Systems | Development | Analytics | API | Testing

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Rainforest QA vs. hiring a QA engineer

When you’re ready to make the transition from manual testing to test automation, it’s natural to consider hiring. A QA engineer — who has the technical skills to write and maintain automated tests in an open-source framework — can take the burden of end-to-end test management off of your development team, allowing them to ship more code, faster. But hiring a good QA engineer is — often prohibitively — expensive.

Fintech Testing in 2024 - 3rd QA Meetup at Surat

Join us as we recap the highlights from our latest QA meetup event at Alphabin Technology Consulting office in Surat In this video, we bring you insights from an excellent gathering of QA enthusiasts and specialists who convened to exchange ideas, insights, and connections. Dive into the lively discussions about When and How to Start Accessibility Testing, Test Automation, Performance, and Security in Fintech Testing.

Federated Connectivity: Unlocking Data Silos with API Gateways

"The whole is more than the sum of its parts." Aristotle is credited with this quote, and it's true in the world of data. Legacy systems typically approached their role in a limited manner. Each system was intended to be used by a certain user set and handle well-defined processes and associated data. The result was a disintegrated environment with data being difficult to obtain, and frequently out of date. The parts couldn't easily cooperate to make a whole.

Reimagine Batch and Streaming Data Pipelines with Dynamic Tables, Now Generally Available

Since Snowflake’s Dynamic Tables went into preview, we have worked with hundreds of customers to understand the challenges they faced producing high-quality data quickly and at scale. The No. 1 pain point: Data pipelines are becoming increasingly complex. This rising complexity is a result of myriad factors.

Data Accessibility: A Hurdle Before SAP's AI Integration

Unlocking the power of AI within SAP for your team requires overcoming a significant hurdle: data accessibility. SAP data’s complexity, spread across various modules, creates silos of information that your team might struggle to understand and utilize effectively. Inaccessible or misaligned SAP data will hinder your AI system’s ability to learn and deliver valuable results specific to your organization.

How No-Code API Tools Automatically Generate APIs

At some point, anyone who has enjoyed using a computer has wondered if they could build their own app. But software development becomes intimidating fast if you’re not a programmer. Fortunately, there is a wide range of no-code platforms available today. Even in the enterprise, there’s demand for no-code development. As APIs have become one of the most important components of the modern application ecosystem, several no-code API solutions are now available.

Impact of AI on #SoftwareTesting: Are Testers Ready? | #QonfX 2024

Join industry experts Rahul Verma, Navin Nair, Nagabhushan Ramappa, and our amazing host Balaji Ponnada in an insightful panel discussion on "AI's Impact on Testing, Tester Roles, and Tester Readiness." In this session, the panelists discuss how artificial intelligence (AI) has revolutionized software testing, sharing the complexities and opportunities of AI-driven testing environments. Through real-world examples and interactive discussion, they explore the changing role of testers in the AI era and provide valuable insights into the future of software testing.

How to Get Data Out of ERP Systems with APIs

Enterprise Resource Planning (ERP) systems are designed to streamline business operations by centralizing data across an organization. However, extracting and utilizing this data can be a complex and time-consuming process. One solution to this problem is the use of APIs (Application Programming Interfaces), which allow you to get data out of ERP systems and other software applications.

Data Prep for AI: Get Your Oracle House in Order

Despite the transformative potential of AI, a large number of finance teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.