|MapR Adds Data Tiering|
|Written by Kay Ewbank|
|Monday, 09 July 2018|
MapR has added data tiering to its new release, along with access via the S3 API and the ability to run Kubernetes-based apps without changing the container images.
MapR is best known for its data platform that provides access to a variety of big data sources including Apache Hadoop and Apache Spark. MapR-DB is its built-in database designed to work with data-intensive applications that spread across local networks, edge and the cloud.
The changes to the 6.1 release start with the addition of NFSv4 support and the ability to access data via the S3 API. This adds to support for POSIX, NFSv3, and HDFS.
The second improvement is the ability to use tiered storage without needing to change applications. Cold data can be moved out of the cluster into an object storage system, where performance is lower but the costs are also lower. If data moved out is accessed, it is recalled back to the high performance access. Files, tables, and streams can even be tiered in whole or in part, and they are fully read/write even after tiering.
Encryption at rest is another improvement, both for data stored in MapR as well as data that is in tiered storage. You can also now run existing Kubernetes-based applications transparently on MapR without changing the container images that make up the applications.
The final area of improvement is a set of changes designed to simplify the development and deployment of AI and Analytic applications. This starts with the ability to run MapR and Spark 2.3 in the same cluster accessing the same data. The analytics toolkit for Hive 2.3 has had a large number of JIRAs resolved, and there's expanded SQL support for Apache Drill. Support for KSQL has also been added to enable the creation of streaming analytics apps for Kafka. Native language bindings for Python and Node.JS have been added and you can run queries directly on JSON datatypes without ETL.
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|Last Updated ( Saturday, 07 July 2018 )|