Comparison

Choose wisely! The right graph database can make or break your project. Discover how DB-Engines rescues developers from regretful decisions.

In this article, we will compare two leading graph databases, Memgraph and Neo4j graph database, to help you choose the best platform for your needs.

Discover the differences between relational and graph databases, their respective characteristics, features, and applications. This article also delves into Cypher and SQL query languages, various data modeling techniques, and differences in performance.

Learn how to pick the best streaming analytics tool for your use case

Little by little, the hosting cost of a graph database can turn out to be quite substantial. Hosting costs are highly correlated with how much resources a database uses, which is not as straightforward as you might think. Find out how expensive it is to host Memgraph and Neo4j instances and why.

When choosing a graph database, you are probably thinking about the costs of certain features your solution requires. The more production-ready the community edition is, the fewer costs you will have! If you are choosing between Memgraph and Neo4j, both open-source databases, check how community editions compare and find out why Memgraph is a feature-rich community edition ready for production.

Memgraph delivers results up to 120 times faster than Neo4j while consuming one quarter of the memory!

Are your NetworkX algorithms taking even more and more time to produce the results you need to finish up your research? Or the application reached a critical point and its starting to lag due to increase in data analysis? Could Memgraph tackle the same computations in less time? I think you probably know the answer is “Doh!” but here are the numbers to prove it.

You can't always get what you want, but if you try sometimes, well, you might find, you get what you need. We took The Rolling Stones' advice and tried really hard to find the visualization tool that we need... It didn't really turn out as we wanted, or needed, but at least we had fun testing some software.

In-memory databases primarily rely on RAM storage instead of using hard or external disks for memory storage. Let’s take a look at how in-memory databases work along with some use cases in data science.

A Knowledge Graph is a reusable data layer that is used to answer sophisticated queries across multiple data silos. With contextualized data displayed and organized in the form of tables and graphs, they achieve pinnacle connectivity.

Query languages used for graph data management are called graph query languages (GQLs). Below are the popular graphDB query languages that provide a way to unpack information in graphs. Let’s dive into the details of popular query languages with their pros and cons.

Some use cases in data analysis require near-instantaneous processing of data. To make it happen, you need tools for real-time data analysis that support the use of continuous queries.

A basic overview of the architecture and features offered by Apache Pulsar and Apache Kafka

Learn about the benefits of using a graph database instead of SQL in terms of data modeling, querying, and development speed and flexibility.