a website, such as the number of page views, unique visitors, bounce rate, and average session duration.
2. Traffic Sources: This category includes data related to the sources of traffic to a website, such as search engines, social media, direct traffic, and referral traffic.
3. User Behavior: This category includes data related to user behavior on a website, such as the pages visited, time spent on each page, and the actions taken on the website.
4. Geolocation: This category includes data related to the geographic location of website visitors, such as the country, region, and city.
5. Devices: This category includes data related to the devices used by website visitors, such as desktop, mobile, and tablet.
6. Browsers: This category includes data related to the browsers used by website visitors, such as Chrome, Firefox, Safari, and Internet Explorer.
Overall, Plausible's API provides a comprehensive set of data that can be used to analyze website traffic and user behavior, and to make data-driven decisions to improve website performance.
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:
