MapR Clarity vs. Cloudera Unity

MapR Clarity Provides a Clear Path for Cloudera and Hortonworks Customers to AI, Hybrid Cloud, Containers, and Operational Analytics

Download the PDF

Executive Summary

The announcement of Cloudera acquiring Hortonworks has created uncertainty and confusion for their customers due to significant overlap between the two companies’ offerings. Redundant products will need to be “rationalized” or eliminated, causing disruption for their customers. The merger is focused on cost efficiencies in the sales force and support model. It creates a negative technology situation, forcing customers to shift, move, or make choices.

While Cloudera/Hortonworks announced they will ship the “Unity Release” eventually, every customer will be shown the path to move to a common platform because the merged company will be unable to sustain two platforms.

MapR identified a minimum of six areas of overlap that need to be rationalized:

  • Data Science: Data Science Workbench vs. IBM Data Science Experience
  • SQL: Impala vs. Hive LLAP, Hive on Spark vs. Hive on Tez
  • Security: Sentry vs. Ranger
  • Management: Manager vs. Ambari
  • Governance: Navigator vs. Atlas
  • Cloud: SDX vs. DataPlane

In addition, the two companies have no clear offerings in four areas critical to customers today:

  1. AI/ML — Neither Cloudera nor Hortonworks have a good AI/ML story because their platforms do not support POSIX and AI/ML libraries like MapR does. The MapR Data Platform was built in anticipation of the evolutions of workloads from Hadoop to Spark to AI/ML. Unlike the competition, MapR supports these workloads on a single platform and on one cluster in production.

  2. Hybrid and Multi-Cloud — Neither Cloudera nor Hortonworks support seamless environments that span on-premises deployments to one or more clouds. MapR today supports hybrid and multi-cloud environments with open APIs, letting you avoid cloud lock-in and continue to run legacy applications as is. Additionally, MapR helps you lower cloud costs by up to 30%.

  3. Containers — Neither Cloudera nor Hortonworks support containerized stateful applications. MapR is a leader and early adopter of both Docker and Kubernetes, and has made containerized stateful applications a reality. With the MapR Data Platform, data from containerized applications is persistent, scales as containers grow, and is protected with replication, mirroring, and instant snapshots.

  4. Operational Analytics — Neither Cloudera nor Hortonworks support operational analytics. MapR lets you apply analytics in real time to operational applications. Only MapR supports mission-critical business applications under production SLAs, all without compromising on data consistency.

MapR Provides Clarity Today

While Cloudera and Hortonworks continue to “rationalize,” MapR will be using this time to further innovate and expand our offerings. MapR is dedicated to delivering the capabilities customers want now for greater agility through containerization, seamless hybrid and multi-cloud, and a built-in path to AI/ML from an analytics environment.

Additionally, MapR delivered a number of enterprise-grade capabilities in 2011, including: high-availability, disaster recovery, NFS, and a single security model. Cloudera and Hortonworks have yet to deliver on these capabilities. Given Cloudera’s and Hortonworks’ underlying architecture, they are unlikely to get there anytime soon.

MapR Clarity Merger Mess
Data Science Open APIs to enable ML and AI on the same cluster. Redundant Offerings:
Data Science Workbench vs. IBM DSX
Shaky Foundation:
Siloed clusters, data movement required. No support for Python ML.
SQL Most open platform for SQL - Hive on MR, Hive on Tez, Spark, Drill, Impala. End-to-end JSON flexibility/schema-less queries. Redundant Offerings:
Impala vs. Hive, Hive on Spark vs. Hive on Tez
Shaky Foundation:
No support for data exploration and operational analytics
Security Unified, common security model at platform level. Ubiquitous data protection, expressive and granular row/column access controls, and built-in auditing. Redundant Offerings:
Sentry vs. Ranger
Shaky Foundation:
Add-on tools for security means inconsistent security models and access is not addressed at platform level.
Management Unified tooling for all data. Advanced management features with multi-tenancy, quotas, data placement, snapshots, and mirroring. Built-in High Availability. Redundant Offerings:
Manager vs. Ambari
Shaky Foundation:
Hard to manage all data, even with many tools
Governance Enterprise-wide data catalog Redundant Offerings:
Navigator vs. Atlas
Shaky Foundation:
Incomplete governance. Downstream dependencies on disparate security tools.
Hybrid and Multi-Cloud Built for hybrid/multi-cloud. Transparent data synchronization across on-premises and clouds, global namespace, built-in streaming, and storage optimizations to balance performance, capacity, and cost. Redundant Offerings:
SDX vs. DataPlane
Shaky Foundation:
Cannot support multi-cloud natively, no global namespace. Concerns with cloud data management and consistency.
Datasheet PDF

MapR Clarity vs. Cloudera Unity