The customer is one of the largest private-sector banks in India that has been leading the way in innovation and customer service. With a network of over 5,500 branches and 15,000 ATMs across the country, this bank offers a range of financial products and services to its customers, including personal banking, corporate banking, wealth management, insurance, and investment banking.
We are announcing BigQuery ML inference engine, which allows you to run predictions on a broad range of models hosted across multiple locations.
New BigQuery pricing editions and autoscaling let you choose the right price-performance for your workloads, and pay for only what you use.
BigQuery data clean rooms can help organizations create and manage secure environments for privacy-centric data sharing, analysis, and collaboration.
Customizing exceptions is usually not a common concern during software development. But if you catch an error in an observability tool and can't correctly and quickly identify the problem, you may need more information and details about an exception. This article will show you how to customize exceptions in Ruby and mitigate potential future problems due to a lack of error information. Let's dive straight in!
With digital technologies transforming industries, companies adopt Agile and other practices to gain a competitive advantage. DevOps is one such method that has changed software development as we know it. The new-age approach helps accelerate productivity and also enables better collaboration between the development and operation teams. But the biggest benefit of adopting DevOps is faster time-to-market. It has helped around 83% of organizations unlock business value and deliver customer value faster.
In this blog post, we will run JMeter tests from code. First we will look at how we can create an IntelliJ project in Java to build a JMeter performance test. Once you understand how JMeter tests in code can be written you can start to build tests in your development code base to compliment unit tests or functional tests written by developers during the creation of application and services.
I’m a firm believer that banks rarely invest to drive revenue and elevate customer service. But I recently hosted a roundtable discussion in London with Appian, a process automation platform company, where leaders from mainstream and challenger banks in the UK gathered to discuss the challenges facing financial institutions when adopting new technology.
Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?
Traditionally, manual test management and automated testing generate independent quality reporting data that has to be either manually integrated in spreadsheets or programmatically via scripts and reporting tools. This process takes valuable QA time away from actual testing and can also create inconsistency and inaccuracy when reporting on release readiness due to both the lag time in combining data from both sources and general human error.
