Discover how LLMs & graph databases can improve community engagement in our latest webinar with insights from Orbit's Steeve Bete.

Check out how University of West Florida researchers used Memgraph to boost ML for cyber threat detection in their latest study.

Memgraph uses Cypher - an open query language for property graph databases to query the database. Although Cypher is intuitive, switching from one tech stack to another within your team is sometimes hard. To bring graph technology close to any user, we created GraphChat - a new feature in Memgraph Lab that allows you to query a graph database with the natural language.

Discover the power of integrating Confluent's Kafka platform with Memgraph for data management. Learn how to use Docker Compose to create an environment where Kafka's robust data streaming capabilities merge with Memgraph's rapid data processing.

How can denormalization improve query speeds and data retrieval in graph databases with large, interconnected datasets? Explore the balance between enhanced performance and data simplicity, and read about effective techniques for applying denormalization to achieve optimal database efficiency.

Exploring how to write complex Cypher queries and mutations in Memgraph to effectively work with graph data

Overview of few interesting visualization tools that can be used in Python and guide on how to implement and use them.

Explore the Apache License 2.0, a open-source license offering a balanced blend of freedom and protection for developers. Originating from the Apache Software Foundation's, this license has evolved alongside open-source development practices. Key features include its permissive nature for using, modifying, and distributing software, flexibility around derivative works, and robust protection against patent claims.

In this blog post, our very own intern, Patrik, shares his experience creating a backing for a powerful knowledge extraction engine.

In this article, Oleksandr, one of Memgraph’s amazing interns, shares insights on open-source contribution and the story behind Memgraph and LangChain.

Learn about Oleksandr's perspective on creating a GitHub LLM analytics tool as part of Memgraph's intern program this summer.

Read on to learn about Obsidian note-taking with ODIN and get to know the backstory behind Memgraph's integration with Obsidian from our very own intern, Oleksandr's perspective.

Read on to learn more about a brand new real-time graph visualization of Bluesky, a new social network for microblogging.

Enhance the speed of query execution with our detailed guide. Optimize your database performance and boost productivity.

Explore the importance of betweenness centrality and other key centrality measures in network analysis through our comprehensive blog post.

Explore the possibilities of Memgraph and Kubernetes with our latest Helm chart repository.

Discover the power of graph databases and their role in modern data management. Explore the definition, advantages, and applications of graph databases in this comprehensive article.

Memgraph is preparing new community call and this time with a special guest, Brett Brewer, who will talk about querying Memgraph through an LLM.

In this case study, you will learn how the collaboration between Memgraph and Company X yielded impressive results, including a 135% increase in detection efficiency and savings in the seven-figure range.

PostgreSQL, also known as Postgres, is an open-source relational database management system with a strong reputation for reliability, feature robustness, and performance. It is known for its ability to handle a diverse range of workloads, but what sets it apart from other databases? Uncover the unique advantages of PostgreSQL, explore its core features, and see why it might be the right choice for your next project.

Explore the fundamental differences between SQL and NoSQL databases, get to know their features, advantages, and explore examples in this blog.

Though ensuring data consistency in a database can be challenging, to keep the data consistent, databases employ the principles of ACID transactions. In this article, you will explore ACID properties and the meaning of isolation levels for your application.

Explore the foundations of graph search algorithms and learn how to leverage them effectively to unlock new possibilities for solving complex problems and advancing your development skills.

This article will guide you through all the factors to consider when choosing a database, providing insights into popular database models to help you make an informed decision.

This is the only deep dive you need to compare different types of NoSQL databases and explore their applications across various industries.

How to find credible sources to help you learn more about graphs and apply them to your use case? Save these 7 graph algorithm books and e-resources in the bonus section.

From social networks and biological systems to recommendation engines, graph clustering algorithms enable data scientists to gain insights and make informed decisions that create value.

Graphlytic has introduced support for Memgraph DB and Memgraph Cloud which enables seamless connectivity between your Memgraph instance and any Graphlytic installation.

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

Meet the three companies at the Summit and dive into the Integrated Link Analysis for unstructured data powered by AI, NLP and Graph Analytics.

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.

In the modern era of technology, safeguarding sensitive information and critical assets poses a significant cybersecurity challenge for organizations. To address this issue, Saporo and Memgraph have joined hands to provide a potent solution that helps businesses detect and minimize potential routes of attack.

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.

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

How many recommendation algorithms do you need for a successful recommendation engine? Start with these five and we'll recommend you more!

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

An in-memory database is a database that is kept in the main memory (RAM) of a computer and controlled by an in-memory database management system. When analyzing information in an in-memory database, only the RAM is used.

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

What is PageRank algorithm? How can it be used in various graph database use cases? How to use it in Memgraph? If these questions are keeping you up at night, here is a blog post that will finally put your mind at ease.

Get valuable insights into the world of community detection algorithms and their various applications in solving real-world problems in a wide range of use cases. By exploring the underlying structure of networks, patterns and anomalies, community detection algorithms can help you improve the efficiency and effectiveness of your systems and processes

Betweenness centrality is one of many measures you can get from performing a centrality analysis of your data. It identifies important entities in your network that are actually a vulnerability and can bring your processes to a standstill. Dive deeper into this important metric and how it can be used in various use cases.

With the rising number of cyber-attacks followed by the massive digitalization of companies, the right tool is needed to maximize performance and prevent further attacks from happening. We explain why graph databases offer a perfect choice in cybersecurity use cases and why they make your business more secure.

The number one reason for creating a knowledge graph is to find the knowledge not visible at first glance. The simplest way to discover new knowledge using a graph database is by matching patterns. Finding new patterns can help you discover fraudulent activities or discover alternative action for guaranteed success.

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.

Building real-time analytical applications require capable infrastructure. Picking the right software infrastructure components can take time and effort. When it comes to graph databases, find out why Memgraph is a fast and powerful real-time graph database.

One issue many companies face today is that they have a lot of siloed data, making it difficult to draw conclusions or reason about the processes that drive their business. By using graph technology, it is easy to create knowledge graphs and use this data to gain insights and make informed decisions.

Although networks are an easy concepts to understand, they are poorly managed in many various industries. Learn how graphs can help scale your network topologies and draw conclusions crucial for your business

A lot of companies today have massive amounts of siloed data just sitting there and not being used. No information or knowledge was gained, and no conclusions were made. For the data to be useful, it needs to be interconnected and shaped into a knowledge graph that will produce value for the company. Read how graphs can help!

This blog post deals with solving fraud detection problems with graph machine learning. Learn how to load data, training and plot to find out who did it! It’s elementary, my dear reader.

The new version of Memgraph’s open-source graph extension library, MAGE, now supports node classification and link prediction algorithms. Install the new version of MAGE if you would like to write custom algorithms faster by using the C++ API, need the igraph algorithms or k-means clustering.

Graph Neural Networks can be used for a variety of applications but do you know what it takes to create a great recommendation system? Dive deep into the math of GNNs, implement a link prediction module and show everyone how stunning graph machine learning can be!

Data lineage helps you make informed decisions that reduce costs, streamline operations and power innovation. Discover how stream tech helps with automatically mapping data lineage, and learn how Memgraph integrates with event streaming platforms.

Find out how to organize data into a graph. Model vehicle insurance claims, incidents, policies and more while preserving the interconnected nature of individual insurance cases.

For every problem in the energy management system, there is a graph algorithm that can point you in the right direction! Here is an overview of the most useful graph algorithms for highlighting weak links, high-risk nodes and many more.

It’s true every recommendation engine requires a performant database to analyze the data and provide the recommendation, but why exactly does Memgraph stand out? Easy - C++, in-memory, real-time analytics! Three things to change the recommendation game.

When you notice your traditional IAM system no longer provides adequate analysis and decision making is getting harder as your company grows because you always have to pick up the slack manually, it’s high-time you turn your attention to graphs. They have everything you need - high performance, flexibility and scalability.

The GDPR has placed high demands on organizations doing business in the European Union, mainly focused on how personal data is collected and processed. However, this does not mean it can’t be business as usual again. Find out why graph databases are the best way to achieve GDPR compliance and how they get it done.

With power being the most powerful asset, it’s still managed by inadequate tools and systems based on tabular data. Good for aggregations and mathematical operations but terrible for actually managing large-scale, highly connected dynamic systems. Luckily, graphs can regain control over energy systems and topologies, and help save millions.

If your data is trapped inside tables and you can’t seem to get satisfying answers to questions that would enhance your business, it’s time to switch to graph databases. Here are three main reasons why!

The world has changed a lot in the past couple of years, and it’s no different for business organizations. More and more businesses no longer have strict hierarchical organizations and people often change teams and projects they work on and resources they need. It is no wonder that if the IAM systems also don’t change, they will no longer be helpful in supporting the organization. Switching to graphs presents a change the IAM systems desperately need.

You no longer need to rely on manually inspecting data lineage before making changes to your organization’s data landscape. Find out how to get insights with Memgraph’s analytics so that you can move on to impact analysis, data migration, or upgrading your data infrastructure!

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.

Complex JOINs necessary for tracking data lineage with relational DBs drag down the speed of analyzing and visualizing the lineage and pinpointing issues and solutions. That is why graph technology is perfect to model and manage data lineage! Not convinced? Read the post to find out more.

Sometimes choosing graphs solutions isn’t the only step you can take to mitigate risks. In case fraudsters foolishly think they can outsmart an analytics team, they probably haven’t come across their new best friend - fraud detection systems enhanced with machine learning models.

Did you ever fall down some bottomless pit of bad data modeling? Our inter Adrian sure did, but he learned a lot from it - how to recognize the pitfalls and how to avoid them in the future! Hope his experience helps you… but let’s be honest, we never really learn from other people’s mistakes, so if you fall be sure to yell for help!

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.

Learn what is Docker Compose and how it can help you in the application development process.

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.

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

Real-time process optimization and supply chain scheduling are just some of the use cases for graph technology in the chemical industry. Read more about graph use cases in the production of industrial chemicals and how Memgraph helps.

In today’s era of highly developed information systems, one of the key tasks is to seek out the shortest path in the network – from the beginning point to the endpoint.

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.

Choose to store python objects partially into an in-memory Graph Database and into an SQLite database on disk.

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

The holidays are upon us, and we thought that it might be useful to compile a list of graph-related articles for you.

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

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

Network analysis is rapidly gaining popularity throughout different industries. Find out why!

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

How to build a Python web application for visualizing a Social Network Graph in Python with Docker, Flask and D3.js

Learn how graph databases can offer powerful data modeling and analysis capabilities your business can leverage to easily model real-world complex systems and answer challenging questions.

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