Neo4j Graph Database vs Snowflake

by Gralio Feb 05, 2025

Neo4j excels in graph-based data and relationship analysis, offering flexible deployment and strong community support. Snowflake offers broader data management capabilities with cloud-based warehousing, analytics, and secure data sharing, but may have higher costs and less specialized graph features.

At Gralio.ai we help to simplify your decision-making process by offering detailed, side-by-side software comparisons like this one, to help you confidently choose the tool that aligns with your business goals.

This comparison was created by analysing 747 reviews and 60 websites, saving 4 hours, 39 minutes of reading.

Neo4j Graph Database

Snowflake

About

Neo4j Graph Database is a database management system specifically designed to store and analyze relationships in data. Unlike traditional databases, Neo4j uses a graph structure, making it ideal for uncovering complex connections and patterns. It offers tools for analytics, visualization, and querying, and can be deployed on your servers, in the cloud, or as a fully managed service. Neo4j is known for its speed and scalability, particularly with large datasets containing many relationships, and is trusted by leading companies across various industries.

Snowflake is a cloud-based data platform designed for businesses to store, manage, and analyze large amounts of data. It works like a giant, virtual warehouse for all your data, accessible from anywhere. Snowflake adapts to your needs, scaling its power and cost up or down on demand. This makes it suitable for tasks like analyzing customer trends, building data-driven applications, or sharing data securely with partners. Its strength lies in handling massive datasets with high speed and efficiency.

Summary

Main difference

Neo4j Graph Database is purpose-built for managing and analyzing interconnected data, making it ideal for applications requiring relationship analysis. Snowflake is a versatile cloud data platform specializing in data warehousing, analytics, and secure data sharing, catering to broader data management needs.

Relative strengths of Neo4j Graph Database (compared to Snowflake)

Superior graph data handling and relationship analysis capabilities, particularly with Cypher query language.
Flexible deployment options, from self-hosting to fully managed cloud services.
Strong community support and extensive documentation.

Relative weaknesses of Neo4j Graph Database (compared to Snowflake)

Can be more complex to learn and implement for users unfamiliar with graph databases.
Limited storage options for the AuraDB cloud service, especially for very large datasets.
Advanced features and enterprise edition can be expensive, potentially impacting value for mid-sized businesses.

Who should use Neo4j Graph Database VS. Snowflake

Neo4j is a powerful graph database solution for developers and data scientists working with complex datasets. Its intuitive interface and the expressive Cypher query language enable in-depth relationship analysis. Users praise its speed and performance, even with large datasets, while some find the storage options for AuraDB limited. Neo4j is known for its excellent visualization tools and strong community support.

Snowflake is a cloud-based data platform ideal for data-driven businesses of all sizes needing to store, manage, and analyze large datasets. Users praise its scalability and easy-to-use interface, enabling seamless integration with various data tools. However, some have noted limitations in machine learning features and unstructured data support. Snowflake excels in handling massive datasets with high speed and efficiency, making it suitable for complex analytics, building data-driven applications, and secure data sharing.

Best for small, medium, and enterprise companies.
Ideal for Software, IT, Telecommunications, and other data-heavy industries.

Best for small, medium, and large enterprises.
Ideal for Software, IT, Telecommunications, and various other industries.

Neo4j Graph Database and Snowflake features

Supported

Partially supported

Not supported

Type in the name of the feature or in your own words tell us what you need

High-Speed Graph Database

Supported

Neo4j is designed for high-speed graph operations, enabling quick access to connected data.

Partially supported

Snowflake supports high-speed graph databases through integrations with Neo4j and TigerGraph.
Flexible Deployment Options

Supported

Neo4j offers flexible deployments including self-hosting, hybrid cloud, multi-cloud, and fully managed cloud services.

Partially supported

Snowflake supports multi-cloud and fully managed cloud, but not self-hosting or hybrid cloud deployments.
Data Warehouse Integration

Supported

Neo4j integrates with data warehouses like Snowflake, BigQuery, Redshift, and Azure Synapse.

Supported

Snowflake excels in data warehouse integration, seamlessly connecting with diverse data sources like relational databases, cloud platforms, NoSQL databases, and streaming services. Its compatibility with third-party tools, data sharing capabilities, flexible architecture, support for semi-structured data, and query optimization prowess make it highly capable of integrating with other warehouse databases.
SQL support

Not supported

Neo4j does not support SQL for querying data. It uses its own query language called Cypher.

Supported

Snowflake fully supports querying data via SQL.
Columnar storage

Not supported

Neo4j does not use columnar storage.

Supported

Snowflake stores data in a columnar format, meaning data for each column is stored together, separate from other columns. This is beneficial for data analytics and business intelligence applications where calculations are often performed over a single data column.
SQL data transformation

Not supported

Neo4j does not support SQL queries. It uses Cypher, a graph query language, for data transformations.

Supported

Snowflake supports data transformation using SQL queries, including filtering, aggregating, joining, and manipulating data. It also supports views, stored procedures, and UDFs for this purpose.

Qualities

Value and Pricing Transparency

No data

-0.26

Neutral sentiment
Customer Service

No data

+0.56

Rather positive sentiment
Ease of Use

+1

Strongly positive sentiment

+0.75

Strongly positive sentiment
Reliability and Performance

+1

Strongly positive sentiment

+0.81

Strongly positive sentiment
Ease of Implementation

No data

+0.33

Rather positive sentiment
Scalability

No data

+0.93

Strongly positive sentiment

Neo4j Graph Database and Snowflake Pricing

Neo4j offers different pricing plans for both their self-managed graph database and their fully managed graph database as a service AuraDB. AuraDB has a free plan, a professional plan for building production-ready apps, a business-critical plan for scaling apps for enterprise use, and a virtual dedicated cloud option. Neo4j also offers a community edition of their self-managed graph database, which is open source and free to use. Their enterprise edition, in contrast, is for enterprise-grade availability and security. For their graph data science product, Neo4j offers a community edition, an enterprise edition, and AuraDS in a professional and enterprise plan.

Plans

User sentiment

Strongly positive

See full Pricing page

No data

User sentiment

Neutral

-0.26

See full Pricing page

Neo4j Graph Database and Snowflake review insights

747 reviews analysed from and

Users love

Highly intuitive and user-friendly interface for querying and visualizing graph data.
Cypher query language is very expressive and allows for complex relationship analysis.
Excellent performance and speed, even with large datasets containing many relationships.
Neo4j Browser is a great tool for visualizing and interacting with graph data.
Strong community support and extensive documentation.

The ability to easily scale warehouses based on requirements
Query profile is a very useful feature
The user interface is easy to implement and navigate
Seamless integration with various data tools

Users dislike

Limited storage options for AuraDB, especially for large datasets.
Cypher query language can be difficult to learn for users unfamiliar with graph databases.
Documentation can be improved with more practical examples and real-world use cases.
Upgrade process for self-hosted instances is not always straightforward.
Advanced features and enterprise edition can be expensive.

Limited machine learning features
Data migration efforts are large to transfer everything from on premises to cloud
The new UI is too confusing
Unstructured data support is lacking

Neo4j Graph Database and Snowflake Ratings

G2

4.5/5

(131)
Glassdoor

3.6/5

(180)

G2

4.5/5

(538)
Capterra

4.6/5

(78)
Glassdoor

4.0/5

(749)

Company health

Employee growth

9% increase in the last year

20% increase in the last year

Web traffic

19% increase in the last quarter

1% increase in the last quarter

Financing

August 2021 - $755M

March 2022 - $2B

How do Snowflake's data warehousing capabilities compare to Neo4j's graph-based analysis for uncovering relationships?

Snowflake excels at data warehousing, providing a scalable and efficient platform for storing, processing, and analyzing large datasets using SQL. It's designed for structured data and traditional business intelligence tasks. Neo4j, on the other hand, specializes in graph-based analysis, focusing on uncovering relationships and patterns within connected data. Neo4j uses a graph data model and the Cypher query language, making it ideal for exploring complex connections that are difficult to analyze with traditional relational databases like Snowflake. While Snowflake can handle some semi-structured data, Neo4j is purpose-built for relationship analysis where understanding connections is paramount.

Which platform better suits real-time analytics, Snowflake's SQL-based approach or Neo4j's graph traversal speed?

Neo4j is better suited for real-time analytics involving complex relationships. Its graph traversal speed excels at quickly navigating and analyzing connections within data, making it ideal for applications like social network analysis, recommendation engines, and fraud detection. While Snowflake's SQL-based approach is powerful for structured data and large-scale analytics, it's not as optimized for the rapid traversal of relationships that characterize real-time graph analytics.

What are the advantages of Neo4j Graph Database?

Neo4j's advantage lies in its native graph database structure, enabling superior performance and ease of use when dealing with complex relationships within data. This makes it ideal for tasks like social network analysis, recommendation engines, and fraud detection where understanding connections is crucial, unlike Snowflake which is designed for more general data warehousing and analytics. Neo4j also offers a user-friendly interface specifically designed for graph data exploration and visualization, simplifying complex queries and analysis compared to Snowflake's SQL-centric approach.

What are the disadvantages of Neo4j Graph Database?

Neo4j's Cypher query language, while powerful, can be challenging to learn for users unfamiliar with graph databases. The limited storage options for AuraDB, particularly for large datasets, can be a constraint. Users also mention that the documentation could be improved with more practical examples and real-world use cases. Finally, the cost of advanced features and the enterprise edition can be prohibitive for some.

Alternatives to Neo4j Graph Database and Snowflake

Microsoft SQL Server

Microsoft SQL Server is a database management system for businesses of all sizes. It helps you analyze various types of data and can be used in multiple environments, including on your servers and in the cloud. SQL Server offers high performance and strong security features. It allows developers to build applications using different programming languages and provides mobile business intelligence tools.

Tigergraph

TigerGraph is a graph database designed for businesses to analyze large, complex datasets. It helps uncover hidden relationships within your data, like connections between customers, products, and transactions. This allows for faster and more insightful decision-making in areas like fraud detection, personalized recommendations, and supply chain optimization. TigerGraph is known for its speed, handling massive datasets, and its ability to analyze data in real time.

Google Cloud BigQuery

Google BigQuery is a fully managed data warehouse service that helps businesses analyze massive datasets to gain insights. It's serverless, so you don't need to manage any infrastructure, and it can handle data from various sources. BigQuery is designed for fast query processing, allowing you to quickly explore and analyze your data. It's a powerful tool for data-driven decision-making in any industry, regardless of company size.

GraphDB

GraphDB is a database tool that excels at connecting different types of data. It uses a flexible structure called a "knowledge graph" to find links and relationships within your data, making it easier to search and analyze. GraphDB is suitable for both small and large businesses and offers a free version for smaller projects. The enterprise version allows for more data and faster performance. Its strength lies in handling large amounts of data and uncovering hidden connections in real-time, ultimately leading to better business insights.

Palantir Foundry

Palantir Foundry is a software platform designed to help large organizations manage and analyze their data. It brings all your data together in one place, making it easier to find insights, make decisions, and automate operations. Foundry is used by businesses in various sectors, including finance, healthcare, and government. It is known for its advanced security features and its ability to handle very large and complex datasets.

Amazon Neptune

Amazon Neptune is a database service designed specifically for handling large datasets with complex relationships. It allows you to easily store and quickly analyze connections within your data, like social networks, product recommendations, or fraud detection patterns. Amazon Neptune manages all the technical complexities, so you can focus on using the insights from your connected data.

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