Python is one of the most in-demand programming languages in the world — and for good reason. Knowing how to code has never been so valuable thanks to the expanding world of tech and focus on data science. From landing high-paying jobs to improving your skillset, learning Python scripting can bring you many opportunities to succeed. However, while these opportunities are robust, many challenges come with learning Python.

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.

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.

In the BigQuery Spotlight series, we talked about Monitoring. This post focuses on using Audit Logs for deep dive monitoring. BigQuery Audit Logs are a collection of logs provided by Google Cloud that provide insight into operations related to your use of BigQuery. A wealth of information is available to you in the Audit Logs. Cloud Logging captures events which can show “who” performed “what” activity and “how” the system behaved.

APIs are being created faster than ever before with ever-advancing technologies such as node.js and AngularJS. With the flexibility in design and integrations for APIs, there isn't a more exciting time than now to be an API developer. However, with so many new technologies and methods of creating APIs comes the question, "What makes a good API?" While the increase in API creation has many advantages for businesses in multiple areas, there is also more room for low-quality API production.