Systems | Development | Analytics | API | Testing

Databox’s Benchmarks feature allows you to see how your company compares to others like yours and helps you set better strategy and business goals.

For any developer, console.log() is one of the most well-known javascript functions since it allows us to quickly check for errors in our code in some circumstances. If you're unfamiliar with it, it's a tool that Javascript developers use to debug their code. Almost all popular browsers include a console, which comes in helpful for debugging Javascript. To access your browser's console, you use the console object of Javascript.

Since the turn of the century, websites have progressed dramatically. What used to be a network of basic text-based pages has developed into a network of carefully created experiences, complete with responsive buttons, parallax scrolling, and tailored information. These website design aspects don't appear out of anywhere, of course.

Testing and Quality Assurance can be endless tasks. That’s why testing teams need metrics to measure and quantify their work and success. Testing metrics provide tangible ways to measure the progress of testing, as well as the readiness to deploy a product. One of the most common and useful metrics is code coverage. Many testers consider it a good practice to write test cases that provide maximum code coverage and verify the expected and wanted behavior of the software.

27 experts share the top data quality issues that they’ve struggled with and how to solve them for better data reporting.

Change data capture (CDC) is a crucial, but also tremendously underappreciated, feature that forms the backbone of modern ETL workloads. Without knowing which data has changed since you last accessed it, you’d be forced to extract all the data from a source table or database each time that you perform data integration—which would be a tremendously inefficient process.

Since the release of Cloudera Data Engineering (CDE) more than a year ago, our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines.

“Water, water, everywhere, nor any drop to drink.” The famous line from Samuel Taylor Coleridge’s epic poem “The Rime of the Ancient Mariner” has a fitting application to today’s data problem. Enterprises are deluged with data, but they often have no way to leverage it. According to most experts, only a small percentage of data is usable and made useful, and most of it is in the dark — thus the term, “dark data.”

Out with the old; in with the new! If you haven’t already checked out the new Snowflake® interface (aka Snowsight®), make it your New Year’s resolution. Set yourself up for success in 2022 by spending a few minutes getting to know the new features and experiences that are in public preview—available when you click the Snowsight button at the top of your console’s menu bar.

With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.