Hortonworks, which offers enterprise Apache Hadoop solutions, added new enterprise features including containerization for faster and easier deployment of applications, as well as new collaborations with Microsoft Azure and Google Cloud.
The enhancements in the latest Hortonworks Data Platform (HDP) 3.0, which is based on Apache Hadoop 3.1, include:
- Agile application deployment via containerization, which enables apps to be launched quickly, allowing users to save time and resources.
- Support for deep learning applications, allowing customers to run workloads such as machine learning and deep learning that require substantial – and expensive – GPU resources. This feature leverages pooling and isolation which enables data scientists to democratize and share GPU access.
- Real-time database, delivering improved query optimization to process more data at a faster rate by unifying the performance gap between low-latency and high-throughput workloads. Enabled via Apache Hive 3.0, HDP 3.0 offers the only unified SQL solution that can perform interactive query at scale – regardless of whether the data lives on-premises or in the cloud.
- Enhanced security and governance, promoting greater regulatory compliance, including GDPR, through full chain of custody of data as well as fine-grained auditing of events. These new features offer the unique ability to track the lineage of data from its origin to the data lake. It also enables auditors to view data without making changes, have time-based policies and audit events around third parties with encryption protection.
Hortonworks is also expanding its existing partnership with Google Cloud to deliver next-gen big data analytics for hybrid cloud deployments. Specifically, HDP now integrates with Google Cloud Storage, which offers consistent cloud storage for running big data workloads. On-demand analytics workloads can be spun up in minutes with no up-front cost and unlimited elastic scale. It also enables Apache Hive and Apache Spark can be leveraged for interactive query, machine learning and data analytics.
