Predictive Works's repositories
ignite-graph
IgniteGraph is a client layer for using Apache Ignite as a graph database. It is an implementation of the Apache TinkerPop interfaces.
works-sqlstream
This project complements Apache Spark structured streaming with hand-picked streaming sources and sinks.
deep-graph
DeepGraph supports a 360° view on graph technology and supports analytics, storage and visualization.
grafana-ignite-datasource
Apache Ignite Data Source Plugin for Grafana
ignite-janus
Apache Ignite Storage Backend for JanusGraph
cdap-spark
A wrapper for Apache Spark to make machine & deep learning available in Google CDAP data pipelines.
sqlalchemy-ignite
This project provides a SQLAlchemy driver for Apache Ignite. It was built to enable (ad-hoc) data exploration and visualization of datasets managed by Apache Ignite.
works-beats
A collection of standalone Akka-based Http(s) services to connect to Fiware, OpenCTI, Osquery fleets, OPC-UA, ThingsBoard and Zeek.
aerospike-graph
AerospikeGraph is a client layer for using Aerospike as a graph database. It is an implementation of the Apache TinkerPop interfaces.
cdap-connect
Purpose-built data connectors for Google CDAP data pipelines
witsml-frames
WITSML Frames provides a DataFrame-centric view over WITSML data and prepares them for Apache Spark based machine learning and deep learning.
works-stream
This project complements Apache Spark Streaming with hand-picked streaming receivers for Eclipse Ditto, Fiware Context Broker, Fleet (DM), Eclipse Paho, HiveMQ, OpenCTI, Google PubSub, Server Sent Events, ThingsBoard and Zeek Sensor.
akka-sigma
This project implements an Akka-based wrapper for Sigma rules.
akka-streams-ignite
This project supports stream-aware, reactive, ingestion pipelines for Apache Ignite in Java and Scala.
cdap-graph
CDAP plugins to use JanusGraph as data source and sink with CDAP data pipelines
sage-frames
SageFrames brings together Sage ERP cloud platform and DataFrame-based data science. Transforming purchase & sales data into insights, recommendations and more with Apache Spark has never been easier.