Five years ago, Cloudera shared with the world our plan to transfer the lessons from decades of relational database research to the Apache Hadoop platform via a new SQL engine — Apache Impala — the first and fastest open source MPP SQL engine for Hadoop. Impala enabled SQL users to operate on vast amounts of data in open formats, stored on HDFS originally (with Apache Kudu, Amazon S3, and Microsoft ADLS now also native storage options),
We at Cloudera believe that all companies should have the power to leverage data for financial gain, to lower operational costs, and to avoid risk. We enable this by providing an enterprise grade platform that allows customers to easily manage, store, process, and analyze all of your data, regardless of volume and variety.
Cloudera’s Enterprise Data Hub (EDH), a modern machine learning and analytics platform that is optimized for the cloud,
Cloudera Data Science Workbench (CDSW) provides data science teams with a self-service platform for quickly developing machine learning workloads in their preferred language, with secure access to enterprise data and simple provisioning of compute. Individuals can request schedulable resources (e.g. compute, memory, GPUs) on a shared cluster that is managed centrally.
While self-service provisioning of resources is critical to the rapid interaction cycle of data scientists, it can pose a challenge to administrators.
A quick conversation with most Chief Information Security Officers (CISOs) reveals they understand they need to modernize their security architecture and the correct answer is to adopt a machine learning and analytics platform as a fundamental and durable part of their data strategy. However, many CISOs fear deployment of an initial use case will be somewhat daunting. Cloudera has partnered along with Arcadia Data and StreamSets to make it easier than ever for CISOs to take the first step and deploy basic use cases leveraging data sources common to many environments.
With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.