Quality assurance (QA) activities are often seen as a necessary yet time-consuming and expensive process. This outlook can often limit the potential and impact of good QA. In reality, QA is not just about finding and fixing bugs; it’s a strategic value-add that can drive innovation, improve product stickiness, accelerate time-to-market, and enhance overall product quality. Yet, finding the balance between a necessary task and strategic impact can be challenging for many organizations.

Data fabric is an architecture that integrates different data systems and tools. It provides unified access to data stored across various locations to organize, manage, and govern it without moving it to a central database or data warehouse or changing its format. Data fabric relies on metadata to ‘understand’ the data’s structure, lineage, and meaning across various sources. This information enables informed decision-making and optimized data usage.

The modern-day SDLC methodologies such as agile, CI/CD, and DevOps are flexible enough to incorporate change requests in each sprint, which increases the probability of introducing errors in existing functionality. This makes validating existing functionality, detecting newly introduced bugs, and resolving them mandatory in each build release. Whether manual or automated, such software testing is widespread and referred to as regression testing.

Over the past two and a half decades, Java has consistently been ranked as one of the top 3 most popular programming languages in the world . As a compiled language, any source code written in Java needs to be translated (i.e., compiled) into machine code before it can be executed.

Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities. Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives.

Modern applications store a lot of data, yet databases continue to be the primary source of data that these applications need to function. This is where database APIs come in, making it easier for applications and services to retrieve and manipulate data. A database API’s biggest advantage is that it eliminates database operations’ dependence on proprietary methods and provides a unified interface, streamlining data operations. Here’s everything you need to know about database APIs.

Tips for writing natural language prompts to generate SQL and charts using BigQuery data canvas’ NL2SQL and NL2Chart capabilities.

Here’s a look at how we scored and our key takeaways from the report We're thrilled to be recognized as a Leader in The Forrester WaveTM: API Management Software, Q3 2024! We believe this recognition validates our commitment to empowering businesses with the tools they need to unlock the full potential of their APIs.

A balanced combination of manual and automated testing is the way to go if you want to achieve higher test coverage and efficiency. They cover for each other. Manual testing is easy to learn and do, but it is highly likely to create inconsistent test results, and sometimes it takes a lot of time and effort. On the other hand, automation testing requires a lot of technical expertise to create and maintain scripts, but it checks bugs much faster with higher consistency.

Spring Boot is an open source Java framework designed to facilitate the creation of stand-alone, production-grade Spring applications. Whether you’re developing a RESTful API, a microservice, or a batch task, Spring Boot simplifies the process. While platforms like Microsoft Azure and Amazon Web Services (AWS) offer cloud deployment options for Spring Boot applications, Choreo provides specialized features and tools tailored specifically for Spring Boot, streamlining deployment and management.