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.
How can your organization ensure data access and transparency? Measure yourself against this model, courtesy of our partners at Slalom.
To dive deeper into some of the findings of our recent fintech mobile report, we reached out to banking and finance professionals in a series of conversations. We explored region-specific challenges and how different institutions keep up with the industry’s increasingly mobile-first direction.
Waldo is a no-code testing platform, that allows teams to test apps without the hassle of scripting. With the Waldo Verified Step, you can automate testing and ship your app to the users faster, directly from Bitrise.
We software engineers like to think ourselves unflappable. Consider that we spend most of our days staring at glowing pages of eldritch horror that no mortal was meant to witness. We whisper and type our otherworldly incantations, all the while feeling the hungry gaze of a lurking cross-site scripting bug, or a shadowy use-after-free, or an accidental summoning of ZALGO. (H̨e̛ ̕c͢om͢es, you know.)
For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases.
Today, companies can measure every aspect of business health, except the health of their data which drives business decisions. Data is vital to inform critical decisions such as identifying new routes to market, systems to support business agility, and more resilient supply chains. As Harvard Business Review puts it, “Your organization’s data is the source of both the opportunity and the challenges to your innovation.
Data architecture is a hot topic right now. And rightfully so. Technological advances bring out a myriad of new solutions that go beyond the traditional relational databases and data warehouses. They enable companies to accelerate their entire data pipeline (or at least remove painful bottlenecks) and shorten the analytic cycles. The portfolio of data assets managed by companies is also growing.
