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Begin by navigating to the Amplitude dashboard. Use Amplitude's built-in data export feature to download your event data. You typically export this data in a JSON or CSV format. Ensure you have the necessary permissions to access and export the data.
Store the exported data locally on your machine or a secure server. Organize the data files in a consistent directory structure to make it easy to upload to Amazon S3 later. This step helps in ensuring that your data is ready and accessible for the next steps.
Log in to your AWS Management Console and navigate to the S3 service. Create a new S3 bucket if you do not have one already dedicated to storing this data. Ensure you configure the bucket with the appropriate permissions and settings for your use case, such as enabling versioning or setting up lifecycle rules if necessary.
Use the AWS Management Console or AWS CLI to upload your exported Amplitude data files to the S3 bucket you created. If using the CLI, a command like `aws s3 cp /path/to/local/data s3://your-bucket-name/ --recursive` can upload the files recursively. Organize the data within the bucket as needed, possibly by date or event type.
In the AWS Glue Console, set up a new Glue Crawler. This crawler will catalog the data stored in your S3 bucket. Specify the S3 path to your data and select the appropriate IAM role that grants Glue permission to access the data. Run the crawler to populate Glue Data Catalog with the schema of your data.
Within the Glue Console, create a new ETL (Extract, Transform, Load) job. Define the source as the table created by the Glue Crawler. Specify the target as either another S3 bucket or a more structured data store like Amazon Redshift or an RDS database if needed. Use Glue's built-in transformations to clean and structure the data as required.
Execute the Glue ETL job and monitor its progress through the AWS Glue Console. Check the job metrics and logs to ensure that the process completes successfully. Once the data is processed and loaded into the desired destination, verify the data integrity and make any necessary adjustments to the ETL job for future runs. This guide provides a direct method to move data from Amplitude to S3 using AWS Glue, leveraging AWS's native tools and services without involving third-party connectors.
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.
Amplitude is a cross-platform product intelligence solution that helps companies accelerate growth by leveraging customer data to build optimum product experiences. Advertised as the digital optimization system that “helps companies build better products,” it enables companies to make informed decisions by showing how a company’s digital products drive business. Amplitude employs a proprietary Amplitude Behavioral Graph to show customers the impact of various combinations of features and actions on business outcomes.
Amplitude's API provides access to a wide range of data related to user behavior and engagement on digital platforms. The following are the categories of data that can be accessed through Amplitude's API:
1. User data: This includes information about individual users such as their demographics, location, and device type.
2. Event data: This includes data related to user actions such as clicks, page views, and purchases.
3. Session data: This includes information about user sessions such as the duration of the session and the number of events that occurred during the session.
4. Funnel data: This includes data related to user behavior in a specific sequence of events, such as a checkout funnel.
5. Retention data: This includes data related to user retention, such as the percentage of users who return to the platform after a certain period of time.
6. Revenue data: This includes data related to revenue generated by the platform, such as the total revenue and revenue per user.
7. Cohort data: This includes data related to groups of users who share a common characteristic, such as the date they signed up for 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:
