Apache Hop is an innovative metadata-driven that uses metadata objects to describe how you want to process your data. Apache Hop architecture takes care of the heavy lifting.
Based on visual development, Apache Hop allows you to visually design your workflows and pipelines. Let’s say scripting and code are an option, not a necessity.
You can design runtime configurations, a new metadata type, to design pipelines once, and run them in any environment, on any engine you choose.
Apache Hop places the ETL processes in a centrally managed platform, which also manages the quality and persistence of data, resulting in higher availability, and reliable information.
With Apache Hop, the data processing life cycle has become a software life cycle. Robust and reliable data processing requires testing, a fast and flexible deployment process, and a strict separation between data and metadata.
When analyzing your data, there often is more value in finding out how your data is related than in the individual data points themselves.
There are numerous use cases where graphs can provide new insights: fraud detection, social network analysis, path finding and many more.
With Neo4j's Cypher query language and the powerful algorithms that can be applied to your graphs, you'll be ready to look at your data in entirely new ways.
The combination of Neo4j and Kettle/PDI/Hop makes loading data to and extracting data from your graphs a breeze.
know.bi is ready to help in modelling, loading and querying your data into Neo4j graphs.