Notes from the Trenches

Release 2.25 follows the footsteps of 2.24 of focusing on daily practice. Some of the biggest features in this release are automatic ontology support for popular logging and alert tools and a managed migration & update script. In parallel, much of our time is going to working directly with everyone in the field to assist with your projects, and we're excited to bring the resulting workflows and concepts into features for everyone.

Sometimes the best feature is to improve the experience with the current ones.  Behind the scenes, we've been working much more closely with our users. Externally, release 2.24.24 comes with a bunch of tiny improvements from working with everyone. This release is for you!

Graphistry has made it to Azure! The easiest way to get started in Azure is through the Azure Marketplace, and for enterprise users, we also support Docker installation.

Of course, we still love Amazon too: The Graphistry ontology now supports CloudTrails datatypes out-of-the-box. On-premise? In 2.24.11, we also added an example of setting up an RHEL 7.6 environment for nvidia-docker-based software in addition to our existing Ubuntu guides.  

Version 2.23.4 brings a bunch to enterprise analysts and developers! Read on to learn more about those, and see full release notes at our new release notes page . 

Version 2.22.7 streamlines use with Gremlin/TinkerPop (CosmosDB, Neptune, JanusGraph, ...), helps you turn Jupyter Notebooks into dashboards, and adds more URL API parameters. Read on to learn more about those, and see full release notes at our new release notes page . 

Curious to turn your graph DB data into GPU-accelerated visual graph analytics sessions & dashboards? One-click launch on AWS now! 

Version 2.22 makes life better for both new and existing users. We're especially excited about introducing TigerGraph and SQL support, and the continued progress with the 2.0 engine. Read on to learn more about those, and see full release notes at our new release notes page . 

Curious to explore & automate your own CSV or database/API data with GPU visual graph analytics and investigation automation? One-click launch on AWS now! 

It feels likes eye-popping times for those deep into building the future of visual data experiences. With Looker exiting (-> Google for $3B), Tableau exiting (->SalesForce for $16B), and less public, Periscope & ZoomData exiting, the Graphistry team is experiencing good feelings and key reflections. One of them is... the $16B exits are just a prelude to the next $160B in opportunities.

One of the easiest and most powerful ways to empower your team is to create and embed automated Investigation Templates (docs). Analysts don't need to know what templates are available ahead of time: instead, they get Graphistry links embedded into their existing workflows. For example, you can augment alert emails with targeted investigation links, or add contextual links to any web dashboard. This is great for tasks like recommending particular kinds of investigations, and putting contextual entity views in reach at the right time.

The video tutorial walks through creating an investigation template and embedding links into Splunk as contextual Workflow Actions:

Next steps & further reading

• Docs for investigation template links
• Graphistry 2.0  + Launch in your AWS

Graphistry makes it easy to explore the hidden connections in any CSV or flat file by automatically exposing the underlying graph. This tutorial walks through the CSV Mini-App notebook that comes with Graphistry and applies it to visualizing the recent Implant Files medical device recalls database by the ICIJ.

Screenshot: ICIJ's The Implant Files visualized live with Graphistry - The pandemic of 70,000+ medical device recalls

1. Setup

• Download the recall data from the ICIJ and unzip to get the event data, events-1551346702.csv 
• Launch Graphistry!

2. Go through the video tutorial!

• Launch and clone the CSV Upload Mini-App notebook, and rename to "icij_implants.ipynb"
• Follow the instructions in the notebook
• Settings used for each section:
  
  • Upload:
    
    file_path = './events-1551346702.csv'
    
    
  • Data cleaning:
    
    hits = pd.DataFrame([[c, len(df[c].unique())] for c in df.columns], columns=['col', 'num_uniq']).sort_values('num_uniq')
    
    skip_nodes = ['icij_notes', 'determined_cause', 'action_classification', 'icij_notes', 'country', 'status', 'source']
    nodes = [x for x in list(hits.query('num_uniq > 10 & num_uniq < 9288')['col']) if not x in skip_nodes]
    
    df = df_orig.query('country == "USA"')
    
    
  • Plotting:
    
    mode = 'B'
    max_rows = 50000
    node_cols = nodes
    categories = { }
    
    

Next steps & further reading

• Pandas pd.read_csv() : Load most files
• Graphistry's graphistry.hypergraph(): Turn any Pandas dataframe into a live graph visualization
• Graphistry in Jupyter notebooks + UI guide 
• ICIJ's Implant Files investigation (+ the data!)
• Resulting use of CSV Mini-App notebook
• Graphistry 2.0  + Launch in your AWS