![]() ![]() In general, however some common types of test data include input values output values expected results, and error conditions. This will vary depending on the specific application under test. When generating test data it is important to consider the type of data that will be most useful for testing the software application. This can be more efficient than manual creation but it may still require some manual input. ![]() Another way to generate test data is through automated means such as using a tool or script. This involves creating test data by hand which can be time-consuming and error-prone. There are various ways in which test data can be generated. The purpose of generating test data is to have a set of data that can be used to test the functionality of a software application. This can be done manually or through automated means. ![]() With this example above and the 5 previously mentioned essential constructs in mind, the sequence is:ĭ data generation is the process of creating test data for use in software testing. In this example, with is utilizing and methods available from Java Faker see API docs and then compare the class methods with configuration above such as Name.full_name, Beer.name, etc. This also highlights the differences between with and matching in configuration. Notice how the order event customer_id value references the previously generated customer key field? (Hint: with this kind of data generation, we can test our join code!) There are 5 essential constructs to understand when customizing key-value data generation:Īttribute the name of the field to generate dataįor example, consider the configuration of the following: MSK Data Generator can be deployed in a variety of ways including:ĭeploying in a container running in Elastic Container Serviceĭeploying as a Kafka Connect source connector in MSK ConnectĬustomizing Data Generation Configuration Knowing more about Java Faker capabilities and options will be helpful. Like many dynamic data generation projects, the key component is the use So basic knowledge of Kafka Connect will be helpful. MSK Data Generator is deployed and configured as a Kafka Connect Source, This generator easy to use with Amazon MSK. This project can likely be used outside of Amazon MSK, but to start at least, the focus will be making (Nothing against Clojure mind you! It's just more folks know Java.) Stream processor applications (in Kinesis Data Analytics for Apache Flink or Kinesis Data Analytics Studio for example) which perform joins.įor an example, see AWS Big Data Blog Query your Amazon MSK topics interactively using Amazon Kinesis Data Analytics Studio Why translate to Java?īy translating to Java, the hope is we open up the potential of wider communityĬollaboration. Multiple streams of "joinable" data is especially useful when building The dynamically generated Customer event customer_id can reference the Order event customer_id. (AKA: cross-reference, reference-able, joinable, etc.)įor example, we can generate one stream of Order events containing a customer_id (as well as price, sku, quantity, etc.)Īnd at same time, we can generate a different stream of Customer events containing a customer_id (as well as first name, last name, location, etc.) The killer feature is being able to generateĮvents which reference other generated events. ![]() MSK Data Generator is a translation of the awesome Voluble Apache Kafkaĭata generator from Clojure to Java. ![]()
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