How Not to Fail at Data Visualization by Using Icinga and InfluxDB

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Icinga provides an out-of-the box integration for InfluxDB. It allows you to leverage the data that is collected for monitoring purposes and store the metrics in your time series database. While collecting data that way is pretty easy, proper visualization requires more attention. Misinterpretation of data is one of the most common causes of wrong conclusions. It make us hunt ghosts during debugging sessions. There are many common pitfalls which we can avoid if we follow some rules. In this webinar, we will show some of the most common mistakes in visualizations and how to avoid them.

Technology

How not to Fail at Data
Visualization by Using Icinga and
InfluxDB
August 4th, 2020

Responsibilities Contact
Product
Partnerships
@bobapple
blerim.sheqa@icinga.com
Blerim Sheqa
CPO

Icinga Overview
Icinga Stack
Monitoring
Availability, Reliability,
Observability
Log
Management
ElasticStack, Graylog
Automation
Director, CfgMgmt-Support,
API
Metrics and
Analytics
InfluxDB, Grafana, Graphite

Icinga Overview
Key Aspects
Dynamic
Rule based configuration
Automation
Scalable
Built-in cluster
High available and distributed
Extendable
Your infrastructure, your monitoring
Integrated with your tools

• Write all metrics directly to InfluxDB
• leveraging the HTTP API
• Names and tags are fully configurable
• add data such as location, environment, OS, …
• Add thresholds optionally
• identify exceeded thresholds
• Add metadata optionally
• e.g. state, execution time, latency, …
Icinga with InfluxDB
Key Aspects

• High availability mode
• automated failover
• Secure
• with authentication and SSL
• Buffering
• custom buffering intervals and thresholds
Icinga with InfluxDB
Key Aspects

• By default data is kept forever
• Retention policy
• for housekeeping
• Continuous query
• for downsampling
Icinga with InfluxDB
Data Retention

Icinga with InfluxDB
High Availability
enable_ha = true enable_ha = true

• Proper stacking of values
• Add grid, y-axis and x-axis
• Even if it looks boring!
• Split into multiple graphs
• where applicable
Comparability
Summary

• Which questions do you want to answer?
• Single purpose dashboards
• improves the performance
• decrease complexity
• Add related information
• state changes, threshold exceedance, alerts, …
Readability
How to improve

• Quality over quantity
• Trust the data
• Know the details
• what do you collect
• at what pace
• how long is it stored
• who needs the data
Know your Data

Call for Papers is now open!
We’re looking for great speakers – submit
your speaker application today.
November 10 – 11, 2020
North America Virtual
Experience
www.influxdays.com/virtual-experience-2020/

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Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale. In this webinar, Anais Dotis-Georgiou will dive into: Requirements for rebuilding InfluxDB’s core Key product features and timeline How Apache Arrow’s ecosystem is used to meet those requirements Stick around for a demo and live Q&A

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RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics? Join this webinar as Ryan McCrary dives into: RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations Their data architecture including Kapacitor for alerting and Grafana for customized dashboards Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers

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How Not to Fail at Data Visualization by Using Icinga and InfluxDB

1. How not to Fail at Data Visualization by Using Icinga and InfluxDB August 4th, 2020

2. Responsibilities Contact Product Partnerships @bobapple blerim.sheqa@icinga.com Blerim Sheqa CPO

3. What is Icinga?

4. Icinga Overview Icinga Stack Monitoring Availability, Reliability, Observability Log Management ElasticStack, Graylog Automation Director, CfgMgmt-Support, API Metrics and Analytics InfluxDB, Grafana, Graphite

6. Icinga Overview Key Aspects Dynamic Rule based configuration Automation Scalable Built-in cluster High available and distributed Extendable Your infrastructure, your monitoring Integrated with your tools

7. Icinga with InfluxDB

8. Icinga with InfluxDB

9. • Write all metrics directly to InfluxDB • leveraging the HTTP API • Names and tags are fully configurable • add data such as location, environment, OS, … • Add thresholds optionally • identify exceeded thresholds • Add metadata optionally • e.g. state, execution time, latency, … Icinga with InfluxDB Key Aspects

10. • High availability mode • automated failover • Secure • with authentication and SSL • Buffering • custom buffering intervals and thresholds Icinga with InfluxDB Key Aspects

11. Icinga with InfluxDB

12. • By default data is kept forever • Retention policy • for housekeeping • Continuous query • for downsampling Icinga with InfluxDB Data Retention

13. Icinga with InfluxDB High Availability enable_ha = true enable_ha = true

14. Common Pitfalls in Data Visualization

15. Misinterpretation

16. Conventions

17. Follow Conventions

18. Follow Conventions

27. • Proper stacking of values • Add grid, y-axis and x-axis • Even if it looks boring! • Split into multiple graphs • where applicable Comparability Summary

28. Readability

29. Readability

30. Readability

31. • Which questions do you want to answer? • Single purpose dashboards • improves the performance • decrease complexity • Add related information • state changes, threshold exceedance, alerts, … Readability How to improve

32. Know your Data

33. • Quality over quantity • Trust the data • Know the details • what do you collect • at what pace • how long is it stored • who needs the data Know your Data

34. Q&A

35. Call for Papers is now open! We’re looking for great speakers – submit your speaker application today. November 10 – 11, 2020 North America Virtual Experience www.influxdays.com/virtual-experience-2020/

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