Systems | Development | Analytics | API | Testing

Great data visualizations have the power to persuade decision makers to take immediate, appropriate action. When done well, data visualizations help users intuitively grasp data at a glance and provide more meaningful views of information in context. Good data visuals give busy workers a high-level summary of important data. They also offer a big-picture perspective and highlight trends, anomalies, and outliers while giving users the option to drill down into details and ask new questions when needed.

Throughout the One Big Cluster Stuck series we’ve explored impactful best practices to gain control of your Cloudera Data platform (CDP) environment and significantly improve its health and performance. We’ve shared code, dashboards, and tools to help you on your health improvement journey. We’d like to provide one last tool.

With updates to our installer user interface (UI), advanced functions, filters and more, Yellowfin 9.9 is a must-have update to streamline and improve your analytics experience. The latest release brings a fresh look for the Yellowfin installer to make it easier to install and upgrade Yellowfin, along with additional enhancements added for existing parts of the suite, including new Custom Advanced Functions, predefined filters and Bookmarks.

Understand how unenforced Key Constraints can benefit queries in BigQuery.

For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. But as the data volumes, data variety, and data usage grows, users face many challenges when using Hive tables because of its antiquated directory-based table format. Some of the common issues include constrained schema evolution, static partitioning of data, and long planning time because of S3 directory listings.

Iceberg is an emerging open-table format designed for large analytic workloads. The Apache Iceberg project continues developing an implementation of Iceberg specification in the form of Java Library. Several compute engines such as Impala, Hive, Spark, and Trino have supported querying data in Iceberg table format by adopting this Java Library provided by the Apache Iceberg project.