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...Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task.

Spark Job Server offers a RESTful interface for submitting, managing, and running jobs or contexts on Apache Spark. Rather than requiring every application to embed Spark or manage Spark contexts manually, this server abstracts a long-lived service where clients can upload JARs, start and stop contexts, submit jobs synchronously or asynchronously, and manage named objects (RDDs / DataFrames) across job executions. It supports multiple modes (transient jobs, persistent contexts for reuse,...

This domain specific language lets you declare complex java objects with a Json-like notation. In addition to that, it lets you specify in a natural way what the mockito mocks should return when called with a given value.

...It was developed at Twitter to store social graph data (followers, following, blocks, etc.) and secondary indexes. FlockDB emphasizes horizontal scalability, replication, and support for high rates of writes and updates, as well as efficient paging through very large result sets. It is not a general graph database in the sense of supporting complex multi-hop traversal queries or sophisticated graph algorithms; instead it focuses on the core problem of storing and querying directed edges with attributes such as sort order, state (normal, archived, removed), and position. Edges are stored both in forward and backward directions to facilitate queries in both directions. ...