Telegraf is an open-source server agent designed to collect metrics from stacks, sensors, and systems — with nearly 300 inputs and outputs. Telegraf Operator makes it easy to use Telegraf for monitoring your Kubernetes workloads. It enables developers to define a common output destination for all metrics, and configure Sidecar monitoring on your application pods using annotations. With the Telegraf sidecar container added, it will collect data and start pushing the metrics to a time series database, like InfluxDB. Discover how to use the Telegraf Operator as a control center for managing individual Telegraf instances which are deployed throughout Kubernetes clusters. Find out how to use the InfluxDB and Telegraf Operator to monitor and get metrics from your Kubernetes workloads.
Join this webinar as InfluxData's Pat Gaughen and Wojciech Kocjan provide:
InfluxDB & Telegraf overview
Telegraf Operator deep-dive
Live demos of sample deployments!
Telegraf is an open-source server agent designed to collect metrics from stacks, sensors, and systems — with nearly 300 inputs and outputs. Telegraf Operator makes it easy to use Telegraf for monitoring your Kubernetes workloads. It enables developers to define a common output destination for all metrics, and configure Sidecar monitoring on your application pods using annotations. With the Telegraf sidecar container added, it will collect data and start pushing the metrics to a time series database, like InfluxDB. Discover how to use the Telegraf Operator as a control center for managing individual Telegraf instances which are deployed throughout Kubernetes clusters. Find out how to use the InfluxDB and Telegraf Operator to monitor and get metrics from your Kubernetes workloads.
Join this webinar as InfluxData's Pat Gaughen and Wojciech Kocjan provide:
InfluxDB & Telegraf overview
Telegraf Operator deep-dive
Live demos of sample deployments!
LinkedIn started its Trino journey back in 2015 and has been an active contributor in the community. We have been witnessing massive growth YoY and our workload has been exponentially growing with more than 5k unique users, processing 100s of PB, millions of queries and quadrillions of rows every week. Trino at LinkedIn is used for a diverse variety of use cases like detecting fraud and abuse, data scientists measure impact of COVID on economic and jobs landscape, engineers run ad hoc analysis to debug production issues, business analysts build robust data driven offering to help salespeople make smarter decisions, site-reliability engineers analyze internal system performances and more. In this talk, we will go through Trino's growth at LinkedIn, how it fits into our data ecosystem, some of our operating challenges and dive into a few of our use cases. We'll also talk about our learnings, contributions, and philosophy on open source and what has worked well for us.
[DockerCon 2023] Reproducible builds with BuildKit for software supply chain ...Akihiro Suda
Images maintained by a reputable organization or an individual are often considered to be trustworthy; however, it is hard to deny the possibility that they might have silently injected malicious codes that are not present in the source repo. Also, even if they have no malicious intent, their images can still be compromised on an accidental leakage of registry credentials.
The latest release of BuildKit solves this supply chain security concern with reproducible builds. Reproducible builds is a technique to ensure that a bit-for-bit identical image can be reproduced from its source code, by anybody, at any time. When multiple actors can attest to an image's reproducibility, it signifies that the image contains no code of a secret origin.
Audiences of this talk will learn how they can and how sometimes they cannot make their images reproducible to improve their trust.
Running Kubernetes in Production: A Million Ways to Crash Your Cluster - DevO...Henning Jacobs
Bootstrapping a Kubernetes cluster is easy, rolling it out to nearly 200 engineering teams and operating it at scale is a challenge. In this talk, we are presenting our approach to Kubernetes provisioning on AWS, operations and developer experience for our growing Zalando developer base.
We will walk you through our horror stories of operating 80+ clusters and share the insights we gained from incidents, failures, user reports and general observations.
Most of our learnings apply to other Kubernetes infrastructures (EKS, GKE, ..) as well.
This talk strives to reduce the audience’s unknown unknowns about running Kubernetes in production.
Prometheus monitoring from outside of Kubernetes 〜どうして我々はKubernetes上のPromet...whywaita
talked by Prometheus Tokyo Meetup #2 https://prometheus.connpass.com/event/127574/
re-upload: https://speakerdeck.com/whywaita/prometheus-monitoring-from-outside-of-kubernetes-kubernetesprometheus-prometheustokyo
OSMC 2022 | Ignite: Observability with Grafana & Prometheus for Kafka on Kube...NETWAYS
Self-managing a highly scalable distributed system with Apache Kafka® at its core is not an easy feat. That’s why operators prefer tooling such as Confluent Control Center for administering and monitoring their deployments. However, sometimes, you might also like to import monitoring data into a third-party metrics aggregation platform for service correlations, consolidated dashboards, root cause analysis, or more fine-grained alerts. If you’ve ever asked a question along these lines: Can I export JMX data from Confluent clusters to my monitoring system with minimal configuration? What if I could correlate this service’s data spike with metrics from Confluent clusters in a single UI pane? Can I configure some Grafana dashboards for Confluent clusters?
This talk will enable you on achieving the below:
Monitoring Your Event Streams: Integrating Confluent with Prometheus and Grafana (this article)
Monitoring Your Event Streams: Tutorial for Observability Into Apache Kafka Clients
Apache Spark Streaming in K8s with ArgoCD & Spark OperatorDatabricks
Over the last year, we have been moving from a batch processing jobs setup with Airflow using EC2s to a powerful & scalable setup using Airflow & Spark in K8s.
The increasing need of moving forward with all the technology changes, the new community advances, and multidisciplinary teams, forced us to design a solution where we were able to run multiple Spark versions at the same time by avoiding duplicating infrastructure and simplifying its deployment, maintenance, and development.
Video k prezentaci https://youtu.be/Hi0CSr9usn8
Dnešní zákazníci chtějí rychlé a bezchybné služby. IT týmy jsou pod tlakem efektivně tuto službu poskytovat. High level pohled na ITIL. Příklady řešení z praxe s důrazem na efektivitu a rychlost nasazení.
LinkedIn started its Trino journey back in 2015 and has been an active contributor in the community. We have been witnessing massive growth YoY and our workload has been exponentially growing with more than 5k unique users, processing 100s of PB, millions of queries and quadrillions of rows every week. Trino at LinkedIn is used for a diverse variety of use cases like detecting fraud and abuse, data scientists measure impact of COVID on economic and jobs landscape, engineers run ad hoc analysis to debug production issues, business analysts build robust data driven offering to help salespeople make smarter decisions, site-reliability engineers analyze internal system performances and more. In this talk, we will go through Trino's growth at LinkedIn, how it fits into our data ecosystem, some of our operating challenges and dive into a few of our use cases. We'll also talk about our learnings, contributions, and philosophy on open source and what has worked well for us.
[DockerCon 2023] Reproducible builds with BuildKit for software supply chain ...Akihiro Suda
Images maintained by a reputable organization or an individual are often considered to be trustworthy; however, it is hard to deny the possibility that they might have silently injected malicious codes that are not present in the source repo. Also, even if they have no malicious intent, their images can still be compromised on an accidental leakage of registry credentials.
The latest release of BuildKit solves this supply chain security concern with reproducible builds. Reproducible builds is a technique to ensure that a bit-for-bit identical image can be reproduced from its source code, by anybody, at any time. When multiple actors can attest to an image's reproducibility, it signifies that the image contains no code of a secret origin.
Audiences of this talk will learn how they can and how sometimes they cannot make their images reproducible to improve their trust.
Running Kubernetes in Production: A Million Ways to Crash Your Cluster - DevO...Henning Jacobs
Bootstrapping a Kubernetes cluster is easy, rolling it out to nearly 200 engineering teams and operating it at scale is a challenge. In this talk, we are presenting our approach to Kubernetes provisioning on AWS, operations and developer experience for our growing Zalando developer base.
We will walk you through our horror stories of operating 80+ clusters and share the insights we gained from incidents, failures, user reports and general observations.
Most of our learnings apply to other Kubernetes infrastructures (EKS, GKE, ..) as well.
This talk strives to reduce the audience’s unknown unknowns about running Kubernetes in production.
Prometheus monitoring from outside of Kubernetes 〜どうして我々はKubernetes上のPromet...whywaita
talked by Prometheus Tokyo Meetup #2 https://prometheus.connpass.com/event/127574/
re-upload: https://speakerdeck.com/whywaita/prometheus-monitoring-from-outside-of-kubernetes-kubernetesprometheus-prometheustokyo
OSMC 2022 | Ignite: Observability with Grafana & Prometheus for Kafka on Kube...NETWAYS
Self-managing a highly scalable distributed system with Apache Kafka® at its core is not an easy feat. That’s why operators prefer tooling such as Confluent Control Center for administering and monitoring their deployments. However, sometimes, you might also like to import monitoring data into a third-party metrics aggregation platform for service correlations, consolidated dashboards, root cause analysis, or more fine-grained alerts. If you’ve ever asked a question along these lines: Can I export JMX data from Confluent clusters to my monitoring system with minimal configuration? What if I could correlate this service’s data spike with metrics from Confluent clusters in a single UI pane? Can I configure some Grafana dashboards for Confluent clusters?
This talk will enable you on achieving the below:
Monitoring Your Event Streams: Integrating Confluent with Prometheus and Grafana (this article)
Monitoring Your Event Streams: Tutorial for Observability Into Apache Kafka Clients
Apache Spark Streaming in K8s with ArgoCD & Spark OperatorDatabricks
Over the last year, we have been moving from a batch processing jobs setup with Airflow using EC2s to a powerful & scalable setup using Airflow & Spark in K8s.
The increasing need of moving forward with all the technology changes, the new community advances, and multidisciplinary teams, forced us to design a solution where we were able to run multiple Spark versions at the same time by avoiding duplicating infrastructure and simplifying its deployment, maintenance, and development.
Video k prezentaci https://youtu.be/Hi0CSr9usn8
Dnešní zákazníci chtějí rychlé a bezchybné služby. IT týmy jsou pod tlakem efektivně tuto službu poskytovat. High level pohled na ITIL. Příklady řešení z praxe s důrazem na efektivitu a rychlost nasazení.
Video: https://youtu.be/i-uatke7bps
Atlassian Cloud jako produkt pro malé a střední firmy (Small and Medium Enterprise)
Předpoklady:
Jsme malá nebo střední firma (SME) z pohledu počtu uživatelů (10 až max 500).
Máme „běžné“ IT oddělení (do 10 adminů) nebo IT spíš nemáme.
ITSM - Jira Service Desk a spřátelené aplikace z rodiny AtlassianOnlio
Pro koho vybíráme service desk a co chceme řešit?
Nechme si poradit, odborník nese odpovědnost :).
Náklady na prvním místě, platíme za nákup, maintenance, správu, ale i řízení týmu agentů.
Nasazujme po kouskách, agilně, … a uvidíme.
Hlavně komunikujme, se zákazníkem, s týmem.
Sledujme reporty, ty hlavní ukazujme ostatním.
Kurz přežití na Jira Serveru - podpora pro serverové produkty Atlassian skonč...Onlio
Odkaz na video https://youtu.be/sIAME5GvlyI
1) Sledovat Security Advisories https://www.atlassian.com/trust/security/advisories
2) Omezit přístupy na Server – VPN, přístup jen z definovaných IP adres, jen nezbytné porty
3) Omezit možnosti architektury k napadení – Apache http jako proxy před Tomcat (je první na ráně a odstíní problémovější funkcionality)https://cwiki.apache.org/confluence/display/TOMCAT/Connectors#Connectors-Q3
4) Provádět vlastní security audity a rozhodovat o dalším postupu
Migrace do Atlassian cloudu z Jira Server Data Center a Confluence (+video)Onlio
Odkaz na video https://youtu.be/btn-oTxyFTs
U větších instancí vhodné migrovat po fázích (např. sada méně používaných projektů jako první, nejvíce vytížené projekty jako poslední), roztřídit projekty do migračních fází v rámci plánu.
Teoreticky nic nebrání tomu zmigrovat všechny projekty najednou (vhodné u menšího počtu projektů nebo tam, kde se pracuje napříč více projekty).
Vzhledem k možnosti využít trial cloud verze lze migraci provést nejprve v testovací fázi (do testovací instance).
Video: https://youtu.be/75dcGroCOSg
Představení Jira Service Management
Druhy projektů v Jira Cloud
Upgrade na novější verzi, vyplatí se? Jaké jsou novinky?
Možnosti rozšíření, typy & triky
Video k prezentaci https://youtu.be/Wi_nCMYRpQ4
Confluence cloud knowledge base - Znalostní báze obecně i pro JSM, projektová, firemní, týmová dokumentace. Co nesplní DMS, ale Confluence ano - collaborative editing. Příklady řešení.
Video k prezentaci https://youtu.be/c8JRmtFlwdY
Atlassian Access - Služba pro Cloud, která umožňuje organizacím přidat funkce správy identit a přístupu IAM (identity and access management) na podnikové úrovni.
Video k prezentaci https://youtu.be/R9WozzlaYGA
Jira Service Management cloud automation - Příklady automatizace pro Jira Service Management, use cases (kaskádová pole atd.)
Video: https://youtu.be/qoN6z-bpGa0
Rychlý start práce v prostředí Atlassian
Redukce ceny za HW, instalaci, upgrades a další skryté náklady Vždycky nejnovější verze produktu v údržbě Atlassian
Správa Data Residency – již od Standard plánu
Volba měsíční platby – možnost flexibilně měnit počet uživatelů
7. 7
Insight Cloud vs Insight for Data Center/Server
The Insight app 3 main versions:
Insight - Asset Management Data Center and Server (Functionality
is the same as Insight included in Jira Service Management Data
Center 4.15+)
Insight - Asset Management Cloud app from Marketplace (end-of-
life March 31, 2022)
Insight in Jira Service Management Cloud Premium and Enterprise
Porovnání https://support.atlassian.com/jira-service-management-
cloud/docs/what-are-the-differences-between-insight-in-cloud-and-server/
8. 8
Lze Insight použít s projekty Jira Software nebo Jira Work
Management?
Ano. Objekty Insight lze propojit s issue Jira Software nebo Jira Work
Management, pokud je ve stejné instanci aktivní licence Jira Service
Management Premium nebo Enterprise.
Pro přístup k databázi Insight musíte být licencovaným uživatelem Jira
Service Management Premium nebo Enterprise a správci vám udělili
potřebná oprávnění.
FAQ https://www.atlassian.com/licensing/insight-jira-service-management
9. 9
Migrace z Insight addonu do Insight JSM
Návod, jak používat export a import schématu Insight, který vám pomůže
migrovat vaše data z aplikace Insight - Asset Management Cloud do
Insight for Jira Service Management Cloud Premium nebo Enterprise,
naleznete zde https://support.atlassian.com/jira-service-management-
cloud/docs/migrate-insight-cloud-to-insight-in-jira-service-management/
Osobní poznámky:
Menu „Import“ vždy chybí v administraci Cloud Insight JSM, je nutné žádat
support Atlassianu.
Po importu Insight schema key se často změní číslo (např. KEY-3 -> KEY-2).
11. 11
Stručný postup vytvoření AM v Insight
1. Vytvořte schéma – strukturu, objekty, parametry
2. Definujte permissions pro schéma
3. Vytvořte custom field (typ Insight) – konfigurace na schéma a filtr
4. Přidejte custom field do screen – pro agenty i customers
Permissions – Role:
Object Schema Managers, Developers, Users, +Jira admins
https://support.atlassian.com/jira-service-management-cloud/docs/what-
are-roles/
12. 12
Vytvoření custom fieldu (typ Insight)
Založení pole Insight + Insight object field configuration
Přidání na screen pro Agenta, případně i pro Customera
https://www.atlassian.com/software/jira/service-management/product-guide/tips-and-tricks/insight-
cloud-get-started#6-configure-insight-custom-fields
13. 13
Insight Query Language - IQL
IQL slouží pro dotazy do Insight, vytvoření search views, automation
rules, advanced references mezi assety, nebo pro importy.
https://support.atlassian.com/jira-service-management-cloud/docs/use-
jira-and-insight-query-languages-with-insight/
Příklady:
objectType = "Host" AND "Operating System" = "Ubuntu„
objectType in objectTypeAndChildren("Asset Details")
Owner.User = ${reporter} (při využití attributu User v Objektovém Typu)