Dataguise Launches DgSecure Version 5.0 Jun 19, 2015
The platform provides multi-cluster support from a single integrated dashboard with integrations for Cloudera, Hortonworks, MapR, and Pivotal HD.
Dataguise announced the launch of DgSecure 5.0, which allows businesses to scale sensitive data discovery and automate data protection.
The new software version adds security access monitoring and entitlement functionality that allow administrators to define and audit sensitive data interactions.
The platform provides information about the status of sensitive data, with greater control over which users can view the information. It accomplishes this by combining sensitive data discovery with integrated Access Control List (ACL) permissions as defined in Apache Sentry, Apache Ranger or MapR’s ACLs.
“Automation is critical. Big data does not mean big hiring. Our customers are constantly challenged by the human scale problem behind big data, and their projects are growing in size, complexity, scale, but their manpower isn’t,” Jeremy Stieglitz, vice president of product management for Dataguise, told eWEEK. “Finding Hadoop expertise has and continues to be a real bottleneck to project success. When we bring and add security into the mix, we cannot be a people tax. Automation is a way to add security and compliance, without adding a huge operational overhead for how to manage and deploy security.”
The platform provides multi-cluster support from a single integrated dashboard with certified integrations for Cloudera, Hortonworks, MapR, and Pivotal HD. It also provides advanced encryption key management and integration with Safenet, RSA and Thales, featuring industry-standard Key Management Interface Protocol (KMIP) support.
“Our largest customers understand that there is a trade-off between easy and strong. In other words, easy security–the kind that you can turn on with one switch — tends to be the fastest to penetrate or breach,” Stieglitz said. “I’m not implying that we don’t work very hard to ensure that the operation of data discovery and protection is easy and optimized for scale, but there is policy, management, authorization and scalability work to make sure it is done correctly for customers that have trillion-row record files.”
Other enhancements include automated cell-level encryption (AES and FPE) capabilities for Flume, Sqoop, and HIVE API, and automation features through Oozie support to facilitate full job scheduling, automation of discovery and protection through security workflow scripting and command line interfaces.
“Two big changes are occurring in big data right now. At the platform level, things are moving from batch and moment-in-time to real-time and continuous streaming,” Stieglitz explained. “Spark, Kafka, Storm, NoSQL, Tez, Impala, Drill, Yarn, and others are enabling this in the technology of Hadoop. From a data governance standpoint, it shortens the time that everything exists, and changes where you have to think about applying security.”