Readings
4 posts
The Twelve-Factor App, a Methodology for Building Web Apps
Suggested by the designers of the Heroku PaaS platform, the twelve-factor methodology can be applied to apps written in any programming language, and which use any combination of backing services (database, queue, memory cache, etc). It is aimed at building Software-as-a-Service apps that:
- Use declarative formats for setup automation, to minimize time and cost for new developers joining the project;
- Have a clean contract with the underlying operating system, offering maximum portability between execution environments;
- Are suitable for deployment on modern cloud platforms, obviating the need for servers and systems administration;
- Minimize divergence between development and production, enabling continuous deployment for maximum agility;
- And can scale up without significant changes to tooling, architecture, or development practices.
More about the Twelve-Factor App
A New Model-Based Approach for API Testing
Keeping Pace with Agile Development, Visualizing Complex Dependencies, and Orchestrating for Completeness of Testing are three good reasons to select a Model-Based approach for API testing, according to Collin Chau, a DevOps test expert.
"With the proliferation and complexity in microservices development that the Internet of Things brings, development teams are struggling to embrace API testing for more effective QA testing in-sprint. Learn how a model-based testing approach makes the difference in your API tests."
Read Collin Chau full article in Continuous Testing
NLP Search Paves the Way for Augmented Data Discovery
Combining natural language understanding and natural language generation will result in dynamic, bi-directional human-machine communication that will take several forms: text, voice and images. In text and voice scenarios, the BI or analytics solution can converse with the user to render the desired result - regardless of data-related and query-related search complexity.
Data visualizations also will become more interactive, if not immersive, along the lines of Busby from Oblong Industries. This product focuses on immersive interfaces, not specifically BI or analytics. However, its concepts could have a ripple effect on how people interact with data and thus, augmented data discovery.
"I think the future of BI is no BI. Don't ask me to search and look for things anymore. Give me that piece of information when I need it and if I need it. Come to me when there's something I need to know", foresees Erick Brethenoux, senior director analyst at Gartner.
For more information, read Lisa Morgan TechTarget article entitled NLP makes augmented data discovery a reality in analytics
Is BERT a Game Changer in NLP?
BERT (Bidirectional Encoder Representations from Transformers) is an open-sourced NLP pre-training model developed by researchers at Google in 2018. It has inspired multiple NLP architectures, training approaches and language models, including Google’s TransformerXL, OpenAI’s GPT-2, ERNIE2.0, XLNet, and RoBERTa.
For instance, BERT is now used by Google Search to provide more relevant results. And it can also be used in smarter chatbots with conversational AI applications, expects Bharat S Raj.
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