We've been thinking long and hard about software testing and automation here at Katalon. We've noticed a shift in the Software Testing industry from Test Automation to Autonomous Software Testing (AUST). By "autonomous," we mean software testing that is completely driven by Artificial Intelligence (AI) and Machine Learning (ML), without any human intervention.
Learn how Fivetran solution architects are supporting customers with implementing robust and scalable data platforms.
It is possible to become more efficacious and proficient with any tool when you know how to use shortcuts. Consider this for a moment. Do you think it is okay if someone repeatedly selects text by dragging through the mouse and selecting the cut option from the menu for once instead of pressing Ctrl+X? or by holding down the mouse button to copy the entire text instead of pressing Ctrl+A? There is no exception to this rule when it comes to Linux shortcuts.
Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. For analytic applications to properly leverage a hybrid, multi-cloud ecosystem to support modern data architectures, data observability has become even more important. I spoke to Mark Ramsey of Ramsey International (RI) to dive deeper into that last subject.
When setting up a modern data stack, data warehouse modeling is often the very first step. It is important to create an architecture that supports the data models that you wish to build. I often see people going straight to writing complex transformations before thinking about how they want to organize the databases, schemas, and tables within their warehouse. To succeed, it is key to design your data warehouse with your models in mind before starting the modeling process.
In the past, the process improvement aspect of business process management relied on lengthy and time-consuming research. Process managers held discussions with stakeholders, made guesstimates about how or why a process underperformed, and spent days, sometimes weeks testing enhancements to see if they could observe a difference. Now, businesses have access to a data-driven process optimization tool: process mining.
Last time in this series, we looked at improving the developer experience. As your Lambda function gains more features and dependencies, you may notice that the bundle size begins to grow exponentially. This can negatively affect deployments and cold starts. A bigger bundle means deployments take longer to upload to the AWS cloud. The JavaScript engine has more work to do before it can execute the Lambda function. In this take, we will focus on optimizing our Lambda function.
CDP Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with Cloudera Data Platform (CDP). For more information and to get started with COD, refer to Getting Started with Cloudera Data Platform Operational Database (COD).
Cloudera Operational Database enables developers to quickly build future-proof applications that are architected to handle data evolution. It helps developers automate and simplify database management with capabilities like auto-scale, and is fully integrated with Cloudera Data Platform (CDP). For more information and to get started with COD, refer to our article Getting Started with Cloudera Data Platform Operational Database (COD).
