Customers and prospects of Cloudera and Hortonworks are confused – and rightly so. It is unclear which of the numerous redundant projects will stay and which will go once the two companies merge. Offerings will be “rationalized” over time, as Cloudera promises a Unity release sometime in the months and years ahead. Regardless, neither company has any single-platform, production-ready offering in the areas that matter most to organizations today: AI/ML, hybrid cloud, containers, operational analytics, and IoT.
Fortunately, there is no need to wait. MapR provides Clarity today. MapR supports AI/ML and analytics workloads on a single platform. On one cluster. In production. MapR supports hybrid and multi-cloud environments with open APIs, which allows you to avoid cloud lock-in and continue to run legacy applications as is. MapR is a leader and early adopter of both Docker and Kubernetes, and has made containerized stateful applications a reality. 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 experts help you identify your desired business outcomes and use cases, support and performance requirements, and production SLAs. You are provided with a step-by-step implementation plan to migrate from Cloudera or Hortonworks to MapR, if applicable.
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. 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.
Charles Wheelus, Principal Data Scientist at Cequint
MapR Clarity: MapR supports running data science workloads in the same cluster as traditional analytics. This means no AI/ML silos. Users have access to all data in place from any compute profile thanks to open APIs like POSIX and container volume plugins.
Merger Dilemma: Once Cloudera and Hortonworks merge, will you have to move your data to Data Science Workbench or IBM Data Science Experience (via Hortonworks partnership)? Why would you develop against a platform with an uncertain future for data science and AI?