The Clarity Program consists of MapR Data Platform updates that include capabilities and new features that address:
·Broad Support for AI – MapR supports diverse data management and logistics capabilities with standard interfaces to directly support tools such as TensorFlow, Caffe, and PyTorch. MapR Clarity also includes the recently announced support for RAPIDS open-source software from NVIDIA and the ability to inject data flows directly into GPU-driven data science projects. New features for the MapR Data Science Refinery include support for the Helium repository browser and ability to share Zeppelin visualization packages across multiple containers. The Clarity Program provides these AI related capabilities today which clears up confusion caused by incomplete competing solutions.
·Synchronized Cloud and Multi-Cloud – MapR underscores cross-cloud synchronization and data replication with new S3-compatible API support. Not only can on-premises file-based applications directly use object storage across clouds, MapR can coordinate data access and flows with existing Hadoop installations forestalling the need for wholesale replacement. Data synchronization across locations, whether on-premises, cloud or at the edge, remains a challenge for existing alternatives as well as merged solutions.
·Stateful Containers – MapR includes support for frictionless data access for containerized stateful applications, direct data access with native Kubernetes volume drivers and the ability to use containers and Kubernetes directly with NVIDIA containers. Supporting stateful applications in containers remains a challenge for alternative solutions today.
·Integrated IoT – MapR extends to the edge with uniform management, security and data protection which includes integrated support for streams. There is no separate streaming cluster to manage or secure with MapR. New capabilities include support for Kafka KSQL that provides support for interactive and non-interactive SQL and allows launching stream applications with no need to write code in Java or Python.
·Uniform Data Governance – The MapR Data Platform extends from on-premises to cloud to edge with a common management, data security and data protection model. Data security and management is not based on data type or access method, it is a unified part of the platform and can extend across locations and across processing types without introducing security gaps or duplicate management tasks. Competing solutions fall short by tying data security to the access methods.
“Every day we ingest a very large volume of machine generated data, so having a fast, reliable data analytics platform is critical to our business," said Charles Wheelus, principal data scientist at Cequint. “We made the switch to the MapR Data Platform from Hortonworks and our data ingestion now runs flawlessly. Before switching to MapR, our data ingestion often required manual intervention, resulting in a loss of productivity. Stepping up to MapR has drastically reduced administration time, freeing us to focus on delivering value to our partners and customers.”
“The announced merger of Cloudera and Hortonworks has put a spotlight on the fact that the shared underlying data platform is the same and there are 10 or more overlapping projects that need to be consolidated,” said Jack Norris, senior vice president, data and applications, MapR. “Customers are running on a platform that is bound to have disruptive changes ahead with this ‘merger mess’, including halted innovation and uncertain migrations. MapR is focused on helping organizations best leverage their current investments and quickly meet their continuing business needs and outcomes with a platform that is already years ahead.”
The Clarity Program also includes a free StepUp assessment service to leverage MapR Professional Services’ expertise and offers a comprehensive program to understand the current customer’s data architecture, workflows, applications and uses cases. The Professional Services team will engage with customers to provide:
·High-level architectural approach, performance planning and recommendations
·Feature mapping to address defined gaps and needs·Step-by-step implementation plan for on-premise, cloud, edge execution