Riak TS Integrations

Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites. Solr enables powerful matching capabilities including phrases, wildcards, joins, grouping and much more across any data type. Solr is proven at extremely large scales the world over. Solr uses the tools you use to make application building a snap. Solr ships with a built-in, responsive administrative user interface to make it easy to control your Solr instances. Need more insight into your instances? Solr publishes loads of metric data via JMX. Built on the battle-tested Apache Zookeeper, Solr makes it easy to scale up and down. Solr bakes in replication, distribution, rebalancing and fault tolerance out of the box.

Riak CS is a highly available, scalable, easy-to-operate object storage software solution that’s optimized for holding videos, images, and other files. It provides simple but powerful storage for large objects built for private, public, and hybrid clouds. Whether you need large object storage for applications or you’re building a service offering, Riak CS provides a cost-effective solution that’s highly available, scalable and simple to use for storing all your images, text, video, documents, database backups and software binaries. Optimized for public, private or hybrid clouds, Riak CS is Amazon S3- and OpenStack Swift-compatible, has robust APIs, and scales easily to handle petabytes of data using commodity software that provides near-linear performance increases as capacity is added.

Mesos is built using the same principles as the Linux kernel, only at a different level of abstraction. The Mesos kernel runs on every machine and provides applications (e.g., Hadoop, Spark, Kafka, Elasticsearch) with API’s for resource management and scheduling across entire datacenter and cloud environments. Native support for launching containers with Docker and AppC images.Support for running cloud native and legacy applications in the same cluster with pluggable scheduling policies. HTTP APIs for developing new distributed applications, for operating the cluster, and for monitoring. Built-in Web UI for viewing cluster state and navigating container sandboxes.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.