[MapR Newsletter] In a Consolidating Market, MapR Delivers Today!

John Schroeder CEO, Founder MapR Technologies In a Consolidating Market, MapR Delivers Today!   Recently Cloudera and Hortonworks announced their merger. The merger is not a surprise as Hortonworks M&A rumors have circulated for years. Cloudera and Hortonworks bet on commodity Hadoop and haven't differentiated themselves enough in the market. They have seen their growth stall and have been very capital intensive businesses. Hadoop alone isn't enough to support the demands of advanced analytics and AI/ML. Simply put, commodity Hadoop falls short of today's customer needs when it comes to AI, analytics, and the cloud, and this makes it hard to find a path to profitability and sustained growth. Neither has a good AI/ML story because their platforms do not support POSIX and the AI/ML libraries the way MapR does.   Read More         MapR 6.1 Release with MEP 6.0 Is Now Generally Available (GA)   MapR recently announced the general availability of the MapR 6.1 release of our data platform. This release comes with many new features and functionalities in MapR-DB and MapR-ES including the following enhancements: MapR-DB (an HBase Binary and Document Database):     •   Advanced Querying and Indexing of JSON arrays of scalar or sub-documents (aka Complex Types)   •   New programming language clients for Python and Node.js developers, based on open protocol to ease the development of additional clients   •   Fine-grained monitoring for tables that capture and expose detailed metrics into MCS   •   JSON Change Data Capture format to ease the consumption of database events in your application   MapR-ES (a Kafka API-Based Pub/Sub System)     •   Support of Apache Kafka 1.1 API   •   Stream Processing with KStreams   •   Stream SQL/Analytics with KSQL   Read More         NVIDIA and MapR, Match Made for the AI Race   Last week we announced support within the MapR Data Platform to accelerate data access and production deployments for data science through the RAPIDS open-source software from NVIDIA. The MapR Data Platform for RAPIDS enables data scientists to:     •   Collect data at scale from a variety of sources and preserve raw data so that potentially valuable features are not lost   •   Make input and output data available to many independent applications even across geographically distant locations, on premises, in the cloud or at the edge   •   Manage multiple models during development and easily roll into production   •   Improve evaluation methods for comparing models during development and production, including use of a reference model for baseline successful performance   •   Support rapid stream-based delivery of standard files including Parquet, ORC, JSON, Avro, and CSV file formats directly into RAPIDS   Learn More         New Free AI and ML Training   Visit the MapR on-demand training page and register for Intro to Artificial Intelligence and Machine Learning - the latest business-focused course on AI, made for you by the folks at MapR Academy. It's free. Take the "artificial" out of your intelligence and get your ground game on! Read the companion blogs and fill out your AI dance card.   Get Started         Join the Discussion   Whether you are new to big data, Apache Hadoop or the MapR Data Platform or are on your way to becoming an expert, we welcome your voice in the MapR Community. Click here to join several on-going discussions with other data enthusiasts.   Read More         Additional Resources     AI and Analytics in Production - How to Make it Work     MapR - NVIDIA Reference Architecture     Getting Started with Apache Spark from Inception to Production

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