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  1. Stackups
  2. DevOps
  3. Testing Frameworks
  4. Testing Frameworks
  5. Capybara vs Pig

Capybara vs Pig

OverviewComparisonAlternatives

Overview

Capybara
Capybara
Stacks858
Followers191
Votes15
Pig
Pig
Stacks57
Followers111
Votes5
GitHub Stars686
Forks447

Capybara vs Pig: What are the differences?

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1. **Size and Weight**: Capybaras are larger in size and heavier than pigs, with capybaras typically weighing between 77 to 146 pounds and pigs generally ranging from 110 to 770 pounds.
2. **Dietary Preferences**: Capybaras are herbivores, primarily consuming grasses and aquatic plants, while pigs are omnivores, feeding on a varied diet that includes plants, insects, and even small animals.
3. **Natural Habitats**: Capybaras are found in South America, particularly in regions with ample water sources, such as marshes and rivers, whereas pigs are distributed globally, with both wild and domesticated populations.
4. **Social Behavior**: Capybaras are highly social animals, living in groups of up to 100 individuals, whereas pigs generally form smaller social units consisting of a sow and her offspring.
5. **Reproduction and Offspring**: Capybaras typically give birth to litters of around 2 to 8 offspring, while pigs have larger litters ranging from 6 to 12 piglets on average.
6. **Behavioral Traits**: Capybaras are known for their calm and peaceful demeanor, often being described as gentle giants, whereas pigs can exhibit more aggressive behavior, especially when threatened or in certain breeding situations.

In Summary, the key differences between Capybara and Pig lie in size, diet, habitat, social behavior, reproduction patterns, and overall behavioral traits.

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Detailed Comparison

Capybara
Capybara
Pig
Pig

Capybara helps you test web applications by simulating how a real user would interact with your app. It is agnostic about the driver running your tests and comes with Rack::Test and Selenium support built in. WebKit is supported through an external gem.

Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.

No setup necessary for Rails and Rack application. Works out of the box.;Intuitive API which mimics the language an actual user would use.;Switch the backend your tests run against from fast headless mode to an actual browser with no changes to your tests.;Powerful synchronization features mean you never have to manually wait for asynchronous processes to complete.
-
Statistics
GitHub Stars
-
GitHub Stars
686
GitHub Forks
-
GitHub Forks
447
Stacks
858
Stacks
57
Followers
191
Followers
111
Votes
15
Votes
5
Pros & Cons
Pros
  • 12
    Best acceptance test framework for Ruby on Rails apps
  • 2
    Synchronous with Rack::Test
  • 1
    Fast with Rack::Test
Cons
  • 1
    Hard to make reproducible tests when using with browser
Pros
  • 2
    Finer-grained control on parallelization
  • 1
    Proven at Petabyte scale
  • 1
    Open-source
  • 1
    Join optimizations for highly skewed data
Integrations
Rails
Rails
No integrations available

What are some alternatives to Capybara, Pig?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Robot Framework

Robot Framework

It is a generic test automation framework for acceptance testing and acceptance test-driven development. It has easy-to-use tabular test data syntax and it utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new higher-level keywords from existing ones using the same syntax that is used for creating test cases.

Karate DSL

Karate DSL

Combines API test-automation, mocks and performance-testing into a single, unified framework. The BDD syntax popularized by Cucumber is language-neutral, and easy for even non-programmers. Besides powerful JSON & XML assertions, you can run tests in parallel for speed - which is critical for HTTP API testing.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Cucumber

Cucumber

Cucumber is a tool that supports Behaviour-Driven Development (BDD) - a software development process that aims to enhance software quality and reduce maintenance costs.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

TestCafe

TestCafe

It is a pure node.js end-to-end solution for testing web apps. It takes care of all the stages: starting browsers, running tests, gathering test results and generating reports.

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