I worked on a project focused on the quality of public menus, using Neo4j AuraDB to connect and create relationships between food items. This allowed us to visualize data in interesting ways and identify communities. A key feature was using the Green Dot to link unstructured data, such as investment information, with structured data from tables and PDFs. The AuraDB documentation was also helpful in making these connections and providing valuable insights.
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Neo4j Turns Historical Data into a Queryable Knowledge Graph
What do you like best about the product?
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.
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.
What do you dislike about the product?
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.
- 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.
What problems is the product solving and how is that benefiting you?
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.
- 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.
Visualize data in interesting ways and identify communities at fair price
What is our primary use case?
What is most valuable?
The most valuable features of Neo4j AuraDB include its flexible data model and broad language support. It’s great that it offers a dedicated query language, which delivers excellent performance and high availability. Additionally, it’s hosted on AWS Cloud, which ensures reliability. The platform also allows for the integration of videos and other media.
What needs improvement?
Some features can help if they can visualize graphs better.
They have Neo4j Bloom, which is great for visualization. If you can visualize the graph directly within Neo4j AuraDB, that would also work well.
What do I think about the stability of the solution?
I don't have any problems about the performance
What do I think about the scalability of the solution?
Scalability is very good.
Which solution did I use previously and why did I switch?
I’ve used RDP before but prefer to start my analysis with Python and sometimes Neo4j Bloom. The most important feature is that Neo4j is a powerful graph database, enabling faster and more efficient analysis.
How was the initial setup?
It's very simple to create a cloud account, and it takes a few minutes to deploy.
What was our ROI?
ROI is nice because you can have an incredible return.
What's my experience with pricing, setup cost, and licensing?
It has fair pricing.
Which other solutions did I evaluate?
The community is very nice, and you can find many things.
What other advice do I have?
Neo4j AuraDB is a powerful graph database that enables us to accomplish impressive tasks. Specifically, as a cloud-based service, it eliminates the need for a high-performance computer to use it.
Sometimes, I collaborate with Smiths when working with large amounts of information. To streamline the process, I often use a chatbot agent plugin, which allows me to respond quickly in real-time, improving the overall user experience.
I've been using this chatbot agent for investment-related projects, but my first project focused on maintenance and public school menus. This initial project is more important because it involves public schools, children, and food insecurity. Conducting this analysis and developing the AI project with Neo4j could lead to meaningful results in the future. We can improve the accuracy of the model by providing context. I can't supply the necessary context if I use traditional methods, like vector regression. However, by creating a knowledge graph in Neo4j AuraDB, I can offer this context to the model, leading to better accuracy and performance.
It's very easy to maintain it.
It's an incredible tool that is quick to use and delivers impressive results. Many people should give Neo4j AuraDB a try. It's a very effective graph database.
Simplifying Machine Learning based product development with scalable graph database
What do you like best about the product?
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.
What do you dislike about the product?
one thing i think which needs to be improved is adding more sql like features in CYPHER query
What problems is the product solving and how is that benefiting you?
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.
Neo4j- Knowledge graph Database
What do you like best about the product?
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.
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.
What do you dislike about the product?
No security for data and No data encryption.
There is limit in the graph size like per graph it supports 10 B of nodes.
There is limit in the graph size like per graph it supports 10 B of nodes.
What problems is the product solving and how is that benefiting you?
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.
NEO4J
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
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.
Neo4j is an excellently supported and mature graph database
What do you like best about the product?
The active development, the large, international community, integrations, available documentation and training, certification programs, online events, friendly UI, easy-to-use query language and APOC.
What do you dislike about the product?
I cannot think of anything serious, but the upgrade process could be more straightforward and not require running multiple upgrades to get to the latest version if you're running a much older one in production.
What problems is the product solving and how is that benefiting you?
As with graph databases, one of the most generous benefits, especially compared to traditional databases, is the relationships between the data that can be easily traversed and help discover hidden connections.
Most human friendly cypher language with best integration capabilities
What do you like best about the product?
Neo4j is the most interactive and easy to use or query tool I have ever worked with. The cyphers are so user-friendly that someone with no knowledge of programming or query languages can get started at any moment which gives us an edge to explain the BI analysis and parameters to our customers. Visualizations help you debug and resolve the issues way faster compared to other DBs. And their integration with most cloud services allows a smooth integration in our applications.
What do you dislike about the product?
There is nothing that comes to my mind right now.
What problems is the product solving and how is that benefiting you?
Knowledge graph-based recommendations problems with real-time BI analytics using Looker and inhouse dashboards.
cool
What do you like best about the product?
Very intuitive and engaging. Love the different labs that neo4j offers to understand the various use cases. Easy to get on with. One good thing is browser, desktop and aura db all have a unified user experience
What do you dislike about the product?
CQL is a learning curve. Not easy to understand the syntaxes. Even though neo4j bloom is intuitive, it has many features that require some training to fully make use of it
What problems is the product solving and how is that benefiting you?
its helping visualise the data in a graph format, unlocking newer insights and relationships between entities. I have use neo4j to visualise the network connectivity
Understanding information has never been easier
What do you like best about the product?
The visuals make understanding easy for all of my team.
What do you dislike about the product?
I wish that I had a better understanding of how to program.
What problems is the product solving and how is that benefiting you?
When I can connect the relationships between customers, materials, and schedules I can make better choices.
Wonderful tool for creating Knowledge Graph- providing explanability to Black Box ML Models
What do you like best about the product?
Very Easy to convert a standard dataset to graphical form which provides more detailing and understandability.
What do you dislike about the product?
Yet not encountered, still exploring NEO4J
What problems is the product solving and how is that benefiting you?
Neo4J for Graphical Data Science and its integration with Python
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