If you need a SQL-capable database-like solution that is file-based and embeddable in your existing Java Virtual Machine processes, Apache Derby is an open-source, zero cost, robust and performant option. You can use it to store structured relational data but in small files that can be deployed right alongside with your solution, such as storing a set of relational master data or configuration settings inside your binary package that is deployed/installed on servers or client machines.
Neo4J is great for creating network graphs or illustrating how things are related. It is also good for finding individuals or things that have greater influence than others in a system. It is not appropriate if you have standard data sets that can be analyzed using conventional methods or visualized using Tableau, for example.
Apache Derby is SMALL. Compared to an enterprise scale system such as MSSQL, it's footprint is very tiny, and it works well as a local database.
The SPEED. I have found that Apache Derby is very fast, given the environment I was developing in.
Based in JAVA (I know that's an obvious thing to say), but Java allows you to write some elegant Object Oriented structures, thus allowing for fast, Agile test cases against the database.
Derby is EASY to implement and can be accessed from a console with little difficulty. Making it appropriate for everything from small embedded systems (i.e. just a bash shell and a little bit of supporting libraries) to massive workstations.
Mature Query language, I found Cypher QL to be mature in handling all sorts of problems we throw at it. Its expressive enough to be intuitive while providing rich features for various scenarios.
Native support for REST API, that makes interacting with Neo4J intuitive and easy.
Support for Procedures in Java, procedures are custom code that could be added to the Neo4J to write custom querying of data. The best part about the procedures is it could be invoked using the REST API. This allows us to overcome any shortcomings from their Cypher query language.
Nice UI and interface for executing the Query and visualizing the response.
UI access controlled by User credentials allows for neat access controls.
Awesome free community edition for small-scale projects.
One of the hardest challenges that Neo4j had to solve was the horizontal scaling problem. I am not updated on recent developments, but at the time of my use, I couldn't find a viable solution.
Neo4j does not play with other open source APIs like Blueprint. You have to use the native Neo4j API.
There wasn't a visual tool to see your data. Of course, third party tools are always available, but I would have loved something which came with the Neo4j bundle. I love that Docker comes bundled with Kitematic, so it's not wrong to hope that Neo4j could also ship with some default visualization software.
SQLite is another open-source zero-cost file-based SQL-capable database solution and is a good alternative to Apache Derby, especially for non-Java-based solutions. We chose Apache Derby as it is Java-based, and so is the solution we embedded it in. However, SQLite has a similar feature set and is widely used in the industry to serve the same purposes for native solutions such as C or C++-based products.
Neo4j is a graph store and has different use cases compared to another NoSQL Document store like MongoDB. MongoDB is a bad choice when joins are common as existing operators for joining two documents (similar to tables in a relational store) as Mongo 3.5 use SQL like join algorithms which are expensive. MongoDB is a great choice when distributed schemaless rich document structures are important requirements. Cross document transaction support is not native to MongoDB yet, whereas Neo4J is ACID complaint with all its operations.
Being Open source, the resources spent on the purchase of the product are ZERO.
Contrary to popular belief, open source software CAN provide support, provided that the developers/contributors are willing to answer your emails.
Overall, the ROI was positive: being able to experiment with an open source technology that could perform on par with the corporate products was promising, and gave us much information about how to proceed in the future.
