Elastic at Procter & Gamble: A Network Story

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Learn how the Elastic Stack helped Procter & Gamble achieve a greater understanding of their data, as well as introducing observability to their toolkit to help them be more proactive and provide better services.

Technologie

1
Andrea Fuggetta
Sr. Software Engineer, Procter & Gamble
@andreafuggetta
Elastic at Procter &
Gamble:
A Network Story

2
Agenda
What are we talking about?
Who we are1
Challenges3
The road so far4
What’s next?5
Problem statement2

3
Who we are
• Founded in 1837 (180+ years)
• Superior quality products
• More than 180 countries
• http://www.pg.com/
Making every day more than ordinary

4
Who we are (cont’d)
• Andrea Fuggetta
• Cincinnati (OH)
• Sr. Software Engineer
• Network Automation
Making every day more than ordinary

5
Problem statement
• Insourcing
• Where is our data?
• What data do we have?
• What are my processes and use cases?
• How do we aggregate the data?
• How do we leverage the data?
• Where is our data!?
Where did we start?

6
I have not failed. I've just found
10,000 ways that won't work
Thomas Edison

7
Problem statement
• So many choices
• Trial and error
• What is Elasticsearch?
• Demo sessions
• First visualizations
• Interest from customers
Where to start?

8
It works on my machine
Every programmer out there

9
Challenges
• Find the processes that govern your data
• Find the people responsible for them
• Find the technology to support the business and use cases
Where to start?

10
Challenges
• How much data do we expect?
‒ Do you know all our data?
‒ Throughput
• Infrastructure
‒ Cloud or DC
‒ Compute power
‒ Memory
‒ Storage
• What product?
‒ Single server
‒ Local cluster
‒ Elastic Cloud
‒ ECE
Scaling – Hosting – Product

11
Challenges
• Define the data sources
‒ Network metrics
• Data flow
‒ 500MB/day
• Infrastructure
‒ Cloud
• What product?
‒ 3 node cluster
‒ Installed manually on VMs
Solutions?

12
“The important
thing is to
never stop
questioning…”
Albert Einstein

13
The road so far
• Define the data sources
‒ Network devices’ syslogs and metrics
• Data flow
‒ 500MB/day ~6TB/day
• Infrastructure
‒ Cloud
• What product?
‒ Elastic Cloud Enterprise (ECE)
• How?
Current state

15
The road so far
Automation
GitHub
Azure
DevOps
Terraform
Ansible
Elastic ECE
Logstash

16
VMs
Supporting ECE
and Logstash
pipelines.
Hot/Warm/Cold
lifecycle
Some numbers
Terabytes
Data coming from
network devices
i.e. Firewall
syslogs
Teams
Currently
leveraging the
solution
42 6 6

17
Results
• Prevented downtime and potential issues
• Increased knowledge of our data
• One destination for logs and metrics
• Easier troubleshooting and forensics
• Increased scalability and mobility
• All in less than 1 year
Long road ahead

18
Results
• Move infrastructure from Azure to AWS
‒ Load balancers
‒ Kafka-like queue (Kinesis)
‒ Virtual Machines
‒ Storage
‒ Monitoring
‒ Installing and configuring software (ECE, Logstash)
• Half day
Examples

19
What’s next?
• More customers – more data
‒ Information Security (SIEM)
‒ Data Science (Search, aggregation, analysis)
• ML
‒ Anomalies detection
• Cloud data and logs
‒ Function beats
‒ Custom ingestion pipelines
• Alerts and actions
‒ Anomalies trigger alerts and scripts to self-heal
• Canvas
‒ Executive views
‒ Hallway monitors
Near future

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Elastic at Procter & Gamble: A Network Story

1. 1 Andrea Fuggetta Sr. Software Engineer, Procter & Gamble @andreafuggetta Elastic at Procter & Gamble: A Network Story

2. 2 Agenda What are we talking about? Who we are1 Challenges3 The road so far4 What’s next?5 Problem statement2

3. 3 Who we are • Founded in 1837 (180+ years) • Superior quality products • More than 180 countries • http://www.pg.com/ Making every day more than ordinary

4. 4 Who we are (cont’d) • Andrea Fuggetta • Cincinnati (OH) • Sr. Software Engineer • Network Automation Making every day more than ordinary

5. 5 Problem statement • Insourcing • Where is our data? • What data do we have? • What are my processes and use cases? • How do we aggregate the data? • How do we leverage the data? • Where is our data!? Where did we start?

6. 6 I have not failed. I've just found 10,000 ways that won't work Thomas Edison

7. 7 Problem statement • So many choices • Trial and error • What is Elasticsearch? • Demo sessions • First visualizations • Interest from customers Where to start?

8. 8 It works on my machine Every programmer out there

9. 9 Challenges • Find the processes that govern your data • Find the people responsible for them • Find the technology to support the business and use cases Where to start?

10. 10 Challenges • How much data do we expect? ‒ Do you know all our data? ‒ Throughput • Infrastructure ‒ Cloud or DC ‒ Compute power ‒ Memory ‒ Storage • What product? ‒ Single server ‒ Local cluster ‒ Elastic Cloud ‒ ECE Scaling – Hosting – Product

11. 11 Challenges • Define the data sources ‒ Network metrics • Data flow ‒ 500MB/day • Infrastructure ‒ Cloud • What product? ‒ 3 node cluster ‒ Installed manually on VMs Solutions?

12. 12 “The important thing is to never stop questioning…” Albert Einstein

13. 13 The road so far • Define the data sources ‒ Network devices’ syslogs and metrics • Data flow ‒ 500MB/day ~6TB/day • Infrastructure ‒ Cloud • What product? ‒ Elastic Cloud Enterprise (ECE) • How? Current state

14. 14 The road so far Architecture

15. 15 The road so far Automation GitHub Azure DevOps Terraform Ansible Elastic ECE Logstash

16. 16 VMs Supporting ECE and Logstash pipelines. Hot/Warm/Cold lifecycle Some numbers Terabytes Data coming from network devices i.e. Firewall syslogs Teams Currently leveraging the solution 42 6 6

17. 17 Results • Prevented downtime and potential issues • Increased knowledge of our data • One destination for logs and metrics • Easier troubleshooting and forensics • Increased scalability and mobility • All in less than 1 year Long road ahead

18. 18 Results • Move infrastructure from Azure to AWS ‒ Load balancers ‒ Kafka-like queue (Kinesis) ‒ Virtual Machines ‒ Storage ‒ Monitoring ‒ Installing and configuring software (ECE, Logstash) • Half day Examples

19. 19 What’s next? • More customers – more data ‒ Information Security (SIEM) ‒ Data Science (Search, aggregation, analysis) • ML ‒ Anomalies detection • Cloud data and logs ‒ Function beats ‒ Custom ingestion pipelines • Alerts and actions ‒ Anomalies trigger alerts and scripts to self-heal • Canvas ‒ Executive views ‒ Hallway monitors Near future

20. 20 Thank you

Elastic at Procter & Gamble: A Network Story

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