Riak TS Integrations

Solr is highly reliable, scalable, and fault-tolerant. It provides distributed indexing, replication, load-balanced querying and automated failover and recovery. Solr powers many of the largest internet sites around the world's search and navigation functions. Solr allows powerful matching capabilities, including phrases, wildcards and joins, grouping, and much more across any type of data. Solr has been proven on large scales all over the globe. Solr uses the same tools that you use to make application development easy. Solr ships with an intuitive, responsive administrative interface that makes it easy to manage your Solr instances. Want to know more about your instances? Solr publishes loads metric data via JMX. Solr is built on Apache Zookeeper, which has been proven to scale well. Solr is a complete solution for replication, distribution, rebalancing, and fault tolerance.

Riak CS is a highly-available, scalable and easy-to-operate software solution for object storage that's optimized to hold videos, images and other files. It offers simple, yet powerful storage for large object built for hybrid, public and private clouds. Riak CS is a cost-effective, scalable, and easy to use solution for large object storage, whether you need it for an application or a service. It can store images, text, videos, documents, backups of databases, and software binaries. Riak CS can be used for public, hybrid or private clouds. It is compatible with Amazon S3 and OpenStack Swift, has robust APIs and can scale easily to handle petabytes using commodity software.

Mesos is built on the same principles as Linux, but at a higher level of abstraction. The Mesos kernel runs at every machine. It provides applications (e.g. Hadoop, Spark Kafka, Elasticsearch, Kafka) with API's that allow for resource management and scheduling across all datacenters and cloud environments. Native support for Docker and AppC images launching containers. Support for legacy and cloud native applications running in the same cluster using pluggable scheduling policies.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.