* Constraints - Configurable conditions under which writes to a table will be rejected. Constraints are written in Java and configurable on a per table basis. * Sharding - Through the use of specialized iterators, Accumulo can be a parallel sharded document store. For example wikipedia could be stored and searched for documents containing certain words. * Large Rows - When reading rows, there is no requirement that an entire row fits into memory. * Namespaces - In version 1.6.0, the concept of table “namespaces” was created to allow for logical grouping and configuration of Accumulo tables. By default, tables are created in a default namespace which is the empty string to preserve the feel for how tables operate in previous versions. One application of table namespaces is placing the Accumulo root and metadata table in an “accumulo” namespace to denote that these tables are used internally by Accumulo. * Volume support - Accumulo 1.6.0 migrated away from configuration of HDFS by using a single HDFS host and directory, to a collection of HDFS URIs (host and path) which allows Accumulo to operate over multiple disjoint HDFS instances. This allows Accumulo to scale beyond the limits of a single namenode. When used in conjunction with HDFS federation, multiple namenodes can share a pool of datanodes.

* Sub-second OLAP Queries Druid’s column orientation and inverted indexes enable complex multi-dimensional filtering and scanning exactly what is needed for a query. Aggregate and filter on data in milliseconds. * Real-time Streaming Ingestion Typical analytics databases ingest data via batches. Ingesting an event at a time is often accompanied with transactional locks and other overhead that slows down the ingestion rate. Druid employs lock-free ingestion of append-heavy data sets to allow for simultaneous ingestion and querying of 10,000+ events per second per node. Simply put, the latency between when an event happens and when it is visible is limited only by how quickly the event can be delivered to Druid. * Power Analytic Applications Druid has numerous features built in for multi-tenancy. Power user-facing analytic applications designed to be used by thousands of concurrent users. * Cost Effective Druid is extremely cost effective at scale and has numerous features built in for cost reduction. Trade off cost and performance with simple configuration knobs. * Highly Available Druid is used to back SaaS implementations that need to be up all the time. Druid supports rolling updates so your data is still available and queryable during software updates. Scale up or down without data loss. * Scalable Existing Druid deployments handle trillions of events, petabytes of data, and thousands of queries every second.