Systems | Development | Analytics | API | Testing

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.

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.

Apache Impala and Apache Kudu make a great combination for real-time analytics on streaming data for time series and real-time data warehousing use cases. More than 200 Cloudera customers have implemented Apache Kudu with Apache Spark for ingestion and Apache Impala for real-time BI use cases successfully over the last decade, with thousands of nodes running Apache Kudu.

The recent failures of regional banks in the US, such as Silicon Valley Bank (SVB), Silvergate, Signature, and First Republic, were caused by multiple factors. To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure.