When it came to brainstorming growth ideas for QA engineers, I found myself stuck. Despite all my thinking, the only obvious transformation I could recall was moving into Project Management.

Discover the most common mistakes new QA engineers make from skipping edge cases to relying too much on tools and learn how to avoid them with practical, real-world advice.

Ah, the age-old question in the digital era: “Will robots take over my job?” In the field of Quality Assurance (QA), this concern has become even more specific: “Will AI replace QA testers?” With the rapid advancement of artificial intelligence in software testing, it’s natural for professionals to question the future of their roles. AI and test automation tools are increasingly capable of handling repetitive tasks, raising concerns about the long-term demand for manual testing and traditional QA practices.

Let’s face it software is everywhere. From mobile banking apps and online shopping platforms to healthcare systems and smart home devices, users demand seamless and bug-free experiences. Behind every smooth app is a strong Quality Assurance (QA) process making sure it works as expected. But traditional QA processes are being stretched to the limit. With frequent releases, complex architecture, and an ever-growing number of platforms and devices to test on, QA teams are under immense pressure.

In Part 1, we focused on securely setting up the AWS Glue and Snowflake integration, including IAM roles, and Secrets Manager. Now it’s time to move towards the core functionality—building the actual ETL pipeline using AWS Glue Studio and connecting it to Snowflake!

In today’s modern data landscape, organizations rely on fast, secure, and automated pipelines to process and analyze data. Two major players that help achieve this are AWS Glue, a serverless ETL (Extract, Transform, Load) service from Amazon, and Snowflake, a highly scalable and cloud-native data warehouse.