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Azure Cosmos DB

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Azure Cosmos DB vs Neo4j: What are the differences?

Developers describe Azure Cosmos DB as "A fully-managed, globally distributed NoSQL database service". Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development. On the other hand, Neo4j is detailed as "The world’s leading Graph Database". Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.

Azure Cosmos DB can be classified as a tool in the "NoSQL Database as a Service" category, while Neo4j is grouped under "Graph Databases".

Some of the features offered by Azure Cosmos DB are:

  • Fully managed with 99.99% Availability SLA
  • Elastically and highly scalable (both throughput and storage)
  • Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes

On the other hand, Neo4j provides the following key features:

  • intuitive, using a graph model for data representation
  • reliable, with full ACID transactions
  • durable and fast, using a custom disk-based, native storage engine

"Best-of-breed NoSQL features" is the primary reason why developers consider Azure Cosmos DB over the competitors, whereas "Cypher – graph query language" was stated as the key factor in picking Neo4j.

Neo4j is an open source tool with 6.56K GitHub stars and 1.62K GitHub forks. Here's a link to Neo4j's open source repository on GitHub.

Movielala, Hinge, and Sportsy are some of the popular companies that use Neo4j, whereas Azure Cosmos DB is used by Microsoft, Rumble, and Property With Potential. Neo4j has a broader approval, being mentioned in 114 company stacks & 47 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 23 developer stacks.

Advice on Azure Cosmos DB and Neo4j
Jaime Ramos
Needs advice
on
Neo4jNeo4jDgraphDgraph
and
ArangoDBArangoDB

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

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Replies (2)
Recommends
ArangoDBArangoDB

I have not used the others but I agree, ArangoDB should meet your needs. If you have worked with RDBMS and SQL before Arango will be a easy transition. AQL is simple yet powerful and deployment can be as small or large as you need. I love the fact that for my local development I can run it as docker container as part of my project and for production I can have multiple machines in a cluster. The project is also under active development and with the latest round of funding I feel comfortable that it will be around a while.

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David López Felguera
Full Stack Developer at NPAW · | 5 upvotes · 19.4K views
Recommends
ArangoDBArangoDB

Hi Jaime. I've worked with Neo4j and ArangoDB for a few years and for me, I prefer to use ArangoDB because its query sintax (AQL) is easier. I've built a network topology with both databases and now ArangoDB is the databases for that network topology. Also, ArangoDB has ArangoML that maybe can help you with your recommendation algorithims.

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Needs advice
on
OrientDBOrientDBNeo4jNeo4j
and
Azure Cosmos DBAzure Cosmos DB

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

  1. Is a graph database the right choice, or can we manage with RDBMS?
  2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
  3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

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Replies (1)
Recommends
ArangoDBArangoDB

You have not given much detail about the data generated, the depth of such a graph, and the access patterns (queries). However, it is very easy to track all samples and materials if you traverse this graph using a graph database. Here you can use any of the databases mentioned. OrientDB and ArangoDB are also multi-model databases where you can still query the data in a relational way using joins - you retain full flexibility.

In SQL, you can use Common Table Expressions (CTEs) and use them to write a recursive query that reads all parent nodes of a tree.

I would recommend ArangoDB if your samples also have disparate or nested attributes so that the document model (JSON) fits, and you have many complex graph queries that should be performed as efficiently as possible. If not - stay with an RDBMS.

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Pros of Azure Cosmos DB
Pros of Neo4j
  • 27
    Best-of-breed NoSQL features
  • 21
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Tunable consistency
  • 10
    Always on with 99.99% availability sla
  • 7
    Javascript language integrated transactions and queries
  • 6
    Predictable performance
  • 5
    High performance
  • 5
    Analytics Store
  • 2
    Ease of use
  • 2
    No Sql
  • 2
    Rapid Development
  • 2
    Auto Indexing
  • 68
    Cypher – graph query language
  • 58
    Great graphdb
  • 31
    Open source
  • 29
    Rest api
  • 27
    High-Performance Native API
  • 24
    ACID
  • 20
    Easy setup
  • 15
    Great support
  • 10
    Clustering
  • 9
    Hot Backups
  • 8
    Great Web Admin UI
  • 7
    Powerful, flexible data model
  • 7
    Mature
  • 6
    Embeddable
  • 5
    Easy to Use and Model
  • 4
    Best Graphdb
  • 4
    Highly-available
  • 2
    It's awesome, I wanted to try it
  • 2
    Great onboarding process
  • 2
    Great query language and built in data browser
  • 2
    Used by Crunchbase

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Cons of Azure Cosmos DB
Cons of Neo4j
  • 15
    Pricing
  • 3
    Poor No SQL query support
  • 4
    Comparably slow
  • 4
    Can't store a vertex as JSON
  • 1
    Doesn't have a managed cloud service at low cost

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- No public GitHub repository available -

What is Azure Cosmos DB?

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

What is Neo4j?

Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.

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What companies use Azure Cosmos DB?
What companies use Neo4j?
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What are some alternatives to Azure Cosmos DB and Neo4j?
Azure SQL Database
It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.
MongoDB Atlas
MongoDB Atlas is a global cloud database service built and run by the team behind MongoDB. Enjoy the flexibility and scalability of a document database, with the ease and automation of a fully managed service on your preferred cloud.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
See all alternatives