Introduction We are going to talk about data in this blog post, predominately test data required for performance testing. This is something that makes the life of a performance tester extremely difficult and awkward as because of the huge quantities required, in the right state, that match the criteria required for your test to run. We often have to approach the use of large quantities data for the purpose of performance testing in a number of ways.
When a data breach occurs involving a cloud service, the impulsive reaction is to denounce using the cloud (at least for sensitive information). Since cloud security is not widely understood, it may be difficult to delineate it in the context of more general information security.
These days, companies are overflowing in data and tolerating ancient API-integrations. Across all industries. Buried in that data are trends that point to customer insights, inefficiencies and synergy opportunities that could provide a competitive edge to a company. Or, just help a company catch back up to its competitive peers. But how to best harness this data?
These days, companies are overflowing in data and tolerating ancient API-integrations. Across all industries. Buried in that data are trends that point to customer insights, inefficiencies and synergy opportunities that could provide a competitive edge to a company. Or, just help a company catch back up to its competitive peers. But how to best harness this data?
Sometime ago, when I was with another software company, I was involved in a large implementation of our software. The deployment required our software to connect with a legacy customer application, so an integration between the two products had to be developed. When the Sales person put together the proposal, he quoted this integration for $0 because “it’s so simple, I could create it myself”.
As APIs become the de facto standard for building and connecting business-critical applications, it’s important for operations teams to gain visibility into the security attributes of your APIs in order to continuously monitor and maintain the health of your API programs.
Machine learning is a branch of Artificial Intelligence that makes it possible for systems to learn without being programmed. Applying this technology to real world Marketing is often misunderstood and is within your reach.
Melbourne, 06 August 2019 - Yellowfin, a world-leading and innovative analytics vendor, has been named Leader by Enterprise Management Associates (EMA) in the research report ‘Innovation in the Use of Artificial Intelligence and Machine Learning for Business Intelligence’. EMA selected Yellowfin as an innovation leader as the analytics suite featured eight of the ten AI-enabled capabilities evaluated in the report and best aligned with today’s customer priorities and pain points.
Katalon Studio 6.3 is now available to download. Take a sneak peek of some new and exciting features! Download Katalon Studio 6.3 First, one of the most requested features by users: namely the dark theme, is now available. The decision to proceed with this new feature was due to the number of upvotes for this mode of display that triumphed all other proposed features (including applying machine learning to make coffee – seriously, no joke).
In the current microservices DevOps environment, there are tough new and evolving challenges for developers and teams to consider on top of the more traditional ones. From worsening versions of already common threats to new-generation evolving threats, new perspectives are required on securing microservices. These new perspectives may not be intuitive for many otherwise sophisticated DevOps and data teams.
