How to add A/B test significance level to Google DataStudio dashboard in 5 minutes

Step 1: write a scheduled query in BigQuery
First, you need to create a scheduled query in BigQuery that periodically gets the significance level of your test. I make the query run every day.The only thing to modify from the following code is how you get the metrics of your A/B test (I get them through SQL queries as I’m already logging them in BigQuery) and the name of your destination table.
Step 2: add the data to DataStudio
Create a new DataStudio dashboard (or go to an existing one) and choose your favorite chart to show the data. 
Step 3: get a coffee
I prefer matcha latte, honestly.
The important thing is that, it’s done! You now have live updates of the A/B significance level in DataStudio. Of course, this is not enough to know when the test has ended.

#ab-testing #statistics #google-data-studio #testing

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How to add A/B test significance level to Google DataStudio dashboard in 5 minutes

How to add A/B test significance level to Google DataStudio dashboard in 5 minutes

Step 1: write a scheduled query in BigQuery
First, you need to create a scheduled query in BigQuery that periodically gets the significance level of your test. I make the query run every day.The only thing to modify from the following code is how you get the metrics of your A/B test (I get them through SQL queries as I’m already logging them in BigQuery) and the name of your destination table.
Step 2: add the data to DataStudio
Create a new DataStudio dashboard (or go to an existing one) and choose your favorite chart to show the data. 
Step 3: get a coffee
I prefer matcha latte, honestly.
The important thing is that, it’s done! You now have live updates of the A/B significance level in DataStudio. Of course, this is not enough to know when the test has ended.

#ab-testing #statistics #google-data-studio #testing

Jon  Gislason

Jon Gislason

1619247660

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

The Ultimate Guide to Multiclass A/B Testing

One essential skill that certainly useful for any data analytics professional to comprehend is the ability to perform an A/B testing and gather conclusions accordingly.

Before we proceed further, it might be useful to have a quick refresher on the definition of A/B testing in the first place. As the name suggests, we can think of A/B testing as the act of testing two alternatives, A and B, and use the test result to choose which alternative is superior to the other. For convenience, let’s call this type of A/B testing as the binary A/B testing.

Despite its name, A/B testing in fact can be made more general, i.e. to include more than two alternatives/classes to be tested. To name a few, analyzing click-through rate (CTR) from a multisegment digital campaign and redemption rate of various tiers of promos are two nice examples of such multiclass A/B testing.

The difference in the number of classes involved between binary and multiclass A / B testing also results in a slight difference in the statistical methods used to draw conclusions from them. While in binary testings one would straightforwardly use a simple t-test, it turns out that an additional (preliminary) step is needed for their multiclass counterparts.

In this post, I will give one possible strategy to deal with (gather conclusions from) multiclass A/B testings. I will demonstrate the step-by-step process through a concrete example so you can follow along. Are you ready?

#hypothesis-testing #a-b-testing #click-through-rate #t-test #chi-square-test #testing

Live Webinar, Testing Superpowers: Using CLion to Add Tests Easily

CLion is great for refactoring C++ code to make it more maintainable.
But as someone asked in Arne Mertz’s “Refactoring C++ Code” webinar, “What can we do if we don’t have tests on the project and can’t easily check the changes introduced by refactorings?
In this webinar you will learn how to:

  • Add tests for untested code, quickly and safely.
  • Use CLion’s code coverage tools to guide your testing.
  • Use Approval Tests to get good coverage really quickly, and explore the behavior of the code.

#testing #clion #tests #live webinar #testing superpowers #add tests

Tamia  Walter

Tamia Walter

1596754901

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

  • Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
  • Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
  • While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

  • Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
  • Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
  • End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr