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Benefits of API security testing

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5 minutes, 24 seconds read

Cybersecurity is a growing concern amid businesses from all quarters. It has become even more crucial amid COVID-19 pandemic when many businesses relying solely on the online medium for business transactions. Organizations all over the world have lost nearly 1$ trillion due to cyberattacks in 2020. Data security breaches, payment hacks, and security risks can cripple your business and ruin your brand image. Hence, it has become business-critical to get your apps tested thoroughly for any security loophole.

Why API security testing?

Application Programming Interface or API, as the name suggests, is the intermediating software or application that allows two endpoints to communicate with each other. Each time, we use an app like social networking app, gaming app, or any other application to send or receive the message, our action passes through a programming interface that connects sender and receiver.

That means securing the data sent to the receiver through an API is very important. Hackers may extract the data and use it in their illegal acts. Ensuring the security of an API before, during, and after the production of any project through testing is what we are going to discuss in detail under API security testing.

Developers use security tests to ensure their applications and web services are 100% safe from unwanted attacks and are not disclosing any sensitive information to the hacker. API Security tests pass through various types of security checks. Each of them has been designed to detect certain vulnerabilities. One security test with multiple security scans gives you the guarantee of your service and you can get assured that your services are well-protected against malicious attacks.

API Security Testing is the only way to ensure that any web service is protected from foreign attacks or not before communication is established between the two endpoints.

Let us highlight the Benefits of API security testing:

  1. Tester can detect error without the user interface

The main advantage of API security testing is that the tester can easily access the application without the user’s involvement. Under this testing system, testers can detect the error at an early stage without running the software application. This is beneficial because it helps QA rectify the error before it impacts the Graphical User Interface.  

  1. Removes vulnerabilities

API testing is done under extraordinary conditions and inputs, which protects the application from unlawful code. API testing adds connecting limits to the software and removes any type of vulnerabilities.

  1. Less time-consuming than functional GUI testing

API testing consumes less time as compared to functional GUI testing. Under GUI testing, developers poll all webpages elements so it takes time. API, on the other hand, requires less coding, and so deliver faster results. A team of engineers analyzed the test results and found that 3000 API test results consumed 50minutes whereas 3000 GUI test consumed 30 hours. 

  1. Testing cost is reduced

As we just said, API testing requires less code than GUI so we can expect to get faster results. The faster results mean, less time, and overall, less testing cost. Early error detection reduces the manual testing cost as well. 

  1. Does not depend on Technology 

API Security Testing uses XML or JSON languages consisting of HTTP requests and responses. These languages do not depend on technology and are used for development. That means testers can use any core language while using automated API testing services for an application.

With so many benefits of API security Testing, the demand continues to rise and so is the challenge to close security holes that may impact the safety of the corporate and customer data. Businesses need to make sure that their API testing does not create any security problem and is flawless.

We are here with some of the best practices for API security testing:

  1. Think out of the box 

It is generally seen that developers work on one small set of services while testing to make that particular set as strong as possible. The problem these days is, front ends and back ends are connected to so many components that Hackers can easily find out one or the other way to enter the software; so developers need to think out of the box to fix this issue. 

  1. Open communication between a tester and a developer can solve the problem

Communication has been a challenge. It can be used as a solution when an open channel of interaction between the testers and developers is established to reduce the defects making the API security testing process easier and faster. 

  1. Thorough check-up of add-on software

The easy usage of API often creates problems. One popular usage is that allows third parties to write add-on apps. Mobile solutions and social media platforms, like Facebook, Instagram, depend on others to add value to their platform. Hackers grab those opportunities and try to get maximum information from such systems or platforms.

  1. Take standards judiciously

Suppliers work on standards to improve API security, but not all follow these standards. The Internet Engineering Task Force’s OAuth is an open authorization standard, that gives clients secured restricted access to system resources without highlighting their credentials. Most Internet users use this standard to log into third-party websites via their Microsoft, Google, Facebook, or Twitter accounts.

The problem comes when the standard is based on HTTP, which already has errors, and APIs add extra feasibility for hackers in such cases.

  1. Try to get authorization and authentication on the front end

Developers tie APIs into other elements of the software. To secure any code, developers need to adopt a strong approach. The process starts with authentication, which checks to see if a person is the same as he or she says they are. Enterprises have moved from simple password systems to multistep authentication emphasizing biometric solutions like fingerprints. Once the authentication process is done, they move to the authorization check to get access to more information.

  1. Don’t forget to check data on the back end

Developers work hard to protect the data on the front end but hackers are smart and they find their way to attack your system. Businesses must incorporate another checkpoint on the back end. If any hacker accesses confidential data, it should have value only when he or she moves the data to their systems. In simple words, we can say, if you miss any criminal on the front end, you still have a chance to catch him or her on the back end.

These are some of the API testing best practices that every business should adopt to close the security holes that may impact their application in the future.

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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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