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?
MapR Clarity: MapR supports traditional analytics use cases as well as schema-less data exploration on all types of data via Apache Drill. MapR is an open platform for SQL, supporting Hive on MR, Hive on Tez, SparkSQL, Drill, and more.
Merger Dilemma: Once Cloudera and Hortonworks merge, will they support Impala or Hive LLAP? Hive on Tez or Hive on Spark? Why would you develop against a platform with an uncertain future for SQL Analytics?
MapR Clarity: MapR is secure by default and supports platform-level security. No need for add-on tools that might be circumvented. MapR has built-in auditing, expressive authorization, and flexible authentication supporting any username/password registry and Kerberos.
Merger Dilemma: Once Cloudera and Hortonworks merge, will they support Sentry or Ranger? Why would you develop against a platform with uncertainty about how security will be addressed?
MapR Clarity: MapR offers a unified, actionable, and intuitive way to manage all data via MapR Control System. With built-in high availability, disaster recovery, and volumes, there is near zero administration required for typical production tasks.
Merger Dilemma: Once Cloudera and Hortonworks merge, will they support Cloudera Manager or Ambari? Why would you develop against a platform with uncertainty about how the platform will be administered?
Many companies have a data lake in place and have questions about how to get to the cloud, support hybrid environments, leverage containers, and extend data processing at the edge. These questions are more important than ever as customers look to understand how the Cloudera Hortonworks merger impacts product roadmap and the timeliness of future innovation. MapR answers all these questions with a generally available product today.
Mike Leone, Senior Analyst from Enterprise Strategy Group
MapR Clarity: An enterprise data catalog covers governance across enterprise systems, not just your data platform. This data catalog supports platform-based data security, machine learning discovery, data tagging, data rating, data lineage, catalog searching, data dictionary, and data lifecycle management for all data across the enterprise.
Merger Dilemma: Once Cloudera and Hortonworks merge, will they support Navigator or Atlas? Why would you develop against a platform with uncertainty about how data will be governed?
MapR Clarity: MapR is built for hybrid and multi-cloud environments. Transparently synchronize data between on-premises, edge, and one or more clouds; no third party solutions or manual effort required. A global namespace gives you a single view into your data wherever it is.
Merger Dilemma: Neither Cloudera nor Hortonworks support seamless data synchronization across deployments, and neither have global namespace capabilities. Given their underlying architectures, it is questionable whether they will get there anytime soon.
MapR Clarity: 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, containerized applications can take advantage of persistent data that is protected by replication and mirroring as well as versioned with point-in-time, consistent snapshots.
Merger Dilemma: Neither Cloudera nor Hortonworks support stateful containerized applications.
MapR Clarity: MapR is your single platform to manage historical, operational, and real-time data with integrated high-performance analytics. Only MapR lets you apply analytics in real time to operational applications under production SLAs, all without compromising on data consistency.
Merger Dilemma: Neither Cloudera nor Hortonworks supports analytical and operational applications on the same cluster. HDFS is not built for real-time. Kudu is years behind and requires a separate cluster.