The third wave of test automation brings about more concepts in software delivery – one of which is ‘continuous testing.’ However, mastering continuous testing is difficult; choosing the right continuous testing tool is even more overwhelming.
It’s no secret that, despite the huge movement toward sustainable living, we just can’t live without some things being instant. One such crutch for students worldwide and most of Asia’s young adult population (also the entire American prison system) are the ubiquitous instant noodles. 100 billion servings are eaten every year. It’s convenience in a cup.
By the September deadline, Europe’s banks and fintech companies should be compliant with the Second Payment Services Directive (PSD2). Rather than focusing inwards on business operations, PSD2 is an outward-looking directive. It encourages open access and competition in the banking industry. Organisations across the breadth of the industry are required to open their payments infrastructures and customer data to third parties.
We’ve entered the era of the information economy, where data has become the most critical asset of every organization. To support business objectives such as revenue growth, profitability, and customer satisfaction, organizations are increasingly reliant on data to make decisions and drive their operations.
Recently I was in the US and people were just blown away when I told them about the success of our business in Japan. Our Japanese business grew nearly 40% last year and this year is shaping up to be great as well, but it’s not something that has happened overnight. The reality is that it’s tough to do business in Japan and it’s taken us over four years to get it right.
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?
First of all, sorry about the delay in getting the second part of this blog written up. Things are moving very fast at Talend at the moment, which is great, but it's led to me having to delay in getting this second part out. I've been spending quite a bit of time working with the Pipeline Designer Summer 2019 release. Some really exciting additions are coming to Pipeline Designer, but we will look into those at a later date.
