Apache Hop - visual, scalable, and manageable data loading to and from Neo4j

Loading data into Neo4j often starts with a number of simple scripts. As your project grows, so does the number and size of your scripts: they quickly become hard to scale, hard to manage, and hard to maintain.

Apache Hop is a new open source data orchestration platform that was designed from the ground up to tackle these problems. In Hop, data developers visually design data pipelines that can run on the Hop native engine, or on Spark, Flink, Google Dataflow over Apache Beam.

Hop has built-in life cycle management functionality to help you manage your projects and environments, version control with git, run unit tests, and more.

Apache Hop is unparalleled in its support for Neo4j with over 20 plugins to design, write to, and read from your graphs.

What we covered in this webinar:
What is Apache Hop? How does it work?
How can you load data to and extract data from Neo4j with Apache Hop?
How can Hop pipelines scale with any workload and volume of data?
How does Hop handle lineage, unit testing, version control, deployment life cycle, ...

https://hop.apache.org

https://www.know-bi.be/neo4j

Check the full 3Hx schedule: http://hop.apache.org/community/events/

Join our chat https://chat.project-hop.org

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00:00 Intro
00:30 Neo4j property graph, nodes, and relationships
03:20 Cypher
04:30 Neo4j use cases
07:00 Neo4j company intro
10:00 Loading to Neo4j
10:25 Common approaches
10:30 Problems with most data projects
13:30 What is required in a mature data project?
15:20 Apache Hop intro
21:15 Why Apache Hop?
22:40 Hop guiding principles
23:55 Key architecture features
25:50 GUI features
26:40 Key configuration features
27:40 Hop 1.0 and roadmap
29:05 Community
30:15 Apache Hop and Neo4j
31:05 Apache Hop enterprise support
31:45 Apache Hop and Neo4j demo
43:10 Q&A 1: Neo4j output and Neo4j graph output (graph model) transforms
45:50 Q&A 2: is the UI collaborative?
47:00 Q&A 3: is Hop for batch or streaming data?
48:20 Q&A 4: run Hop workflows or pipelines scheduled or automated
49:55 Q&A 5: integration with GIS platforms (both Neo4j and Apache Hop)
52:35 Q&A 6: parallel execution, Neo4j locking
55:30 Q&A 7: full load vs CDC loading
56:50 Q&A 8: deployment options