Module 1: Introducing NewRelic Logs

Module 2: NewRelic Components

Module 3: Installing NewRelic

Module 4: Getting Data In

Module 5: Basic Search

Prerequisite

Basic Windows Administration
Basic Linux Administration
Windows Environment Linux
Environment

“Well there’s usually enough money in the bank, so who needs to plan and budget?”

Log = record related to whatever activities occurring on an information system

Also: alert, "event", alarm, message, record, etc

- IDS

- Firewalls/IPS

- Anti-maleware

- Proxies

- Network Infrastructure

- Servers

- Databases

- Application

Machine Data & Log

Data generated by machines, computer processing, applications and sensor data.

• Production Monitoring and Debugging
• Resource Usage
• HTTP Errors
• Slow Queries
• Rogue Automated Robots
• Security Issues

• Tracking Your Site’s/Platform’s Visitors
• Situational awareness and new threat discovery
• Getting more value out of network and security infrastructures
• Extracting what is really actionable automatically
• Measuring security (metrics, trends, etc)
• Compliance an regulations
• Incident response (last, but not least)

Time

Source

Destination

Protocol

Port(s)

User name

Event/Attach type

Bytes exchanged

Pattern detection and recognition: to filter messages based on a pattern book. Normalization: to convert different log elements such as dates to the same format. Tagging and classification: to tag log elements with keywords and categorize them into a number of classes so you can filter and adjust the way you display your data. Correlation analysis: to collate logs from different sources and systems and sort meaningful messages that pertain to a particular event. This helps discover connections between data not visible in a single log. For instance, if you have just experienced a cyber attack, correlation analysis would put together the logs generated by your servers, firewalls, network devices, & other sources, and find the messages that are relevant to that particular attack.
Artificial ignorance: a machine learning process to identify and “ignore” log entries that are not useful and detect anomalies.

1. Sending logs manually
2. Send logs from a file

Datadog Course

Datadog APIs Key Tokens