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Everything You Need to Know About Test Automation as a Service (TAaaS)

6 minutes, 24 seconds read

The enterprise-level digitization and adoption of DevOps and Agile have made test automation a necessity in today’s time. It reduces the time-to-market and hence the production cost. One can execute test automation on web/mobile/desktop application, performance, and APIs at once; generating a comprehensive report based on functionality, time, and build.

Test Automation as a Service is an on-demand automation offering that overrules manual testing. But before, let’s look at key problems with manual testing-

  • It demands manual effort during release/enhancement.
  • Manual testing requires greater resources.
  • Testers usually avoid lengthy testing because of time and resource constraints.
  • It has a limited scope of tests and cannot accomplish in-depth testing. In other words, manual testing has lesser coverage. 
  • It requires testing the application on multiple computers, mobiles, tablets, etc. with different configurations.
  • The scripts are not reusable, i.e. every time testing will require new scripts for instances like the change in OS version.

How Automation Speeds-up Testing by 70%?

Testing automation not only reduces manual efforts but also speeds-up the entire testing process. Here’s how.

  • It cuts down the repetitive tasks/testing, which the test engineers used to do at the time of product release or enhancement.
  • TAaaS covers lengthy testing, which was unattended by manual testing.
  • It also increases the testing coverage with fewer resources.
  • It finds critical defects at an early stage of testing.
  • Its scripts are reusable. Testers need not code new scripts every time for system upgrades and OS version changes. Tests can recur without errors.

The following are the test automation tools categorized application-wise.

Web-based Application Automation

Selenium Webdriver is an open-source tool for automating web-based applications only. Users can test web applications using any web browser.

  • Types of OS for testing in Selenium: Windows, Mac, Linux
  • Browsers supported for testing: Mozilla Firefox, Internet Explorer, Google Chrome, Safari, Opera

Additional Resource: Selenium Testing Automation Framework

Mobile-based Application Automation

Appium is an open-source tool to test web applications running in mobile browsers. It also supports the automation of native and hybrid mobile applications developed for iOS and Android OS. Appium uses Selenium API to test the applications.

You can test a mobile application in just four steps-

  1. Write your test script on Eclipse.
  2. Connect your device to Computer (PC).
  3. Start Appium server.
  4. Run your script (test cases).

Appium supports Chrome browser for testing Android apps and Safari for iOS.

API Automation

Testing is difficult in Java as compared to dynamic languages like Ruby and Groovy. REST Assured is a Java library that provides a domain-specific language (DSL) for writing powerful, maintainable tests for RESTful APIs. Most of the web services are based on REST architecture. Everything is a resource in the RESTful web service. It is lightweight, scalable, and allows creating easy to maintain web apps. How it works-

  • REST Assured captures the (JSON) response of the API call.
  • It validates if the response status code is equal to 200.

Windows App Automation

Winium is a Selenium-based open-source automation framework for the Windows platform. You can test your Windows App following these steps-

  • Write your test script on Eclipse.
  • Start Winium Desktop Driver.
  • Set the path of application in the script.
  • Using “UISpy” inspect the elements.
  • Run your script (test cases).

Frameworks for Test Automation as a Service

A framework is a collection of reusable components that make the overall test execution and development easy and efficient. It is a custom tool designed by Framework Developers to simplify test automation processes.

A framework is a well-organized structure of components. For instance, one driver file executes an entire batch of commands without any manual intervention. The following are the types of frameworks along with the use scenarios specific to Test Automation as a Service protocol.

Data Driven Framework

This automation framework focuses on keeping test script logic and test data separate. For testing, it inputs data sets from a variety of sources like MS Excel Sheets, MS Access Tables, SQL Database, XML files, etc.

When the same test case needs to be executed multiple times with different data sets, the data-driven framework provides data to the test scripts.

Modular Driven Framework

Here, testers create test scripts for individual, small modules of the application. These small scripts (or test modules) can be combined into a master script to test specific scenarios or end-to-end testing. The test modules can also act as a library of functions to use in the future.

When applications contain a lot of modules, a modular framework is suitable for testing.

Keyword Driven Framework

This framework is also known as table-driven testing because it uses a table format to define keywords or action words for each function that the tester needs to execute. It’s a user-friendly framework. Test Engineers can develop test scripts even with limited knowledge of automation tools and programming language.

Behavior Driven Development Framework (Cucumber Framework)

It is a testing framework which supports Behavior Driven Development (BDD). It allows the tester to define application behavior in plain English and simple grammar as defined in Gherkin language. The following are the components of the cucumber framework.

Feature Files: It is an entry point to the cucumber tests. Here, the tester describes the test cases in a descriptive language like English. Feature files are important because they serve as an automation test script as well as live documents. A feature file can contain one or many scenarios. The following is a sample feature file.

#Author: your.email@your.domain.com

#Keywords Summary:

#Feature: List of scenarios.

#Scenario: Business rule through list of steps with arguments.

#Given: Some precondition step

#When: Some key actions

#Then: To observe outcomes or validation

#And, But: To enumerate more Given, When, Then steps

#Scenario Outline: List of steps for data driven as an Examples and <placeholder>

#Examples: Container for s table

#Background: List of steps run before each of the scenarios

#””” (Doc Strings)

#| (Data Tables)

#@ (Tags/Labels): To group Scenarios

#<> (placeholder)

#””

## (Comments)

#Sample Feature Definition Template

@tag

Feature: Title of your feature

I want to use this template for my feature file

  @tag1

  Scenario: Title of your scenario

Given I want to write a step with precondition

And some other precondition

When I complete action

    And some other action

And yet another action

Then I validate the outcomes

And check more outcomes

  @tag2

  Scenario Outline: Title of your scenario outline

Given I want to write a step with <name>

When I check for the <value> in step

Then I verify the <status> in step

Examples:

   | name | value | status |

   | name1 | 5 | success |

   | name2 | 7 | Fail    |

Apart from these testers also use Linear Scripting Framework and Hybrid Testing Framework for Test Automation.

Step Definitions: A Step definition is a small piece of code with a set pattern. The pattern links the Step Definition to all the matching steps. Cucumber executes a Step according to Gherkin Steps.

Test Runner: The JUnit runner uses the JUnit Framework to run cucumber. It is an open-source unit testing framework for Java. It is useful for writing and running repeat/reusable test cases. It requires a single empty class with an annotation-

@RunWith(Cucumber.class)

@CucumberOptions(features=”features”, glue = {“stepDefinitions”})

public class TestRunner {}

Also read – How to perform load testing on applications.

Best Practices for Creating an Effective Testing Framework

  • Integrate Appium and Selenium to cover mobile and web testing together.
  • Integrate REST Assured for API automation to ensure APIs are working as per set functionalities. It saves a great deal of time and resources.
  • Integrate Winium/AutoIt for testing standalone applications.
  • Integrate Cucumber for behaviour-driven development.
  • Use Page Object Model to create generic packages of common classes (codes) that can be used over all the test scripts. It helps to achieve reusability of codes.
  • Integrate JUnit to manage test cases and generate reports.
  • Integrate Maven or Jenkins to achieve continuous testing. Jenkins also helps to run the script for lengthy testing and generate extended reports delivered to all stakeholders. It is useful for tests that take hours to days to complete.

We specialize in business-specific test automation services. Drop us a word at hello@mantralabsglobal.com to streamline and accelerate your product/solution launch.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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