Systems | Development | Analytics | API | Testing

We’re taking a closer look Container-Based Systems. The rise of the microservices-based applications has allowed global enterprises – like Amazon.com, Netflix, Uber, and Airbnb – to achieve unprecedented market dominance. Central to making these microservices-based applications possible is the concept of containerization, and at the core of containerization are Docker and Kubernetes – the two most widespread solutions for building and managing container-based applications.

The Insurance industry is in uncharted waters and COVID-19 has taken us where no algorithm has gone before. Today’s models, norms, and averages are being re-written on the fly, with insurers forced to cope with the inevitable conflict between old standards and the new normal.

The only certainty in today’s world is change. And nowhere is that more apparent than in the way organizations consume data. A typical company might have thousands of analysts and business users accessing dashboards daily, hundreds of data scientists building and training models, and a large team of data engineers designing and running data pipelines. Each of these workloads has distinct compute and storage needs, and those needs can change significantly from hour to hour and day to day.

It’s time, again, to look at a chart that you might not be using, but that you definitively should consider using when doing data visualization. The waterfall chart is great at visualizing the cumulative effect from positive and negative changes, as, for example, you would see in a Profit and Loss (P&L) report.

On average, data-driven companies grow more than 30% every year. Because of the competitive advantage that data confers to incumbents who are capable of extracting value from it, it has been called the new oil. Companies are tapping into this well of resources because of the advantages that it has to offer: But using data to run your operations poses its own set of challenges.

Every company is a digital company. Whether you are a traditional e-commerce company who has been selling goods online for years, a healthcare company who recently needed to accelerate online visits for telehealth, or a company whose business model has drastically shifted due to Covid, the need for digital transformation is here.

Application programming interfaces, or APIs, are how software talks to other software. They abstract the complexity of underlying systems so the systems can connect in novel ways even if they were never intended to interoperate. Consequently, APIs are key ingredients in both most modern digital experiences and the execution of many of today’s most exciting business opportunities.

Thanks to Linus Torvalds, the creator of not only Linux, but also the most popular version control system - Git, multiple developers can simultaneously work on the development of the same application, ramping up the speed of production. Git was revolutionary - it enabled developers to keep track of code changes and collaborate seamlessly across different projects.

Today we are introducing a new series of blog posts that will take a look at recent enhancements to Apache Impala. Many of these are performance improvements, such as the feature described below which will give anywhere from a 2x to 7x performance improvement by taking better advantage of all the CPU cores. In addition, a lot of work has also been put into ensuring that Impala runs optimally in decoupled compute scenarios, where the data lives in object storage or remote HDFS.

In the first blog in this series, I talked about test speed, and why it shouldn’t be your first priority. Rather, you should focus on getting accurate test results back consistently. This week, I’ll discuss the power of parallelization in testing.