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Begin by exporting your data from Amplitude. Log in to your Amplitude account, navigate to the "Exports" section, and select the data range you want to export. Choose the CSV or JSON format for the export to ensure compatibility with AWS services. Download the exported file to your local system.
Set up your AWS environment to receive the data. Create an S3 bucket in the AWS Management Console where you will store the exported Amplitude data. Ensure the bucket has the appropriate permissions to allow data uploads from your system.
Install the AWS Command Line Interface (CLI) on your local machine. The AWS CLI will allow you to interact with AWS services from your terminal or command prompt. Download the installation package from the AWS website and follow the instructions specific to your operating system.
Set up your AWS CLI with the necessary credentials to access your AWS account. Use the `aws configure` command in your terminal or command prompt to input your AWS Access Key ID, Secret Access Key, default region, and output format. Ensure that the IAM role associated with your credentials has permission to access and upload data to S3.
Use the AWS CLI to upload the exported Amplitude data to your S3 bucket. Navigate to the directory containing the exported file and execute the following command: `aws s3 cp [your_file] s3://[your_bucket]/[your_directory/]`. Replace `[your_file]`, `[your_bucket]`, and `[your_directory]` with your file name, S3 bucket name, and desired directory structure respectively.
Set up AWS Glue to catalog your data for easy access and analysis. In the AWS Glue Console, create a new Glue Crawler and configure it to scan the S3 bucket where you uploaded the Amplitude data. Define the data format (CSV, JSON) and specify the database where you want the catalog to be stored. Run the crawler to populate the AWS Glue Data Catalog.
Use AWS Athena to query your data directly from the S3 bucket. In the Athena console, write SQL queries to interact with the data cataloged by AWS Glue. Ensure you set the correct database and table references corresponding to your Glue data catalog. This step allows you to analyze and gain insights from your Amplitude data stored in your AWS Data Lake.
By following these steps, you can effectively move and manage your data from Amplitude to an AWS Data Lake 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.
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:
