Graphistry 2.25.18: Log ontologies and automatic migrations

Posted by Graphistry Team on Sep 26, 2019

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.

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Topics: Release

Graphistry 2.24.24: Happy Users

Posted by Graphistry Team on Sep 10, 2019

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!

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Topics: Release

Graphistry 2.24.11: Hello, Azure and AWS!

Posted by Graphistry Team on Sep 3, 2019

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.  

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Topics: Release, RedHat, AWS, Azure

Graphistry 2.23.4: HyperNetX, data bridge, fuzzy matching, and APIs

Posted by Graphistry Team on Aug 16, 2019

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

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Topics: Release, HyperNetX, Data bridge, Fuzzy matching, API

Graphistry 2.22.7: GPU Visual Graph Analytics with Gremlin (CosmosDB, JanusGraph, AWS Neptune) and Jupyter Dashboards (Voila) with

Posted by Graphistry Team on Aug 5, 2019

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! 

LAUNCH IN YOUR AWS 

 

 

 

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Topics: Jupyter, Release, gremlin, voila, tinkerpop, cosmosdb, neptune

Graphistry 2.22: GPU viz with TigerGraph, SQL, and more!

Posted by Graphistry Team on Aug 1, 2019

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! 

LAUNCH IN YOUR AWS 

 

 

 

 

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Topics: TigerGraph, SQL, Jupyter, Release

From $16B to $160B: The 100X data future beyond SalesForce/Tableau and Google/Looker

Posted by Leo Meyerovich on Jun 10, 2019

 

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.

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Topics: Exploration, GPU, Visualization, Graph, human-in-the-loop, automation, orchestration

Threat Hunting Masterclass: Three data science notebooks for finding bad actors in your network logs

Posted by Graphistry Team on May 13, 2019

 

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Topics: Bro, threat hunting, zeek, masterclass, corelight

Tutorial: Investigation Automation Templates with Splunk

Posted by Graphistry Team on May 8, 2019

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

 

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Tutorial: Graphistry CSV Viewer Mini-App for ICIJ's "Implant Files" Medical Device Recalls

Posted by Graphistry Team on May 2, 2019

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

 

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

 

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