What I like best about Neo4j is how naturally it models complex relationships, especially for an application like ours that stores interconnected data about arts, artists, places, countries and other entities. In a graph database, nodes represent entities (like artists or artworks) and relationships (like "created" or "exhibited") allow for a highly intuitive representation of how these elements connect. This makes querying for complex patterns, like finding all artists who influenced a particular art movement or tracing the exhibitions of a certain artwork across different places, efficient and straightforward. What are the main points that like it more about: - That Neo4j optimizes queries for traversing relationships, such as "What art pieces were created by artists in a specific location?" which make the response faster than in traditional relational databases. - We like that you can easily expand the graph with new relationships or attributes as your dataset grows. - Also, we can search deeper in our data, finding more meaningful connections between our historical data, like trends in art styles or how artists influenced each other across regions, or the several relationship of multiple artist for a specific location or art The flexibility and performance of graph-based queries really shine when dealing with highly relational data, like historical and cultural information.

While the Neo4j offers more positive advantages than disadvantages, but for our case specifically about our history app, there are a few challenges or limitations that might be points of concern, which can be improved: - First big issue was about the restoring the old data from a different version of the database. Neo4j’s backup and restore processes are more complex compared to traditional relational databases. Maintaining backups for our history app can be a bit challenging, especially with the extensive and interconnected historical data which we are managing. As our dataset grows, ensuring that all this valuable information is securely backed up can require careful planning and additional effort. - Different query language than traditional ones. Neo4j uses Cypher, which is different than traditional and may require time to learn especially if you're coming from a SQL background like I did. For more complex queries involving relationships between artists, artworks, places, and tags, Cypher syntax can become difficult to manage, especially as the graph structure grows more intricate, you need to optimize the query to not allow a lot of memory time in the whole process results - Also, one more thing that we find of is importing data into Neo4j, especially from structured sources like Wiki pages, can be more complex than with traditional relational databases. The data needs to be transformed into a graph-friendly format, which can add a layer of complexity when dealing with large-scale imports or frequent updates from sources like Wiki.

Neo4j Graph Database solves several problems that are particularly beneficial for our history app, which stores interconnected information about arts, artists, places, countries, and types from Wiki. Here are the main keys and topics about how the Neo4j solved our goals: - First is how efficiently managing big and comples relationships: Neo4j excels at handling complex, highly interconnected data. In our app, each piece of art may be related to multiple artists, places, and historical contexts. Traditional relational databases struggle with deeply nested relationships, often requiring complex joins and leading to slow queries. Neo4j, however, is designed for querying relationships directly, allowing you to quickly find connections between entities like "artworks created by artists in specific places" or "artists influenced by others across time." What is the benefit for our app can offer fast and accurate search results, even with intricate historical data relationships, improving user experience. - Flexible of the structure for our data: As our dataset grows and evolves day by day, Neo4j allows us to easily expand our graph by adding new nodes (e.g., new artists or art types) or relationships (e.g., "influenced by" or "exhibited at"). In a historical context, new discoveries or data sources (e.g., additional Wiki information) can be easily integrated without restructuring the entire database. The main thing is that the app remains scalable and adaptable, accommodating future data changes without major disruptions. - Relationships Searching: One thing that Neo4j has ability to search deeper, contextual connections. users might want to explore how specific art movements spread geographically, or how one artist's work related to others across different periods or regions. Neo4j allows us to surface these non-obvious patterns easily, providing richer, more valuable insights to users. - Performance: As our app will grow up in the amount of stored historical data, maintaining query performance can be challenging. Neo4j is optimized for traversing vast networks of nodes and relationships efficiently, making it ideal for large-scale, relationship-driven queries.

The ease of creating graph and graph visualisation using neo4j platform

The neo4j graph query language is tough for first timer

We wanted to find root cause of increasing customer complaints for a cpg company's product by connecting the supply chain end to end in graph

The Cypher language allows you to construct queries that are understandable even without technical expertise. Open source and community edition that can be self-hosted. Neo4j Browser to visualize graph data.

For a self-hosted solution, the update process is not always straightforward.

Neo4j helps us creating and maintain a representation of the real world, with its structure of nodes and relations; something that may be impossible to do with sql-like database

the cypher syntax of neo4j is really great for beginners, it is as similar as human communicating with db in english langauage. it's just that we have to express in a correct manner for e.g if i want to retrieve all the persons with a gender male it's cypher syntax will be Match (p: Person { Gender:"Male"} ) return p ) Neo4j's Browser and Bloom feature gives business stakeholder and data scientist/analyst to analyze their data which i think currently no other database give at this moment and on top of that they have their own graph data science library which gives feasibility in developing application such as link prediction, recommendation system, chatbots

I think they should add more compute storage for Aura DB, as our system can load 100s of GB data in neo4j , but currently i dont see any option for

I have expertise in building analytical solution which speaks insights from client data , it's amazing speed of retrieval from billions of nodes and relationships is one of the reason for choosing neo4j for big data and complex relationship analysis. to be more specific we have used neo4j aura 8gb ram instance which gives us feasibility to store millions of nodes and relationships and the best thing about is infrastructure is completely managed by neo4j aura. clients are able to take better business decision based on the visual representation of nodes and relationships, we use neovis.js library for representing these data in the form of nodes and relationship and abstracting the technical logic and ensuring the security of data layer and then using algorithms like Fast RP Embeddings we created recommendation engine easily, thank you neo4j.

I have been using neo4j from last 6 month everyday for developing machine learning and data analysis products. it is really faster than RDBMS and helps in developing products such as market basket analysis, community detection and also for generative ai solutions for creating owr own chatbots. Learning neo4j is quite easy due to its documentation and community support available, It has support for multiple languages such as python, java etc. i personally use neo4j python library because it helps me in integrating with my existing machine learning product. I think every data science professional should be aware of neo4j and its power to create a scalable ML products.

one thing i think which needs to be improved is adding more sql like features in CYPHER query

Neo4j stores data in the form of nodes and relationship, this type of structure helps in faster access to data and finding patterns in data for machine learning. for e.g. recently i have implemented market basket analysis using neo4j whereas i have created product and transaction as nodes and each transaction will be connected to the products which was related to that transaction, hence i was able to get the pattern which product is frequently purchased together and even i was able to find out on which day that product is mostly baught.

The best thing about Neo4j is it is graph database and it has nodes and relationship which makes it simple. It is easy to use. And also it is schema free. Index-free adjacency helps a lot when the data is very complex

Cypher language is specific to Neo4j only.

While using the traditional databases it was very difficult to represent the relationships which were very complex and Neo4j made it easier using its graph model. Real time queries has become easier and faster. Schema free model is very helpful.

It is opensourced graph database. Semi structred data can easily represented and easily get retrive connected data faster. Scalable architecture. It helps to maintain the predictability of relation based queries.

No security for data and No data encryption. There is limit in the graph size like per graph it supports 10 B of nodes.

We are integrating Neo4j knowledge graphs with LLM. This helps to remove model hallucinations in the output or inferencing. Sometime we combine both the results of neo4j and LLM.

It is open source, has an active community behind. Well documented. You can self host it. The GUI, called Neo4j Browser, it's very good tool to visualize the data. Quite flexible, and some fast methods, in order to import graph data into it.

Sometimes the own query language, called cypher, it's a little bit obscure and hard to type.

Thanks to the good and fast import system and the viewer Neo4j Browser, it is very good to create data models.

Neo4j is a great platform for the new user to learn the commands it is very interesting, and we can see the command along with its results. we can see the result in multiple ways like in graph format, table, text or code.

I do not have much dislike about this platform but this is little confusing for the new user because it is little complicated for installation and initiate the commands. User may require the instruction for installation.

A user can easily learn the database command by implementing multiple changes in similar commands. moreover it is very interesting for the user user to run the command and the best thing is that a person can see the overview for more details about the its queries.

It allows storing data with complex relationships, which can be represented suitably, making querying easy.

It's primary use is free, but advanced features seem too costly.

Our data has various complex relationships which can be maintained and queried easily with the help of the Neo4j Graph database.