Demystifying Data Analytics & Visualization Make Your Data Dance

This guy? Definition & Discussion: “Big Data Hype” What is an analytic? How do we visualize Demo: of Data Analytics and Visualization Questions/Discussion Today’s Agenda 2

This Guy? 3 My Wife! Creepy Kids My Wife Made 

Its everywhere We all hear it, but what does it mean? Does it really mean anything or is it just more marketing hype? Is bigger really better? Big Data or Big Hype? 4

How many logs do we have now? Too many to count Not just on your file system, but in traffic too! Human – Human Machine – Human Machine - Machine Logs Logs Everywhere 5 Linux/Unix/Mac(BSD) Microsoft Bro Logs –Or plain Netflow generation Snort or other IDS Switches/Routers

What do you do with all this? 6

How do you decide which logs you want? –Compliance –Policy –Curiosity –Just because Normalization –On the fly (streams) –On the remote/local file system (batch) Get Them In Your Database 7

Tools for Transport: –Flume, fluentd, rsyslog, syslog-ng, sqoop, logstash Tools for Storage: –Note: Relational/Non-relational is important –mySQL, cassandra, Hadoop (HDFS), Elasticsearch Degree’s of Wholeness –ELSA, graylog2, Snare Some Free Tools To Help 8

All data is not gold You need a strategy that gets you the right data at the right time Data is Big... But So What? 9

Wikipedia Definition – “the discovery and communication of meaningful patterns in data” Defining: Analytics 10

Simple! What! A question?! I can understand that! These questions can be used to create –Metrics –Statistics –Network behaviors –These all help the application of Analytics as analytics help are used to create them. Simply a Question 11

I received an IDS alert, is there other similar behavior on my network that I did not receive an alert for? I have an IP blacklist, what hosts on my network connected to those IP addresses? Better yet, is there other similar behavior on my network to non–black-listed IP addresses? Ask Questions of Your Data 12

Unpatched Systems Misconfigured Devices File access –Rates –Personnel Visibility –Of your network –Of your hosts What Other Kinds of Insight 13

So you normalized and stored the data You’ve asked good questions of our data with analytics Now what? We visualize But how? Visualization. 14

15 Demo Time!

16 Questions? Source links in the notes on this slide

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