A Major MapR Data Platform Update; Solidifies Position as Industry's Leading Data Platform for AI & Analytics
On behalf of everyone who worked tirelessly on the MapR 6.1 release, especially every member of the engineering team, I'm delighted to announce the general availability of the MapR 6.1 Release.
The release is now live at https://package.mapr.com/releases/v6.1.0/
MEP 6.0 is now live at https://package.mapr.com/releases/MEP/MEP-6.0.0/
Documentation is now live at https://mapr.com/docs/61/
Six Reasons Why MapR Is the Industry's Best Data Platform for AI and Analytics:
MapR 6.1 Release enables customers to:
- Run analytics and AI on the same data. A large variety of AI tools (and possibly many future tools) will work without any changes on MapR. Unlike competitors that recommend the creation of mini-data pools/silos per data scientist or limit data science to Spark-only environments, the MapR approach provides a way for all data scientists and analysts to build a shared common understanding of the data - which leads to better outcomes and lower total cost of ownership.
- Accelerate their journey to AI, analytics, hybrid cloud, and containerization with the only Analytics and AI platform that brings together edge, on-premises, and cloud systems, leverages cloud object stores for cost-optimized data placement, and provides data and processing services where they need to be.
- Onboard all major datasets and use major AI and Analytics processing systems on MapR seamlessly because MapR now supports all three major analytic APIs.
- HDFS - for data-locality-based efficient analytics
- NFS/POSIX - for integrating into the enterprise data fabric and for ML/AI workloads
- S3-Compatible Object Store - for the new-generation applications and ML/AI workloads
- Use the latest and most compelling analytical toolkit available on the market, including new additions of Drill 1.14, Spark 2.3, Hive 2.3, Kafka 1.1 API, and associated Kafka ecosystem, including KSQL and KStreams.
- Choose from easy-to-use cost/performance options from performance-optimized, capacity-optimized, and cost-optimized tiers. Not only that, MapR takes care of complex data movement, auto-recall, scheduled-recall, expiry, and sync of data across these tiers. All customers have to do is specify their policy. The whole data lifecycle is securely managed and all the security access control policies are retained and applied, regardless of where the data resides - within MapR or outside MapR.
- Set up their clusters with security easily. The "Secure By Default" and Data Encryption at Rest feature sets make MapR arguably the only big data Analytics and AI platform that ships with security ON by default and takes care of all the complexities involved around securing a myriad of data processing and analytical services out-of-the-box. In addition, the recently announced MapR Data Catalog powered by Waterline Data enables customers to build an AI-driven, manual-assist data catalog across the enterprise for self-service, data discovery, and governance use cases.
Additional Resources to learn more:
See the product in action
This blog post was published October 03, 2018.