As a vast number of use cases in cybersecurity involves network-like representation of data, we outline why Memgraph is the best graph database for you in terms of performance, analytics and visualizations.
People tend to update versions of their code project dependencies without inspecting security impacts on their code. In this article, we use Memgraph to analyze Python package vulnerabilities when updating dependencies and provide you with a performant solution using known and reported vulnerabilities.
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.
Find out how you can minimize decision-making risks when dealing with networks by using Memgraph as the one-and-only tool for a complete analysis.
Optimizing a supply chain network can get really messy if you can’t identify dependant products, correctly schedule processes and find critical points in the pipeline. With Memgraph, you can accelerate your supply chain pipeline and build a complete analysis tool to increase the shipments of your goods.
Making decisions that point your business in the right direction is much easier with knowledge graphs. But, to create a knowledge graph, you need to gather all the data scattered in different silos, analyze the current connections between data points and discover new connections. It’s a complex task, but graph databases, such as Memgraph, make it a manageable one.
As networks consist of highly connected data, with Memgraph’s in-memory storage you can analyze network topologies quickly to gain insights from static or real-time data. Discover critical points in the network or component dependencies, optimize resources and run what-if scenarios, then present those findings visually to extract every last bit of information.
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.
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.
The data lineage graph is the single source of truth about your organization’s data. Discover how Memgraph can competently handle this use case with its optimized architecture, power data insights and connect to other software.
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.
Find vulnerabilities and security issues, or perform any other data analysis in your Identity and Access Management system with Memgraph, and ensure you are running a smooth operation.
If you require an energy management system that is scalable, fault-tolerant, and performant, Memgraph is the go-to solution! Analyze highly connected power grids or gas pipelines to make meaningful decisions and improve the impact on your business, the people and the environment around you.
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.
Are you reluctant to switch from a relational database to a graph databases to explore fraud because you believe you first need to be proficient in Cypher to correctly import the data? Be rest assured - there is a Python-friendly approach available within Memgraph!
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.