Predict customer behavior and make accurate product recommendations

By analyzing customers' habits and needs, data analysts can provide spot-on recommendations that will enhance customer satisfaction and boost sales.

Maximize the value of customer-product interactions

Analyze interactions between customers and products to gain valuable insights into users' habits, power up your recommendation systems and boost sales.

Develop the recommendation tools progressively

Build your product recommendations incrementally, from simple proofs to complete and robust recommendation tools based on graphs

Unlocking complex insights with simple queries

All insights, even complex ones like graph neural networks, are available by running built-in graph algorithms within simple queries. No complex JOINS, no coding.


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Graph Technology in Recommendation Engines

Having a truly accurate and adaptable recommendation engine can either make or break a business, and keeping up with the rapid growth and complexity of data is not an easy task. Luckily, graph databases and algorithms specifically designed to analyze customer behavior provide powerful and real-time recommendations.

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Why use Memgraph?

Performance for real-time needs

Handling real-time, highly concurrent, write-heavy data transactions is what Memgraph is all about.

C++ in-memory architecture gives Memgraph a massive head start for any real-time graph analysis needed. Don't just take our word for it, check out the benchmark results and validate them for yourself!

Available High Availability

High Availability is a major concern for developers. Through Memgraph's open source commitment, high availability is available to all developers through the Community Edition.

No need to fork out cash and the Enterprise licence to get an uninterrupted and continuous graph database system.

Full flexibility

Memgraph is a natural drop-in replacement as it is Cypher-ready.

There are multiple drivers to connect to the DB and allowing you to write custom procedures in various languages (Python, C/C++).

Fire up your instance on-prem or on AWS Cloud with a two-week free trial.

Blog post

Why Are SQL Databases Outdated for the Real-Time Recommendation Engines

If a recommendation engine built on relational databases is falling a part due to the bottlenecks made by complex JOINs and never-ending schema changes, there is only one permanent and game changing solution - graph databases.

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Frequently Asked Questions

What use cases is Memgraph best suited for?


Memgraph is best suited for use cases with complex data relationships that require real-time processing and high scalability.

How do I know my use case has complex data relationships?


In relational databases, complex data relationships arise when data from different tables is related or somehow interconnected. Because data is spread across multiple tables, querying it requires hopping from one table to other and joining it with slow and resource-intensive join operations.The complexity of join operations can increase exponentially as the number of tables increases and as the links between various tables are no longer neatly structured following a clearly set pattern. It is no longer sufficient to join just two or three tables but hop through more than seven tables to find the correct link between the data and gain valuable analytics.Examples of complex data are deep hierarchical relationships between data, such as parent-child relationships or many-to-many relationships between different tables.

What are the benefits of being an in-memory graph database?


When data is stored on disk, the computer has to physically read it from the disk and transfer it to the RAM before it can be processed. This process is relatively slow because it involves several physical processes, such as seeking the right location on the disk and waiting for the data to be read. Writing the data is also much slower for the same reasons.Storing data in the computer's RAM eliminates the need for these physical processes, and data can be accessed and added almost instantly. Therefore, in-memory graph databases are ideal for applications requiring fast data processing, real-time analytics, and quick response times.

How does Memgraph compare to other graph databases regarding performance?


Memgraph is designed to be a high-performance graph database, and it typically outperforms many other graph databases in terms of speed and scalability. Key factors contributing to Memgraph's performance are its in-memory architecture and a performant query engine written in C++. Memgraph also offers a variety of tools and features to help optimize query performance, including label and label-property indexes and a custom visualization library. Check our benchmark comparing Memgraph and Neo4j.

Can I try out Memgraph Cloud before making a decision?


As a new user, try out Memgraph Cloud in a 14-days free trial, just log in with a Google account or create a Memgraph Cloud account.

What kind of support does Webflow provide?


We offer fast email support to paid accounts and prioritized help for team accounts. Community support ( is available to free accounts.

How long does it take to learn Webflow?


If you're new to building websites, our video tutorials will get up and running quickly. If you already know concepts behind CSS and the box model, you will feel at home in Webflow.

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Get started today!

Interested in finding out more but not sure where to start? Contact us and we'll be in touch to get you started!

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