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How to test PySpark code with pytest

Start Data Engineering

Ensure the code’s logic is working as expected with tests 2.1. Test types for data pipelines 2.2. pytest: A powerful Python library for testing 2.2.1. Tests are identified by their name 2.2.3. Use fixture to create fake data for testing 2.2.4. Define items to be shared among tests with conftest.

Coding 130
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AMM Performance Testing Report

Ripple Engineering

This report presents a thorough overview of our testing approach, findings, and key takeaways. To achieve this goal, we adopted a new testing methodology compared to the previous performance tests which primarily focused on exploring the maximum software processing capacity. AMM IOU-IOU /W 1 PATH + 1 PATHSTEP 1.12.0

AWS 144
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Test Plan in Software Testing: Types and Steps to Create One

Knowledge Hut

Software testing evaluates and demonstrates that a software product or function performs as intended. Software  Software Testing training has advantages such as preventing problems, lowering development costs, and improving performance. What is a Test Plan in Software Testing? Types of Test Plans in Software Testing 1.

Project 96
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Advantages of Agile Testing Methodology

Knowledge Hut

What is Agile Testing? Agile Testing is also iterative and takes place after each sprint, rather than towards the end of the project. Testing courses iteratively helps to validate the client requirements and adapt to changing conditions in a better manner. Agile methods involve tasks executed in short iterations or sprints.

Project 98
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How to Find and Test Assumptions in Product Development

Assumptions mapping is the process of identifying and testing your riskiest ideas. A few simple ways to test if your assumptions are wrong. You'll learn: Why every product leader goes into a new project with untested, hidden assumptions. You'll learn: Why every product leader goes into a new project with untested, hidden assumptions.

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Hypothesis Testing Explained

KDnuggets

This brief overview of the concept of Hypothesis Testing covers its classification in parametric and non-parametric tests, and when to use the most popular ones, including means, correlation, and distribution, in the case of one sample and two samples.

IT 160
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Introducing SafeTest: A Novel Approach to Front End Testing

Netflix Tech

by Moshe Kolodny In this post, we’re excited to introduce SafeTest, a revolutionary library that offers a fresh perspective on End-To-End (E2E) tests for web-based User Interface (UI) applications. Inability to test CORS setup or GraphQL calls. Complex and unintuitive authoring and debugging of tests.

Coding 97
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Best Practices for Deploying & Scaling Embedded Analytics

You can get new capabilities out the door quickly, test them with customers, and constantly innovate. Embedding analytics in your application doesn’t have to be a one-step undertaking. In fact, rolling out features gradually is beneficial because it allows you to progressively improve your application.

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A Tale of Two Case Studies: Using LLMs in Production

Speaker: Tony Karrer, Ryan Barker, Grant Wiles, Zach Asman, & Mark Pace

Some takeaways include: How to test and evaluate results 📊 Why confidence scoring matters 🔐 How to assess cost and quality 🤖 Cross-platform cost vs. quality trade offs 🔀 and more!

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.

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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. Aarushi Kansal, AI leader, is here to explore ongoing testing and evaluation strategies tailored specifically for LLM-based applications. It’s no surprise given the non-deterministic nature of LLMs.