Best practice 2.2 – Create test data and provision staging environment - Data Analytics Lens

Best practice 2.2 – Create test data and provision staging environment

Using a known and unchanging dataset for test purposes helps ensure that when changes are made to the analytics environment or analytics application code, test results can be compared to previous versions.

Confirming that the test datasets accurately represent real-world data allows the analytics workload developer to confirm the outcomes from the analytics job, as well as comparing test results to previous versions.

Your organization should use a staging environment for user access testing. Your organization should create logically separated AWS accounts for your development, test, staging, and production environments depending upon your development standards.

For more details, refer to the following information:

AWS Whitepaper: Establishing your best practice AWS environment

Suggestion 2.2.1 – Use a curated dataset to test application logic and performance improvements

Analytics projects that are being developed should use the same curated dataset to compare results between tests of different versions of your code. Using the same dataset for all tests allows demonstrating improvement over time, as well as making it easier to recognize regressions in your code.

To help control access to sensitive data, your organization should use data masking techniques when restoring development data to non-production environments. More information on data minimization techniques can be found in Security.

For more details, refer to the following information:

Suggestion 2.2.2 – Use a random sample of recent data to validate application edge cases and help ensure that regressions have not been introduced

Use a statistically valid random sample of recent data to confirm that the analytics solution continues to perform under real-world conditions. Using a sample of recent data also allows you to recognize whether your dataset characteristics have shifted, or whether anomalous data has recently been introduced to your data.

For more information, see the AWS Machine Learning Blog: Create random and stratified samples of data with Amazon SageMaker Data Wrangler.