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.

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.

This article will explore 20 of the most common graph algorithms and various ways to use them in real-life scenarios.

When applying for developer roles, the interviewer might ask you to solve coding problems during technical interviews. This article will help you understand some of the most fundamental ones like BFS, DFS and Dijkstra's algorithm.

Analyzing the European energy crisis and dependency on Russian natural gas imports

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.

The fastest to run any graph algorithm on your data is by using Memgraph and MAGE. It’s super easy. Download Memgraph, import your data, pick one of the most popular graph algorithms, and start crunching the numbers.

A streaming database is a real-time data repository specifically designed to store, accumulate, process and enhance a data stream.

Real-time graph analytics combines streaming data technology, graph databases, and graph algorithms to tackle problems not suited for relational databases and batch processing.

Stream processing is a type of big data architecture in which the data is analyzed in real-time

In this article, you can learn the difference between stream processing vs batch processing, and get to know the basic use cases for each.

Learn more about the benefits of real-time analytics and how they allow you to get key insights almost instantaneously.

Learn how to receive real-time data with WebSocket from Flask server using React on the client side and draw updates with D3.js

Learn about the differences between static and dynamic data, real-time data sources and ways of analyzing streaming data

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

Explore a network of retweets using a dynamic PageRank algorithm and a graph database

Explore rumor spreading in a contact network as it changes through time with online community detection

Discover how to detect communities in dynamic networks quickly with LabelRankT

An in-depth guide to understanding the dynamic node2vec algorithm and its advantages over static algorithms on streaming data

Learn the theory behind how to deal with streaming graph data by using Dynamic PageRank

Learn about a graph-based embedding technique for mapping nodes into a low-dimensional space

Find out what node embeddings are, how to generate them and where they can be used

Learn how to connect to a Pulsar cluster, create an Art Blocks NFT database in Memgraph and analyze streaming sales

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

Through this tutorial, you will learn how to connect to a Redpanda stream from Memgraph and analyze the graph data

A short story about working with librdkafka

Learn how to get Twitch data, find the most interesting statistics from the Twitch network and visualize the output of different MAGE algorithms on ingested data in real-time

Learn how to get Twitch data, find the most interesting statistics from the Twitch network and visualize the output of different MAGE algorithms on ingested data in real-time

Learn how to scrape Twitch data, find the most interesting statistics from the Twitch network and visualize the output of different graph algorithms

Check out what it takes to develop a recommendation engine with graph analytics