Notes about Cutting-Edge Technologies and Everything

This month’s release delivers very useful features like Apache Arrow Flight with Python, full support for CDP 7.1, security enhancements for Oracle connections, a new support bundle and much more. This blog post highlights the following updates: Arrow Flight clients Query support bundle Kerberos support for Dremio-Oracle connections User/job metrics available in the UI Continue reading >>>

Today we are excited to announce the release of Dremio 4.8! This month’s release delivers multiple features such as external query, a new authorization service API, AWS Edition enhancements and more. This blog post highlights the following updates: External query Default reflections Runtime filtering GA Documented JMX metrics and provided sample exporters Ability to customize projects in Dremio AWS Edition Support for Dremio AWS Edition deployments without public IP addresses Read full article >>>

Datanami  is pleased to announce the results of its fourth annual Readers’ and Editors’ Choice Awards, which recognizes the companies, products, and projects that have made a difference in the big data community this year. These awards, which are nominated and voted on by Datanami readers, give us insight into the state of the community. We’d like to thank our dedicated readers for weighing in on their top picks for the best in big data. It’s been a privilege for us to present these awards, and we extend our congratulations to this year’s winners. Best Big Data Product or Technology: Machine Learning Readers’ Choice: Elastic Editor’s Choice: SAS Visual Data Mining & Machine Learning Best Big Data Product or Technology: Internet of Things Readers’ Choice: SAS Analytics for IoT Editor’s Choice:  The Striim Platform Best Big Data Product or Technology: Big Data Security Readers’ Choice: Cloudera Enterprise Editor’s Choice: Elastic Stack Best Big ...

Dremio’s Data Lake Engine delivers lightning fast query speed and a self-service semantic layer operating directly against your data lake storage. No moving data to proprietary data warehouses or creating cubes, aggregation tables and BI extracts. Just flexibility and control for Data Architects, and self-service for Data Consumers. This release, also known as Dremio 4.0, dramatically accelerates query performance on S3 and ADLS, and provides deeper integration with the security services of AWS and Azure. In addition, this release simplifies the ability to query data across a broader range of data sources, including multiple lakes (with different Hive versions) and through community-developed connectors offered in Dremio Hub. Read full article >>>

Here’s what’s NEW: Up to 100x performance improvement for a wide range of query workloads, using Apache Arrow’s new kernel – Gandiva. Gandiva performs just-in-time compilation of SQL queries to machine code to get the fastest possible performance. (Our blog post explains more about how.) Support for Teradata, Azure Data Lake Store, AWS S3 GovCloud, and the latest version of Elasticsearch. Expect more soon! We’ve got a new connector framework that improves performance, stability, and development velocity for all data sources. Cluster Workload Manager, which lets you deploy diverse workloads on a single operational cluster while ensuring critical SLAs for performance and availability. More data catalog features, including wikis and tags for your data sets. That makes it even easier to discover, organize, curate, and share datasets from all your data sources.  Improved security and governance controls, like end-to-end encryption over TLS, and integration with Apache Ranger, ...

This is a major release that includes many new features, performance improvements, and hundreds of stability enhancements - see the highlights and more details below. • Elasticsearch 6.  Dremio now supports the latest versions of Elasticsearch. Enjoy full SQL support, including JOINs, Window functions, and accelerated analytics through any BI tool, including Tableau and Power BI. We also added support for compressing Elasticsearch responses to minimize network traffic.  • Approximate count distinct acceleration.  Dremio now supports accelerating count distinct queries based on an approximation-based algorithm (HyperLogLog). This provides a faster and more memory efficient way of providing distinct counts and is especially useful in high cardinality scenarios with very large datasets.  • Faster ORC performance.  Data encoded in ORC is now significantly faster to access and more memory efficient for ORC managed in Hive sources.  • Support for AWS GovClou...