graph-notebook VS jupyter-memgraph-tutorials

We will now configure a Neptune graph notebook to access the cluster, so we can run queries and generate interactive visualisations. Neptune Workbench allows users to run fully managed jupyter notebook environment in Sagemaker with the latest release of the open source graph Neptune project. This has the benefit of offering in-built capabilities like visualisation of queries

This blog follows on from the setup of Neptune DB with Worldwide Events data in the first part. Here we will run some queries and investigate node relationships in the Neptune Notebook. We can use some of the magic commands available in graph notebook open sourced by AWS and available to use in the Neptune Notebook instance configured in the first part of this blog. We can configure the visualisation options available in the notebook when we execute the query, using the%graph_notebook_vis_options command. This will output a json containing the default configuration options for rendering the graphs.

AWS Graph Notebooks

You will need to download this file which contains a simple dataset of scraped tweets. To import it into Memgraph, we will first need to copy it to the Docker container where Memgraph is running. Find the CONTAINER_ID by running:

Now, I am going to show you an example of how to accomplish this. You can also take a look at this Jupyter Notebook if it's more your style.

Are you considering moving from table data to a graph database, but it seems like a complicated migration? With this short tutorial, we are going to show you how to do just that using GQLAlchemy. You will learn how to import table data from files stored in local or online storage to a Memgraph graph database. You can find the original Jupyter Notebook in our open-source GitHub repository.

GitHub repository