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  1. Stackups
  2. Application & Data
  3. NoSQL Databases
  4. NOSQL Database As A Service
  5. Azure Cosmos DB vs Neo4j

Azure Cosmos DB vs Neo4j

OverviewDecisionsComparisonAlternatives

Overview

Azure Cosmos DB
Azure Cosmos DB
Stacks594
Followers1.1K
Votes130
Neo4j
Neo4j
Stacks1.2K
Followers1.4K
Votes351
GitHub Stars15.3K
Forks2.5K

Azure Cosmos DB vs Neo4j: What are the differences?

Azure Cosmos DB is a globally distributed, multi-model database service provided by Microsoft, Neo4j is a graph database management system. Below are the key differences between the two.

  1. Data Model: One significant difference between Azure Cosmos DB and Neo4j is their data models. Azure Cosmos DB supports multiple data models, including key-value, document, column-family, and graph models. In contrast, Neo4j exclusively focuses on the graph data model, making it ideal for complex connections and relationships between data.

  2. Scalability: When it comes to scalability, Azure Cosmos DB's design allows it to scale horizontally across multiple regions and data centers. It offers both manual and automatic scaling options, providing flexibility to handle variable workloads effectively. On the other hand, while Neo4j does support clustering to distribute the graph database over multiple machines, its scalability is comparatively limited.

  3. Query Language: Azure Cosmos DB uses SQL (Structured Query Language) for querying data, making it familiar and easy to use for developers who are already familiar with SQL. In contrast, Neo4j uses a specialized query language called Cypher, specifically designed for querying graph data. Learning and using Cypher may require additional effort for developers who are not familiar with it.

  4. Indexing: In Azure Cosmos DB, indexing is automatic by default, ensuring high performance for queries on various data models. It offers multiple indexing options to optimize query performance. On the other hand, in Neo4j, although indexing is supported, it needs to be explicitly defined by developers for properties they wish to search frequently, which may require additional effort and maintenance.

  5. Consistency Models: Azure Cosmos DB offers multiple consistency models, including strong, bounded staleness, session, and eventual consistency. Developers can choose the desired consistency level based on their application's requirements. In contrast, Neo4j provides strong consistency as the default option but lacks the flexibility to choose different consistency models.

  6. Deployment Options: Azure Cosmos DB is offered as a fully-managed service in the cloud, providing high availability, automatic backups, and seamless scaling without infrastructure management. It can be integrated with other Azure services and deployed across multiple regions globally. Neo4j, on the other hand, can be deployed in various ways, including on-premises, virtual machines, containers, or in the cloud, offering more deployment flexibility, albeit with additional management responsibilities.

In summary, Azure Cosmos DB offers multiple data models, automatic scaling, SQL querying, flexible indexing, various consistency models, and a fully-managed cloud deployment option. On the other hand, Neo4j focuses exclusively on the graph data model, supports clustering for scalability, uses the specialized Cypher query language, requires explicit indexing, offers strong consistency by default, and provides deployment flexibility.

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Advice on Azure Cosmos DB, Neo4j

Thiru
Thiru

Jul 16, 2020

Needs adviceonMySQLMySQLNeo4jNeo4jOrientDBOrientDB

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}|tool:1482|, @{OrientDB}|tool:1611|, @{Azure Cosmos DB}|tool:1369|, @{Amazon Neptune}|tool:7979|, @{ArangoDB}|tool:1440|), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

217k views217k
Comments
Jaime
Jaime

none at none

Aug 31, 2020

Needs advice

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.

56.4k views56.4k
Comments

Detailed Comparison

Azure Cosmos DB
Azure Cosmos DB
Neo4j
Neo4j

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.

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.

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;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API
Statistics
GitHub Stars
-
GitHub Stars
15.3K
GitHub Forks
-
GitHub Forks
2.5K
Stacks
594
Stacks
1.2K
Followers
1.1K
Followers
1.4K
Votes
130
Votes
351
Pros & Cons
Pros
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Always on with 99.99% availability sla
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Pros
  • 69
    Cypher – graph query language
  • 61
    Great graphdb
  • 33
    Open source
  • 31
    Rest api
  • 27
    High-Performance Native API
Cons
  • 9
    Comparably slow
  • 4
    Can't store a vertex as JSON
  • 1
    Doesn't have a managed cloud service at low cost
Integrations
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python
No integrations available

What are some alternatives to Azure Cosmos DB, Neo4j?

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

Cloud Firestore

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Cloudant

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Google Cloud Bigtable

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

Google Cloud Datastore

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

CloudBoost

CloudBoost

CloudBoost.io is a database service for the “next web” - that not only does data-storage, but also search, real-time and a whole lot more which enables developers to build much richer apps with 50% less time saving them a ton of cost and helping them go to market much faster.

Dgraph

Dgraph

Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.

Firebase Realtime Database

Firebase Realtime Database

It is a cloud-hosted NoSQL database that lets you store and sync data between your users in realtime. Data is synced across all clients in realtime, and remains available when your app goes offline.

RedisGraph

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

Cayley

Cayley

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned.

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