Hewlett Packard Enterprise (HPE) acquired MapR’s business assets including its intellectual property, artificial intelligence/machine learning (AI/ML), and analytics data management technology. The companies didn’t disclose terms of the deal.

MapR, a one-time big data unicorn, had been looking for a buyer or another funding source to avoid cutting 122 jobs and shuttering its Santa Clara, California headquarters. The deal gives it a needed lifeline, and it boosts HPE’s “intelligent edge” strategy as the infrastructure vendor works to infuse AI, ML, and intelligent data processing capabilities across its portfolio from edge to cloud.

It also advances HPE’s $4 billion edge commitment: CEO Antonio Neri a year ago pledged to invest $4 billion in edge technologies and services over the next four years to fund “intelligent edge” research and development around connectivity, security, edge computing, automation, AI, and ML.

“The explosion of data is creating a new era of intelligence where the winners will be the ones who harness the power of data, wherever it lives,” said Neri in a statement. “MapR’s file system technology enables HPE to offer a complete portfolio of products to drive artificial intelligence and analytics applications and strengthens our ability to help customers manage their data assets end to end, from edge to cloud.”

HPE will use MapR’s technology to bolster its Intelligent Data Platform and says MapR’s enterprise-grade file system and cloud-native storage services will complement its BlueData container platform. HPE closed on the BlueData acquisition earlier this year — that purchase also expanded HPE’s AI and big data offerings — and used that company’s technology as the basis for its container platform.

For example, the MapR Data Platform will extend BlueData capabilities for stateful container-based applications. This will enable data scientists and data analysts to stitch together AI/ML and analytics data pipelines across on-premises, hybrid cloud, and multi-cloud environments, HPE says.

MapR also provides a consistent way to secure and orchestrate data across the AI/ML and analytics data pipeline, which simplifies workload management and makes it easier to move applications across disparate environments running its Data Platform.