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Begin by exporting your data from Plausible Analytics. Plausible provides an API that you can use to extract data. Use the API to programmatically download the data in a format like JSON or CSV. Ensure you have the necessary API keys and permissions set up in Plausible to access and extract the data.
Log in to your AWS Management Console and navigate to S3. Create a new S3 bucket to store the exported data. Ensure that you configure the bucket with the appropriate permissions and policies to allow for data uploads. Note the bucket name and region, as these will be required for subsequent steps.
Use AWS CLI or a programming language SDK (such as Boto3 for Python) to upload the exported data from your local system to the S3 bucket. Ensure the data is uploaded to the correct bucket and path. You might use a script to automate this process if you're dealing with large datasets or require frequent uploads.
In the AWS Management Console, navigate to AWS Glue. Set up a new Glue Crawler that will scan your S3 bucket to determine the schema of your data. Configure the crawler to point to the path in your S3 bucket where the data is stored. Run the crawler to populate the AWS Glue Data Catalog with metadata about your dataset.
Once the data schema is available in the Glue Data Catalog, create a new Glue ETL job. This job will process and transform your data as needed. You can write the ETL script in Python (using PySpark) within the Glue console or use an external IDE. Configure the job to read from the source table created by the crawler.
In your Glue job script, define the output of your ETL process. Specify an S3 bucket and path where the transformed data should be stored. This could be the same bucket or a different one, depending on your data architecture needs. Ensure the output format is suitable for your downstream applications or analytics processes.
Use AWS Glue's scheduling feature to automate the execution of your ETL jobs at specified intervals. This will ensure that data from Plausible Analytics is regularly processed and updated in your S3 bucket. Additionally, monitor the execution of these jobs using AWS CloudWatch or Glue's built-in monitoring to ensure they are running correctly and troubleshoot any issues that arise.
By following these steps, you can efficiently move data from Plausible Analytics to AWS S3, and use AWS Glue to process it, 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:
