Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a more powerful user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows. Leveraging Hue as the SQL development workbench, we continue to see usage of the platform increase and the number of analytic use cases grow –
A few weeks back, we announced the upcoming beta of Cloudera Altus Analytic DB for cloud-based data warehousing. As promised, the beta is now available and we wanted to spend some time describing the unique architecture.
Architecture of Cloudera Altus Analytic DB
Altus Analytic DB is built on the Cloudera Altus platform-as-a-service foundation, which also supports the Altus Data Engineering service. The architecture of Cloudera Altus is based around a few simple but important premises —
What is SDX?
Shared Data Experience — SDX — is Cloudera’s secret ingredient that makes it possible to deploy Cloudera’s four core functions (Data Engineering, Data Science, Analytic DB, Operational DB) on a single platform.
Why does that matter?
First, each of those core functions is essential to any modern enterprise business.
- Data Engineering enables the business to run batch or stream processes that speed ETL and train machine learning models
- Data Science enables the business to do exploratory data science at big data scale with full data security and governance
- Analytic DB delivers the fastest time-to-insight with the flexibility and agility to run in any environment and against any type of data.
Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.
As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack.