Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds.
This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.
Since 2013 the UK Government’s flagship ‘Cloud First’ policy has been at the forefront of enabling departments to shed their legacy IT architecture in order to meaningfully embrace digital transformation. The policy outlines that the cloud (and specifically, public cloud) be the default position for any new services; unless it can be demonstrated that other alternatives offer better value for money.
As organizations look to optimize the speed and cost of their cloud journey in today’s rapidly evolving economy, Cloudera is delighted to announce the availability of Cloudera Data Platform (CDP) Public Cloud in AWS Marketplace. Now customers can easily, confidently and cost-effectively discover, procure and deploy the world’s first Enterprise Data Cloud, powered by AWS, for faster time-to-insight from their advanced analytics and machine learning services.
Cloudera Data Platform 7.2.1 introduces fine-grained authorization for access to Azure Data Lake Storage using Apache Ranger policies. Cloudera and Microsoft have been working together closely on this integration, which greatly simplifies the security administration of access to ADLS-Gen2 cloud storage.
Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Data architects deal with constantly evolving workloads and business analysts must balance the urgency and importance of a concurrent user population that continues to grow.
More and more companies are beginning to turn to Development and the cloud as a way to improve their software teams. Whilst it used to be that development and operations were seen as separate, that view has now changed. Linking the two leads to better communication, faster development times, and the ability to stay on top of things.
In our last two posts, we talked with Deloitte’s Marc Beierschoder and Martin Mannion respectively about the requirement organizations have to deploy their data and analytics, quickly, into a hybrid environment. On top of that, there is the fundamental aspect of consistent security and governance of your enterprise data cloud and need for multiple users with different requirements to access data flexibly.
