Ever since Memgraph started, it’s been on a mission to change the way the world solves problems through the power of the graphs. These problems come primarily from the inability to get real-time insights from data that drive decisions due to outdated tools. The old generation of tools cannot uncover patterns and similarities, connect and match distinct entities to one another or analyze highly connected datasets at scale.
In order to tackle this, we built the product and then went open-source so all developers could leverage their streaming data and get deep insights. It was available locally and on-prem with the Community Edition.
So, if you have a large dataset of interconnected, networked data, you have the opportunity to gain insights easily and fast. Thanks to a number of graph algorithms readily available and preoptimised, it doesn’t matter if you obtain insights by understanding relationships in the data, detecting communities of similar characteristics or measuring influence levels. It all works. Such insights will allow you to build applications and systems to achieve better, faster and timely decision making around revenue, cost and risk within various use cases and industries.
With this release of Memgraph Cloud and Memgraph Lab, you will be able to accelerate development and have a smoother experience than before. If you are eager to get started, check out the tutorial on how to build a book recommendation engine from Amazon book dataset. Otherwise, let’s dive in!
Run Graph Algorithms first to get insights …
For a number of years now, graph algorithms were hidden behind closed doors and generally inaccessible except for the selected few in big tech companies. These companies had the knowledge, capabilities and tech resources to build and deploy them and leverage those insights for their narrow use cases. The broader data ecosystems did not have much use or benefit from them up until recently.
Memgraph changes that and makes graph algorithms as simple as Binary Search, available to every developer and super easy to run through MAGE, Memgraph’s graph algorithm library. To run PageRank, it’s like:
CALL pagerank.get(). It’s that easy.
Graph algorithms in MAGE are specifically optimised for Memgraph, making the best of its performant graph database technology. Even though MAGE consists of static and machine learning graph algorithms, the dynamic graph algorithms are where the rubber really meets the road and why they make a difference for the streaming data.
As data from streams gets ingested, insights are automatically and continuously recomputed and updated in an optimised fashion in real-time. You don’t miss a thing and always have the latest insight without delay with each datapoint included. Therefore, developers can now get algorithmically driven insights.
MAGE consists of multiple algorithms like PageRank, Community Detection and others. PageRank makes measuring influence possible and is used for drug discovery in pharmaceutical industry and rankings of search results in Google among others. For example, you can build a real-time book recommendation engine with PageRank. On the other side, Community Detection enables detecting communities of similar behaviours and characteristics and is used for fraud detection in the finance and insurance industry. These algorithms provide insights developers need.
On Real-Time Data Streams …
Thousands of mission-critical applications were built on top of real-time data streams so far and thousandsare yet to be built. Real-time applications are here to stay. All these applications are only valuable if they offer unique and different insights into the data they were built upon. For example, analyzing GitHub commits in real-time or analyzing streaming MovieLens dataset is unique. Memgraph enables developers to analyse their data streams and get insights from them. Graph algorithms offer just that - unique and different insights.
There are a number of streaming technologies available and Memgraph supports them all - RedPanda, Kafka and Pulsar streaming sources are all supported and it is simple to connect to the clusters. But, creating a streaming connection is not the only thing you can do, you can also stop it, start it, drop it and check if it works as intended.
On the other side, if you have static data, that is ok as well - we don’t discriminate. You can still easily import your
In the Cloud and with Memgraph Lab!
Similar to the real-time data streams, Cloud is the future. It provides a headache-free way for application development, without additional installation and set-up. With Memgraph Cloud, you can create a project that uses up to 2 GB of RAM and give it a go for a two week trial.
But that’s not all, you can connect to such an instance with Memgraph Lab, which exposes all the Memgraph’s on-prem magic behind an easy to use graphical user interface and becomes a central place to develop graph-based applications.
Memgraph Lab allows you to to sign in and connect to a data stream. It can be your own data stream or you can select an Awesome Data Stream. You can then select and run a graph algorithm and receive insights on the other side. As it all happens in the browser (or through a desktop app), this process has never been this easy. Memgraph Lab acts as an IGE - Integrated Graph Environment, where each developer can solve application problems without any code leaving Memgraph.
Visualisations are significantly improved in the Lab with graph rendering components using D3. Query execution is on another level as well, and up to 1000x faster than before, which allows developers to get those insights fast and smooth!
Get started today!
Accelerate your app development by getting the insights from your streaming data fast and easy through the Cloud and without additional hassle. Through Lab, you’ll be able to ingest data, run graph algorithms and actually enjoy running the analysis. Get started today with a two week trial of Memgraph Cloud, check a how to get started video, explore at least one Awesome Data Stream or dive straight into the Artblocks example!