With your Digital Transformation in full swing it’s time to transform the way you look at your systems and services. With the speed of DevOps you need your Monitoring to be faster, more agile, and more accurate. You can’t afford your systems to be down. Its time to look at monitoring from a different angle. Let’s explore looking from the top down rather than the bottom up. For more information, please reach out to Craig Haessig. CraigH@mobiuspartners.com
What is observability and how is it different from traditional monitoring? How do we effectively monitor and debug complex, elastic microservice architectures? In this interactive discussion, we’ll answer these questions. We’ll also introduce the idea of an “observability pipeline” as a way to empower teams following DevOps practices. Lastly, we’ll demo cloud-native observability tools that fit this “observability pipeline” model, including Fluentd, OpenTracing, and Jaeger.
Is your company built on software? How do you know if your customer's experience is slow and sucks? How do you debug slowness or troubleshoot an incident? Observability! David Mitchell, VP of Engineering at Datadog will talk to use about Observability, why it's important, what it is and how Datadog helps reduce toil in your environment.
GDG Cloud Southlake #13
Can you understand what’s happening inside your code and system, simply by asking questions using your tools? Can you answer any new question you think of, or only the ones you prepared for?
How deeply can you understand what is happening inside your application? In modern, microservices-based applications, it’s critical to have end-to-end observability of each component and the communications between them in order to quickly identify and debug issues. In this session, we show how to have the necessary instrumentation and how to use the data you collect to have a better grasp of your production environment. On AWS, CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services. With AWS X-Ray, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. X-Ray provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. AWS App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high-availability for your applications.
What is observability and how is it different from traditional monitoring? How do we effectively monitor and debug complex, elastic microservice architectures? In this interactive discussion, we’ll answer these questions. We’ll also introduce the idea of an “observability pipeline” as a way to empower teams following DevOps practices. Lastly, we’ll demo cloud-native observability tools that fit this “observability pipeline” model, including Fluentd, OpenTracing, and Jaeger.
Is your company built on software? How do you know if your customer's experience is slow and sucks? How do you debug slowness or troubleshoot an incident? Observability! David Mitchell, VP of Engineering at Datadog will talk to use about Observability, why it's important, what it is and how Datadog helps reduce toil in your environment.
GDG Cloud Southlake #13
Can you understand what’s happening inside your code and system, simply by asking questions using your tools? Can you answer any new question you think of, or only the ones you prepared for?
How deeply can you understand what is happening inside your application? In modern, microservices-based applications, it’s critical to have end-to-end observability of each component and the communications between them in order to quickly identify and debug issues. In this session, we show how to have the necessary instrumentation and how to use the data you collect to have a better grasp of your production environment. On AWS, CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services. With AWS X-Ray, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. X-Ray provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. AWS App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high-availability for your applications.
Container environments make it easy to deploy hundreds of microservices in today’s infrastructures. Monitoring thousands of metrics efficiently introduces new challenges to not lose insight, avoid alert fatigue and maintain a high development velocity. In this talk I’ll present an overview of important metrics including the 4 golden signals, discuss strategies to organize alerting efficiently, give insight into SoundCloud’s monitoring history and highlight a few success and failure stories.
Whether you’re an enterprise migrating to cloud native or born in the cloud, most of today’s APM and Observability tools don’t support how your engineers and DevOps teams need to develop, deploy, and support their software. Observability needs to shift left and reflect the modern way companies organize their development teams and their vital interdependencies.
Chronosphere is the only vendor addressing the unique requirements for observability in a cloud native world. Join this webinar to learn:
- What cloud native observability is and how it is different from the promises made by traditional cloud APM and observability vendors
- How to use cloud native observability to do more “Dev” and less “Ops” so you can dramatically improve developer and engineer workflows and productivity
- How to make on-call shifts less stressful so your engineers aren’t getting burned out
How to Move from Monitoring to Observability, On-Premises and in a Multi-Clou...Splunk
With the acceleration of customer and business demands, site reliability engineers and IT Ops analysts now require operational visibility into their entire architecture, something that traditional APM tools, dev logging tools, and SRE tools aren’t equipped to provide. Observability enables you to inspect and understand your IT stack on premises and in the cloud(s); It’s no longer about whether your system works (monitoring), but being able to task why it is not working? (Observability). This presentation will outline key steps to take to move from monitoring to observability.
Observability has emerged as one of the hottest topics on the DevOps landscape. Organizations seek to improve visibility into their cloud infrastructure and applications and identify production issues that may negatively impact #customerexperience.
➡️ But what are some of the best practices for scaling observability for modernapplications?
➡️ What challenges are #cloudplatforms facing?
Explore how to overcome the challenges and unlock speed, observability, and automation across your DevOps lifecycle.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
More Than Monitoring: How Observability Takes You From Firefighting to Fire P...DevOps.com
For some, observability is just a hollow rebranding of monitoring, for others it’s monitoring on steroids. But what if we told you observability is the new way to find out why—not just if—your distributed system or application isn’t working as expected? Today, we see that traditional monitoring approaches can fall short if a system or application doesn’t adequately externalize its state.
This is truer as workloads move into the cloud and leverage ephemeral technologies, such as microservices and containers. To reach observability, IT and DevOps teams need to correlate different sources from logs, metrics, traces, events and more. This becomes even more challenging when defining the online revenue impact of a failed container—after all, this is what really matters to the business.
This webinar will cover:
The differences between observability and monitoring
Why it is a bigger challenge in a multicloud and containerized world
How observability results in less firefighting and more fire prevention
How new platforms can help gain observability (on premises and in the cloud) for containers, microservices and even SAP or mainframes
With Instana the "Classic" Observability is not the end of the line. Find out what Observability means and how it can help DevOps, Developers, SREs day-by-day.
In this session we’ll leave the need for performance a foregone conclusion and take a whirlwind tour through the complexity of modern Internet architectures. The complexities lead to evil optimization problems and significant challenges troubleshooting production issues to a speedy and successful end.
Starting with the simple facts that you can’t fix what you can’t see and you can’t improve what you can’t measure, we’ll discuss what needs monitoring and why. We’ll talk about unlikely allies in the fight for time and budget to instrument systems, applications and processes for observability.
You’ll leave the session with a better understanding of what it looks like to troubleshoot the storm of a malfunctioning large architecture and some tools and techniques you can use to not be swallowed by the Kraken.
- Discuss the role of Observability (Logging; Tracing; and Metric) in modern architecture.
- How to implement observability in Golang using OpenCensus.
- The 4 golden signals when designing the metrics.
- How to apply observability into the process.
Dashboards are useless. Open YouTube if you want to watch something. What benefits could automation of streaming KPI metrics bring to your business, and what pitfalls and concerns are to be expected? From Time Series analysis approach to building distributed streaming data pipeline.
Container environments make it easy to deploy hundreds of microservices in today’s infrastructures. Monitoring thousands of metrics efficiently introduces new challenges to not lose insight, avoid alert fatigue and maintain a high development velocity. In this talk I’ll present an overview of important metrics including the 4 golden signals, discuss strategies to organize alerting efficiently, give insight into SoundCloud’s monitoring history and highlight a few success and failure stories.
Whether you’re an enterprise migrating to cloud native or born in the cloud, most of today’s APM and Observability tools don’t support how your engineers and DevOps teams need to develop, deploy, and support their software. Observability needs to shift left and reflect the modern way companies organize their development teams and their vital interdependencies.
Chronosphere is the only vendor addressing the unique requirements for observability in a cloud native world. Join this webinar to learn:
- What cloud native observability is and how it is different from the promises made by traditional cloud APM and observability vendors
- How to use cloud native observability to do more “Dev” and less “Ops” so you can dramatically improve developer and engineer workflows and productivity
- How to make on-call shifts less stressful so your engineers aren’t getting burned out
How to Move from Monitoring to Observability, On-Premises and in a Multi-Clou...Splunk
With the acceleration of customer and business demands, site reliability engineers and IT Ops analysts now require operational visibility into their entire architecture, something that traditional APM tools, dev logging tools, and SRE tools aren’t equipped to provide. Observability enables you to inspect and understand your IT stack on premises and in the cloud(s); It’s no longer about whether your system works (monitoring), but being able to task why it is not working? (Observability). This presentation will outline key steps to take to move from monitoring to observability.
Observability has emerged as one of the hottest topics on the DevOps landscape. Organizations seek to improve visibility into their cloud infrastructure and applications and identify production issues that may negatively impact #customerexperience.
➡️ But what are some of the best practices for scaling observability for modernapplications?
➡️ What challenges are #cloudplatforms facing?
Explore how to overcome the challenges and unlock speed, observability, and automation across your DevOps lifecycle.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
More Than Monitoring: How Observability Takes You From Firefighting to Fire P...DevOps.com
For some, observability is just a hollow rebranding of monitoring, for others it’s monitoring on steroids. But what if we told you observability is the new way to find out why—not just if—your distributed system or application isn’t working as expected? Today, we see that traditional monitoring approaches can fall short if a system or application doesn’t adequately externalize its state.
This is truer as workloads move into the cloud and leverage ephemeral technologies, such as microservices and containers. To reach observability, IT and DevOps teams need to correlate different sources from logs, metrics, traces, events and more. This becomes even more challenging when defining the online revenue impact of a failed container—after all, this is what really matters to the business.
This webinar will cover:
The differences between observability and monitoring
Why it is a bigger challenge in a multicloud and containerized world
How observability results in less firefighting and more fire prevention
How new platforms can help gain observability (on premises and in the cloud) for containers, microservices and even SAP or mainframes
With Instana the "Classic" Observability is not the end of the line. Find out what Observability means and how it can help DevOps, Developers, SREs day-by-day.
In this session we’ll leave the need for performance a foregone conclusion and take a whirlwind tour through the complexity of modern Internet architectures. The complexities lead to evil optimization problems and significant challenges troubleshooting production issues to a speedy and successful end.
Starting with the simple facts that you can’t fix what you can’t see and you can’t improve what you can’t measure, we’ll discuss what needs monitoring and why. We’ll talk about unlikely allies in the fight for time and budget to instrument systems, applications and processes for observability.
You’ll leave the session with a better understanding of what it looks like to troubleshoot the storm of a malfunctioning large architecture and some tools and techniques you can use to not be swallowed by the Kraken.
- Discuss the role of Observability (Logging; Tracing; and Metric) in modern architecture.
- How to implement observability in Golang using OpenCensus.
- The 4 golden signals when designing the metrics.
- How to apply observability into the process.
Dashboards are useless. Open YouTube if you want to watch something. What benefits could automation of streaming KPI metrics bring to your business, and what pitfalls and concerns are to be expected? From Time Series analysis approach to building distributed streaming data pipeline.
Full Docu IT Thesis Project In Computerized Inventory System In Brother Burg...JON ICK BOGUAT
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This is my Full Document for my Thesis Project Last 2014
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ERP integrates business of an organization through a centralized database. The organizational data and transaction data are stored in the database. This data is a rich source of information. There are many software tools that would process the data and discover useful patterns. These techniques are referred to as data mining. The data from an ERP system may not be directly usable by data mining tools. The data may have to be pre-processed and made ready for data mining. A data warehouse is created from the ERP data that makes the data ready for data mining. An organization needs to interact with their suppliers for obtaining the raw material or semi-finished goods. They also need to interact with their retailers and dealers. These interactions may happen using EDI technology. Supply chain management (SCM) refers to managing suppliers and retailers. Customers are the reason why a business exists. The focus has changed from providing customer a product to providing a service built around the product. Customer relationship management (CRM) is the technology that helps an organization to manage its customers. CRM and SCM both integrate with ERP system and are collectively referred to as ERP-II.
The Tableau Experience Kaunas - TOC Sales and Marketing prezentacijaBaltimax
2017 m. spalio 18 d. Kaune vykusiame seminare „The Tableau Experience“ UAB „TOC Sales and Marketing“ atstovas Mindaugas Voldemaras ir Linas Sutkaitis dalinosi savo patirtimi naudojantis „Tableau“ verslo analitikos sprendimu.
Data Warehouses & Deployment By Ankita dubeyAnkita Dubey
This document contains the notes about data warehouses and life cycle for data warehouse deployment project. This can be useful for students or working professionals to gain the basic knowledge about Data warehouses.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Senior Solutions Architect
13 Years of Experience in IT
4 Years at Mobius Partners
Live in San Antonio with my wife and 6 daughters
Yes, 6 Daughters
Sports Nut and Superhero Nerd and a Meme Hoarder
Craig Haessig - Click here for LinkedIn or Craigh@mobiuspartners.com.
About me
3. Team in Operations
1 – 10 team members
A variety of tools that no one really likes
Very reactive
Added after and outage
Siloed
Rarely helps find root cause
Monitoring – The Old Way
4. A New Tool is Not the Answer
There is no Magic Pill
It requires a change in Culture
It requires a change in Processes
It requires a change in Philosophy
6. It is not a new term
It comes from System Control Theory
Observability Definition from Control Theory
A measure of how well the internal states of a system can be inferred
from knowledge of its external outputs
What is Observability?
8. Being able to understand a systems inner working and state by
measuring its external behaviors
A measure of how well we can understand a system from the work
it does
Its not a new word for monitoring and doesn’t replace monitoring
Observability provides deeper insights to help you find the WHY
A “digital exhaust”
What is Observability?
9. An observable system is one that exposes enough data about itself
so that generating information (finding answers to questions yet to
be formulated) and easily accessing this information becomes
simple. – Cindy Sridharan
What is Observability?
10. A Culture of Observability will be a more affective than any tool
Tools will not magically “give you” observability
How much does your company value the ability to inspect and
understand your systems, workloads and behavior?
Culture of Observability
12. Monitoring Observability
Tells you whether the system works Lets you ask why It is not working
A collection of metrics and logs about a
system
The dissemination of information from the
system
Failure Centric Understand system behavior
Is “the how”/Something you do Is “the goal” / Something you have
I monitor you You make yourself observable
Monitoring vs Observability
13. Black Box White Box
Monitoring from the Outside Monitoring from the Inside
Polling, Uptime, pings, etc Metrics, Logs and Traces
Status from 3rd Party Systems you rely on Systems you own and can instrument
Still Important Critical Source of Data for Observability
Types of Monitoring
15. Logs – A record of discrete events that happened over time
Plaintext – Most common
Structure – JSON – Name/Value pair
Collecting and storing these can be expensive but valuable
Pillar 1 – Logs
16. Provides insights into what is happening in a system but you need
context.
Use or Build a Logging Standards for your systems
Write out logs that are useful and clear
Store and aggregate your logs – Many tools out there to do this
Overtime reduce what you don’t need.
Logs – Becoming More Observable
17. Log Analytics tools can help you provide context
Able to search across multiple systems in near real-time
Able to look at what happen in the past and find root cause
Create trending reports
Gain insights and learn over time how your systems behave
Single source for many types of data from multiple systems
Log Analytics
19. Metrics – a set of numbers that give information about a particular
process of activity
Numeric representation of your data in a time series format
Can be leveraged against mathematical modeling and prediction
the deliver knowledge of the behavior of your systems. – Math is
FUN!
Pillar 2 - Metrics
20. Logs can be used to give you metrics. Example: Counting the
number of error codes over a period of time to give you a metric.
Overhead of Metrics generation and stores is consistent. Logs
collection can very compared to Metrics.
Apply labels to give contexts of the data.
Metrics
21. Instrument your code to collect application metrics
System metrics are not enough
Push Developers to identify the metrics we need to monitor the
systems
Lots of great libraries and tools out there to help
Don’t be afraid of collecting too much
Visualize your data – Build Beautiful Graphs
Metrics – Become More Observable
22. Traces – a representation of a service of events that encode the
end-to-end request flow through a distributed system
Gives insights into how services interact with other services
Can see what parts of the system are performing well or poorly
Helps to identify bottlenecks
Pillar 3 - Traces
23. Identify areas where you feel tracing could be beneficial
Use sampling
Be patient
Work with developers to identify how to best instrument your
codebase to start tracing
Tracing – Becoming more Observable
25. Alert fatigue is real
Engineers become numb to noisy or false alerts
Alert on things that require action
Perform automation to remedy before alerting
Alert should tell you what is wrong and why
Better Alerting
26. Utilization – The average time that the resource was busy servicing
work – Memory Utilization
Saturation – the degree to which the resource has extra work
which it cant service, often queued – CPU Run Queue Length
Errors – The count of error events
Use the USE Method
28. Identify what your systems report
Alert when end users and customers are experiencing problems
Make this data readily available
Alert on 3 – 10 metric
Keep it simple
Create your own method
29. 1. Don’t try to boil the ocean
2. Add monitoring to developers responsibility
• Those who built know what to monitor
3. View from a Service/Application POV
4. Collect data
5. Alert on only actionable events
6. Don’t forget about the business – Track Business Metrics
Building an Observable Culture
30. Monitoring is not dead
Monitoring needs to move up the stack
Developers need to own and help instrument their code
Collect all the data
Alert smarter
Observability is not just a buzz word its a Culture
Conclusion
31. Machine Learning to help identify issues earlier and identify trends
More Tools, More Data and More Confusion
Balancing Monolithic and Micro services and Serverless
More Responsibilities with ess Resources
Leverage Automation!
Transform your Culture
Future of Monitoring and Observability
32. • Monitoring in the Time of Cloud Native – Cindy Sridharan
Monitoring and Observability – Cindy Sridharan
3 Pillars of Observability - Cengiz Han
Monitoring Isn’t Observability – Baron Schwartz
Beginners Guide to Observability – Splunk.com
Observability and Instrumentation: what they are and why they matter – Fredric Paul –
New Relic Blog
Monitoring and Observability - Ernest Mueller
Use Method
Sources and References
Editor's Notes
Observability is not just failure centric, Used for debugging and normal usage, not just when something is perceived to be broken
Share Bad Logging Experience
Enriching your data
Labels, tags! Very important
Lot ot tools – Collectd, statsd, promethius, and more
Time series database
Developers add logging, need to also add metrics to their code
Promethius aggregates before it sends it to Time Series
Look up notes
KISS method
Observability is a Culture
On prem, cloud, hybrid, Contianers, VMs, etc. Its noting getting simpler to monitor