In the realm of big data analytics, Hive has been a trusted companion for summarizing, querying, and analyzing huge and disparate datasets. But let’s face it, navigating the world of any SQL engine is a daunting task, and Hive is no exception. As a Hive user, you will find yourself wanting to go beyond surface-level analysis, and deep dive into the intricacies of how a Hive query is executed.
In this post, we'll configure a Phoenix LiveView application to use a structured logger. We'll then use AppSignal to correlate log events with other telemetry signals, like exception reports and traces. Along the way, you'll learn about the benefits of structured logging, and you'll see how to configure a distinct framework and application logger in your Phoenix app. Let's get started!
Robotic process automation (RPA) refers to the use of software robots, or "bots," that mimic human actions to automate repetitive tasks within business processes. RPA technology can interact with applications, systems, and data sources to automate procurement tasks such as data entry, document processing, and report generation.
Bank turmoil associated with recent bank failures has highlighted the importance of due diligence for managing financial risk, growing revenue, and retaining jittery customers. Regulators will likely ramp up their scrutiny–even after hitting financial institutions with a jaw-dropping $2.7 billion in fines in 2021 for failing to meet compliance and due diligence standards.
"Many hands make light work" applies well to the concept of parallel processing. It means that a task becomes easier and quicker when many people work together towards a common goal. In the context of computing, parallel processing refers to the ability to divide a task into smaller parts and distribute them across multiple processors or cores to complete the task more efficiently. Parallel processing involves breaking down a complex task into simpler ones, which can be executed in parallel.
Ensuring the reliability, functionality, and overall quality of software applications has become increasingly crucial. Quality assurance plays a vital role in achieving these objectives by implementing systematic processes and techniques to evaluate and enhance software quality. As technology continues to advance at a rapid pace, new and innovative approaches are emerging to tackle the challenges of software quality.
At ThoughtSpot, we believe making data accessible to every knowledge worker requires human-centered technology—an analytics experience that bridges the “language” barrier between technology and people. AI is the perfect compliment to search because it empowers organizations to analyze, understand, and act on data.
