Conditional Access Overview and Templates are now Generally Available!

Conditional Access Overview and Templates are now Generally Available!

This article is contributed. See the original author and article here.


Today, we are excited to announce the general availability of Conditional Access overview dashboard and templates. Conditional Access protects thousands of organizations across the globe daily and customers often ask us about best practices and how to improve security coverage. Conditional Access overview dashboard and templates empower Microsoft Entra ID customers to gain insights into their security posture, assess the impact of individual policies, and simplify deployment of Microsoft’s recommendations.


 


I’ve invited Krishna Venkit and Lisa Huang-North, product managers on the Identity Security team to share more details about these two features.


 


Best Regards,


 


Nitika Gupta


Group Product Manager, Identity Security


Microsoft Identity Division









————————————————————


 


Hi everyone!


 


The Conditional Access overview is a built-in dashboard that offers a comprehensive view of your Conditional Access posture. As an administrator, it provides a concise summary of your policies, identifies any gaps in your policy coverage, and provides valuable insights based on sign-in activity within your tenant. This feature enables you to swiftly pinpoint areas where you can enhance the enforcement of Zero Trust principles, ultimately bolstering your defense mechanisms.


 


Figure 1 Conditional Access overviewFigure 1 Conditional Access overview

 


The dashboard is now the default landing page of Conditional Access. As the first entry point into Conditional Access, the overview page lets you quickly create new policies using one of the Conditional Access templates which capture commonly used policies and best practices.


 


The dashboard also offers the following insights and reporting capabilities:


 



  •  The “See all unprotected sign-ins” link under the Users tile helps you rapidly identify users that are signing in without the protections of a Conditional Access policy.


 


Figure 2 Sign-ins without CA coverage during the last 7 daysFigure 2 Sign-ins without CA coverage during the last 7 days

 



  • The ‘See all non-compliant devices’ and ‘See all unmanaged devices’ links under the Devices tile help you identify device compliance gaps.


 


Figure 3 Non-compliant devicesFigure 3 Non-compliant devices

 



  • You can discover the top 10 most accessed apps without Conditional Access coverage using the coverage tab and go one step further and identify the users without coverage for that app by clicking on the numbers in the ‘Users without coverage’ column.


 


Figure 4: Top accessed applications without CA coverageFigure 4: Top accessed applications without CA coverage

 


You can discover security alerts generated based on sign-in activity in your tenant and take quick action on the alerts by deploying recommended zero trust conditional access policies using the Conditional Access templates. Speaking of which, let’s take a brief walkthrough of Conditional Access templates.


 


Conditional Access templates are a pre-defined set of conditions and controls that provide a convenient method to deploy new policies aligned with Microsoft recommendations. Customers are assured that their policies reflect modern best practices for securing corporate assets, promoting secure, optimal access for their hybrid workforce.


 


Conditional Access templates are organized across five scenarios:


 



  • Secure foundation

  • Zero Trust

  • Remote work

  • Protect administrators

  • Emerging threats


 


Organizations can choose from 16 predefined Conditional Access templates based on their specific needs. Here is an example!


 


With the “Require phishing-resistant multifactor authentication for admins” Conditional Access template, customers can reduce the risk of compromise and phishing attacks on privileged users. This powerful template uses Conditional Access authentication strengths to help you choose the right authentication method requirements for specific scenarios, making it easier than ever for organizations to move their most critical users towards more secure, modern, and strong authentication.


 


Figure 5: Conditional Access template - Require phishing-resistant multifactor authentication for adminsFigure 5: Conditional Access template – Require phishing-resistant multifactor authentication for admins

 


Learn more about the Conditional Access overview dashboard: https://aka.ms/CAOverviewDashboard


 


Learn more about Conditional Access templates: https://aka.ms/ConditionalAccessTemplateDocs


 


Tell us what you think


 


Give it a try and let us know if you have questions or feedback at https://aka.ms/AzureADFeedback. We hope you will love it as much as we do!


 


Krishna Venkit


Product Manager


Microsoft Identity Division


 


Lisa Huang-North (@lisaychuang),


Senior Product Manager


Microsoft Identity Division


 


 


Learn more about Microsoft identity:   








IDC shares how generative AI transforms business processes within marketing, sales, and service 

IDC shares how generative AI transforms business processes within marketing, sales, and service 

This article is contributed. See the original author and article here.

This post is authored by Gerry Murray, Marketing and Sales Technology Research Director, IDC; and coauthored by Carlena Neely, Product Marketing Manager, Business Applications, Microsoft.

Retail store manager enters store after using her badge to securely open the store.

Dynamics 365 AI

Learn about the latest AI breakthroughs with Microsoft Sales Copilot.

Delving into the realm of customer-centric strategies, IDC analyst Gerry Murray casts a visionary light on the transformative influence of generative AI (Gen AI) on sales and service. Murray’s perspective resonates powerfully with the groundbreaking nature of Gen AI, which is reshaping customer interactions into a new era of efficiency and effectiveness.

Gen AI isn’t just another technology; it’s a strategic leap that orchestrates seamless data and workflows across marketing, sales, and service touchpoints. Gerry Murray emphasizes its potential to eliminate mundane tasks, such as drafting emails and preparing for meetings, while providing real-time support during crucial interactions. It is also extremely exciting to have covered the addition of Microsoft Sales Copilot, providing a more streamlined and AI-powered selling experience.

The true potential of Gen AI infused in tools such as Microsoft Sales Copilot unfolds when it seamlessly integrates with other applications such as CRM systems, Microsoft 365, and Microsoft Teams, presenting a harmony that minimizes risks while maximizing benefits. This strategic synergy aligns with industry best practices and fosters an environment of innovation.

Gen AI transcends the realm of ordinary tools; it’s an enabler propelling business toward an era of seamless experiences and unparalleled efficiency.

AI-powered customer and seller experiences

Customers have extremely high expectations for a vendor’s ability to personalize everything about their experiences pre- and post-sale. Consumers expect each touchpoint in their journey to be informed and enhanced by all the previous touchpoints. Business buyers have the same expectations, but they take much more work on the part of sellers to fulfill as everything about the B2B sales process is far more complex than B2C. In both cases, the days of relying on customers to continually explain the context of their situation to the next point of contact are over.

To achieve today’s new level of continuity, the data from every touchpoint needs to be available to every other system within brand and regulatory policy. Giving all customer-facing functions equal insights into behaviors such as social sentiment, sales engagement, purchase histories, late payments, product returns, and support consumption can greatly improve business performance across the board. That improvement requires the underlying infrastructure supporting front-office applications to enable the customer’s data to be available to service them wherever they go next, which is a daunting challenge for large enterprises with fragmented data silos.

Generative AI (Gen AI) for front office applications can manage the data and workflow triggers between customer interactions across marketing, sales, and service enabling these employees to be more helpful faster, which in turn raises customer satisfaction, advocacy, and lifetime value.

Generative AI for sales

AI-powered role-based assistants can help sales reps increase productivity and personalize every customer interaction so they can close more deals. Gen AI can be present in the tools sellers use daily such as Outlook, Microsoft Teams, or Microsoft Dynamics 365 Sales, and connects to other CRM systems. AI alleviates the tedium and time sinks of endless click loops through menus, drop downs, pick lists, and check boxes. The impact on employee experience will be significant as AI will enable sellers to:

  • Get auto-generated opportunity summaries including status, progress, and highlights of key changes.
  • Create contextual emails that utilize customer CRM data to pull in product, customer, and opportunity information.
  • Prepare for customer meetings with a summary view including account information, recent notes, highlights of any issues or concerns, customer news, and more.
  • Get real-time tips and suggested answers during video meetings prompted by competitor or brand mentions by the customers to stay ready to handle objections.

Augmenting the front office with insight

But AI in and of itself is not enough, as it requires a great deal of data. To help organizations increase the speed of acting on customer insights and orchestrating personalized customer journeys, data infrastructure must offer both customer data platform and customer journey orchestration capabilities as a single solution and continue investments into real-time marketing.

The most effective way to optimize the benefits of AI and minimize the risks at the same time is to put AI in the context of other applications. This approach makes AI effective at completing repetitive tasks for customer-facing employees in marketing, sales, commerce, merchandising, point of sale, customer service and support, call center, loyalty, and so forth, all functions in which decision quality and cycle time are essential to customer satisfaction. Microsoft Dynamics 365 Copilot can eliminate repetitive tasks such as:

  • Drafting messages and project plans.
  • Scheduling and summarizing sales calls.
  • Creating, testing, and fine-tuning audience segments.
  • Matching brand guidelines for emails, forms, and event registration pages using natural language to deliver a consistent brand narrative and customer experience.
  • Orchestrating customer journeys across marketing, sales, and service, so customer actions can be responded to appropriately and quickly, generating sales leads or increasing customer satisfaction.

AI significantly impacts customer-facing employees, improving their experience and enabling them to focus on higher-value tasks. It accelerates decision-making, improves productivity, and enhances the coordination of interactions with customers across various touchpoints.

Enhancing business efficiency

Gen AI is new and evolving at warp speed. IDC expects there to be a great deal of innovation in terms of future capabilities and a wide range of use cases across multiple front-office functions. Examples of how Gen AI can enhance work processes for sales and marketing include:

  • Additional sales use cases for Gen AI could include request for proposal (RFP) creation and response, upselling recommendations, price optimization, contract generation and review, account planning, territory optimization, and more.
  • In marketing, Gen AI can describe their customer segment in their own words to create a target segment with the query assist feature. Marketers can also use Gen AI to get inspiration for email campaign content based on a simple request. Gen AI can make suggestions based on key topics entered by the marketer and the organization’s existing marketing emails, as well as from a range of internet sources to increase the relevance of generated ideas. Additional use cases could include fine-tuned segmentation, send time optimization, content generation, testing and optimization, attribution, media mix modeling, and more.

IDC conclusion

Gen AI is one of the most significant technological advances of the last decade, it is as much of a quantum leap as the graphical user interface, the Internet, and smartphones. Gen AI is a major advancement for line-of-business people who can now explain what they want to do to an AI assistant instead of having to learn how to do it in a graphical user interface (GUI) that might involve hundreds of mouse clicks on menu calls, dialog boxes, drop downs, radio buttons, application switching, and so forth.

Learn more about the latest AI breakthroughs with Microsoft Sales Copilot on the Dynamics 365 AI webpage.


The post IDC shares how generative AI transforms business processes within marketing, sales, and service  appeared first on Microsoft Dynamics 365 Blog.

Brought to you by Dr. Ware, Microsoft Office 365 Silver Partner, Charleston SC.

Utilizando Log Analytics para monitorar logs de auditoria do Azure RedHat OpenShift

Utilizando Log Analytics para monitorar logs de auditoria do Azure RedHat OpenShift

This article is contributed. See the original author and article here.

Utilizando Log Analytics para monitorar logs de auditoria do Azure RedHat OpenShift


 


Introdução


Por padrão, os clusters Azure Red Hat OpenShift possuem uma forma de monitorar os logs de auditoria através do OpenShift Logging, que envolve a instalação do OpenShift Elasticsearch Operator e OpenShift Cluster Logging. Embora essa solução seja eficiente, ela não permite a integração com o Azure Monitor, a solução de monitoramento da Microsoft, nem a centralização dos logs de auditoria de diversos clusters em um único local.


Para demonstrarmos uma solução personalizada, é necessário possuir um cluster Azure Red Hat OpenShift. Caso você não possua um cluster, é possível seguir o tutorial Criando um cluster Azure Red Hat OpenShift e lembre-se de utilizar a opção do pull secret para baixar as imagens da RedHat Pull Secret


 


Pré-requisitos



 


Fluent Bit


Fluent Bit é um sistema de coleta e encaminhamento de registros e logs (logs de eventos e mensagens) desenvolvido como parte do ecossistema Fluentd. É uma solução leve e eficiente projetada para coletar, filtrar e encaminhar logs em ambientes distribuídos.


 


Azure Red Hat OpenShift


Após a criação do cluster, vamos analisar as pastas que estão os logs de auditoria do cluster.



  • Faça o login no cluster, você pode pegar o endereço do cluster no portal do Azure, na aba Overview do cluster criado e clicando no botão Connect238048625-6f564954-2705-49c7-be99-8a3c8035037f.png

  • Clique na URL e utilize o username kubeadmin como user e o password como senha.


 


Instalando o Fluent Bit no cluster


Para fazer a instalação no Azure Red Hat OpenShift, precisamos setar o security context constraints (SCC), para isso você precisa estar logado via cli e ter um usuário com permissão de cluster-admin.


Execute o comando abaixo para criar o SCC:


kubectl create -f https://raw.githubusercontent.com/fluent/fluent-bit-kubernetes-logging/master/fluent-bit-openshift-security-context-constraints.yaml

A instalação do Fluent Bit é feita via helm charts, para isso, vamos adicionar o repositório do helm charts do Fluent Bit:


helm repo add fluent https://fluent.github.io/helm-charts

Por padrão a instalação do Fluent Bit os DaemonSets são instalados somente nos workers nodes, mas para ter acesso aos logs de auditoria, precisamos fazer a instalação somente no master node, para isso, vamos criar um arquivo chamado values.yaml com o seguinte conteúdo:


# kind — DaemonSet or Deployment
kind: DaemonSet

# replicaCount — Only applicable if kind=Deployment
replicaCount: 1

image:
repository: cr.fluentbit.io/fluent/fluent-bit
# Overrides the image tag whose default is {{ .Chart.AppVersion }}
tag: “latest-debug”
pullPolicy: Always

testFramework:
enabled: true
image:
repository: busybox
pullPolicy: Always
tag: latest

imagePullSecrets: []
nameOverride: “”
fullnameOverride: “”

serviceAccount:
create: true
annotations: {}
name:

rbac:
create: true
nodeAccess: false

# Configure podsecuritypolicy
# Ref: https://kubernetes.io/docs/concepts/policy/pod-security-policy/
# from Kubernetes 1.25, PSP is deprecated
# See: https://kubernetes.io/blog/2022/08/23/kubernetes-v1-25-release/#pod-security-changes
# We automatically disable PSP if Kubernetes version is 1.25 or higher
podSecurityPolicy:
create: false
annotations: {}

openShift:
# Sets Openshift support
enabled: true
# Creates SCC for Fluent-bit when Openshift support is enabled
securityContextConstraints:
create: true
annotations: {}

podSecurityContext: {}
# fsGroup: 2000

hostNetwork: false
dnsPolicy: ClusterFirst

dnsConfig: {}
# nameservers:
# – 1.2.3.4
# searches:
# – ns1.svc.cluster-domain.example
# – my.dns.search.suffix
# options:
# – name: ndots
# value: “2”
# – name: edns0

hostAliases: []
# – ip: “1.2.3.4”
# hostnames:
# – “foo.local”
# – “bar.local”

securityContext:
privileged: true
runAsUser: 0
readOnlyRootFilesystem: false
# capabilities:
# drop:
# – ALL
# readOnlyRootFilesystem: true
# runAsNonRoot: true
# runAsUser: 1000

service:
type: ClusterIP
port: 2020
loadBalancerClass:
loadBalancerSourceRanges: []
labels: {}
# nodePort: 30020
# clusterIP: 172.16.10.1
annotations: {}
# prometheus.io/path: “/api/v1/metrics/prometheus”
# prometheus.io/port: “2020”
# prometheus.io/scrape: “true”

serviceMonitor:
enabled: false
# namespace: monitoring
# interval: 10s
# scrapeTimeout: 10s
# jobLabel: fluentbit
# selector:
# prometheus: my-prometheus
# ## metric relabel configs to apply to samples before ingestion.
# ##
# metricRelabelings:
# – sourceLabels: [__meta_kubernetes_service_label_cluster]
# targetLabel: cluster
# regex: (.*)
# replacement: ${1}
# action: replace
# ## relabel configs to apply to samples after ingestion.
# ##
# relabelings:
# – sourceLabels: [__meta_kubernetes_pod_node_name]
# separator: ;
# regex: ^(.*)$
# targetLabel: nodename
# replacement: $1
# action: replace
# scheme: “”
# tlsConfig: {}

## Beare in mind if youn want to collec metrics from a different port
## you will need to configure the new ports on the extraPorts property.
additionalEndpoints: []
# – port: metrics
# path: /metrics
# interval: 10s
# scrapeTimeout: 10s
# scheme: “”
# tlsConfig: {}
# # metric relabel configs to apply to samples before ingestion.
# #
# metricRelabelings:
# – sourceLabels: [__meta_kubernetes_service_label_cluster]
# targetLabel: cluster
# regex: (.*)
# replacement: ${1}
# action: replace
# # relabel configs to apply to samples after ingestion.
# #
# relabelings:
# – sourceLabels: [__meta_kubernetes_pod_node_name]
# separator: ;
# regex: ^(.*)$
# targetLabel: nodename
# replacement: $1
# action: replace

prometheusRule:
enabled: false
# namespace: “”
# additionalLabels: {}
# rules:
# – alert: NoOutputBytesProcessed
# expr: rate(fluentbit_output_proc_bytes_total[5m]) == 0
# annotations:
# message: |
# Fluent Bit instance {{ $labels.instance }}’s output plugin {{ $labels.name }} has not processed any
# bytes for at least 15 minutes.
# summary: No Output Bytes Processed
# for: 15m
# labels:
# severity: critical

dashboards:
enabled: false
labelKey: grafana_dashboard
annotations: {}
namespace: “”

lifecycle: {}
# preStop:
# exec:
# command: [“/bin/sh”, “-c”, “sleep 20”]

livenessProbe:
httpGet:
path: /
port: http

readinessProbe:
httpGet:
path: /api/v1/health
port: http

resources: {}
# limits:
# cpu: 100m
# memory: 128Mi
# requests:
# cpu: 100m
# memory: 128Mi

## only available if kind is Deployment
ingress:
enabled: false
className: “”
annotations: {}
# kubernetes.io/ingress.class: nginx
# kubernetes.io/tls-acme: “true”
hosts: []
# – host: fluent-bit.example.tld
extraHosts: []
# – host: fluent-bit-extra.example.tld
## specify extraPort number
# port: 5170
tls: []
# – secretName: fluent-bit-example-tld
# hosts:
# – fluent-bit.example.tld

## only available if kind is Deployment
autoscaling:
vpa:
enabled: false

annotations: {}

# List of resources that the vertical pod autoscaler can control. Defaults to cpu and memory
controlledResources: []

# Define the max allowed resources for the pod
maxAllowed: {}
# cpu: 200m
# memory: 100Mi
# Define the min allowed resources for the pod
minAllowed: {}
# cpu: 200m
# memory: 100Mi

updatePolicy:
# Specifies whether recommended updates are applied when a Pod is started and whether recommended updates
# are applied during the life of a Pod. Possible values are “Off”, “Initial”, “Recreate”, and “Auto”.
updateMode: Auto

enabled: false
minReplicas: 1
maxReplicas: 3
targetCPUUtilizationPercentage: 75
# targetMemoryUtilizationPercentage: 75
## see https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/#autoscaling-on-multiple-metrics-and-custom-metrics
customRules: []
# – type: Pods
# pods:
# metric:
# name: packets-per-second
# target:
# type: AverageValue
# averageValue: 1k
## see https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-configurable-scaling-behavior
behavior: {}
# scaleDown:
# policies:
# – type: Pods
# value: 4
# periodSeconds: 60
# – type: Percent
# value: 10
# periodSeconds: 60

## only available if kind is Deployment
podDisruptionBudget:
enabled: false
annotations: {}
maxUnavailable: “30%”

nodeSelector:
node-role.kubernetes.io/master: ”

tolerations:
– key: node-role.kubernetes.io/master
operator: Exists
effect: NoSchedule

affinity: {}

labels: {}

annotations: {}

podAnnotations: {}

podLabels: {}

## How long (in seconds) a pods needs to be stable before progressing the deployment
##
minReadySeconds:

## How long (in seconds) a pod may take to exit (useful with lifecycle hooks to ensure lb deregistration is done)
##
terminationGracePeriodSeconds:

priorityClassName: “”

env: []
# – name: FOO
# value: “bar”

# The envWithTpl array below has the same usage as “env”, but is using the tpl function to support templatable string.
# This can be useful when you want to pass dynamic values to the Chart using the helm argument “–set =”
# https://helm.sh/docs/howto/charts_tips_and_tricks/#using-the-tpl-function
envWithTpl: []
# – name: FOO_2
# value: “{{ .Values.foo2 }}”
#
# foo2: bar2

envFrom: []

extraContainers: []
# – name: do-something
# image: busybox
# command: [‘do’, ‘something’]

flush: 1

metricsPort: 2020

extraPorts: []
# – port: 5170
# containerPort: 5170
# protocol: TCP
# name: tcp
# nodePort: 30517

extraVolumes: []

extraVolumeMounts: []

updateStrategy: {}
# type: RollingUpdate
# rollingUpdate:
# maxUnavailable: 1

# Make use of a pre-defined configmap instead of the one templated here
existingConfigMap: “”

networkPolicy:
enabled: false
# ingress:
# from: []

luaScripts: {}

## https://docs.fluentbit.io/manual/administration/configuring-fluent-bit/classic-mode/configuration-file
config:
service: |
[SERVICE]
Daemon Off
Flush {{ .Values.flush }}
Log_Level {{ .Values.logLevel }}
Parsers_File parsers.conf
Parsers_File custom_parsers.conf
HTTP_Server On
HTTP_Listen 0.0.0.0
HTTP_Port {{ .Values.metricsPort }}
Health_Check On

## https://docs.fluentbit.io/manual/pipeline/inputs
inputs: |
[INPUT]
Name tail
Path /var/log/kube-apiserver/*.log
multiline.parser docker, cri
Tag audit.kube-apiserver.*
DB /tmp/kube_apiserver.db
Mem_Buf_Limit 50MB
Refresh_Interval 10
Skip_Empty_Lines On
Buffer_Chunk_Size 5M
Buffer_Max_Size 50M
Skip_Long_Lines Off

[INPUT]
Name tail
Path /var/log/openshift-apiserver/*.log
multiline.parser docker, cri
Tag audit.openshift-apiserver.*
DB /tmp/openshift-apiserver.db
Mem_Buf_Limit 50MB
Refresh_Interval 10
Skip_Empty_Lines On
Buffer_Chunk_Size 5M
Buffer_Max_Size 50M
Skip_Long_Lines Off

[INPUT]
Name tail
Path /var/log/oauth-apiserver/*.log
multiline.parser docker, cri
Tag audit.oauth-apiserver.*
DB /tmp/oauth-apiserver.db
Mem_Buf_Limit 50MB
Refresh_Interval 10
Skip_Empty_Lines On
Buffer_Chunk_Size 5M
Buffer_Max_Size 50M
Skip_Long_Lines Off

## https://docs.fluentbit.io/manual/pipeline/filters
filters: |
[FILTER]
Name kubernetes
Match kube.*
Merge_Log On
Keep_Log Off
K8S-Logging.Parser On
K8S-Logging.Exclude On

## https://docs.fluentbit.io/manual/pipeline/outputs
outputs: |
[OUTPUT]
Name stdout
Match *

## https://docs.fluentbit.io/manual/administration/configuring-fluent-bit/classic-mode/upstream-servers
## This configuration is deprecated, please use `extraFiles` instead.
upstream: {}

## https://docs.fluentbit.io/manual/pipeline/parsers
customParsers: |
[PARSER]
Name docker_no_time
Format json
Time_Keep Off
Time_Key time
Time_Format %Y-%m-%dT%H:%M:%S.%L

# This allows adding more files with arbitary filenames to /fluent-bit/etc by providing key/value pairs.
# The key becomes the filename, the value becomes the file content.
extraFiles: {}
# upstream.conf: |
# [UPSTREAM]
# upstream1
#
# [NODE]
# name node-1
# host 127.0.0.1
# port 43000
# example.conf: |
# [OUTPUT]
# Name example
# Match foo.*
# Host bar

# The config volume is mounted by default, either to the existingConfigMap value, or the default of “fluent-bit.fullname”
volumeMounts:
– name: config
mountPath: /fluent-bit/etc/fluent-bit.conf
subPath: fluent-bit.conf
– name: config
mountPath: /fluent-bit/etc/custom_parsers.conf
subPath: custom_parsers.conf

daemonSetVolumes:
– name: varlog
hostPath:
path: /var/log
– name: varlibdockercontainers
hostPath:
path: /var/lib/docker/containers
– name: etcmachineid
hostPath:
path: /etc/machine-id
type: File

daemonSetVolumeMounts:
– name: varlog
mountPath: /var/log
– name: varlibdockercontainers
mountPath: /var/lib/docker/containers
readOnly: true
– name: etcmachineid
mountPath: /etc/machine-id
readOnly: true

args: []

command: []

# This supports either a structured array or a templatable string
initContainers: []

# Array mode
# initContainers:
# – name: do-something
# image: bitnami/kubectl:1.22
# command: [‘kubectl’, ‘version’]

# String mode
# initContainers: |-
# – name: do-something
# image: bitnami/kubectl:{{ .Capabilities.KubeVersion.Major }}.{{ .Capabilities.KubeVersion.Minor }}
# command: [‘kubectl’, ‘version’]

logLevel: info


Se desejar comparar o arquivo que está sendo criado com o arquivo oficial do Fluent Bit, você pode acessar o repositório do Fluent Bit, o arquivo yaml acima também tem a configuração para as pastas abaixo de logs do Azure Red Hat OpenShift que usam a tag [INPUT].



  • /var/log/kube-apiserver

  • /var/log/openshift-apiserver

  • /var/log/oauth-apiserver


Nessa configuração acima estão também estamos usando a imagem com a tag “latest-debug”, com essa tag é possível ver os logs do Fluent Bit no console do pod após a instalação do Fluent Bit no cluster, para isso basta executar o comando abaixo:


ls /var/log/kube-apiserver
ls /var/log/openshift-apiserver
ls /var/log/oauth-apiserver

Para realizar a instalação, esteja na mesma pasta em que o arquivo values.yaml foi criado e execute o comando abaixo.


kubectl create namespace logging
helm install fluent-bit fluent/fluent-bit –namespace logging –values values.yaml

Logo após instalado, vá ao dashboard do seu cluster, selecione workloads e pods na aba lateral e selecione o project como logging, você deve ter a mesma quantidade de pods que o cluster tem de worker nodes, no meu caso são três workers nodes.


1.png

Com a configuração atual estamos somente lendo os arquivos de logs e mostrando no terminal.


9.png

 


Criando um Log Analytics workspace


Para enviarmos os logs para o Azure Monitor precisamos criar um Log Analytics workspace, para isso acesse siga os passos


Após a criação do Log Analytics workspace e acesse o mesmo e na menu lateral nos settings clique no Agents.


2.png

Salve o Workspace ID e o Primary Key, pois vamos usar os mesmo para a nova configuração.


Agora precisamos adicionar mais um output na configuração do ConfigMap do Fluent Bit.




  • Vá no ConfigMap(fluent-bit) e adicione o output abaixo no final do arquivo e clique no salvar.


      ## https://docs.fluentbit.io/manual/pipeline/outputs

    [OUTPUT]
    Name azure
    Match *
    Customer_ID ${WorkspaceId}
    Shared_Key ${SharedKey}
    Log_Type AuditOpenshift




  • Execute o comando abaixo para criar uma Secret com o WorkspaceId e SharedKey (que é o seu Primary Key). Mude o xxxx para o seus valores


    kubectl create secret generic fluentbit-secret –from-literal=SharedKey=”xxxx” –from-literal=WorkspaceId=”xxxx” -n logging



  • Após criar a secret você pode verificar a mesma rodando o comando abaixo.


    kubectl get secret fluentbit-secret  -n logging



  • Agora precisamos adicionar secret no DaemonSet, para isso vá no menu lateral e selecione DaemonSets e clique no fluent-bit e  selecione Enviroments




  • 0.png




  • Clique no Add from ConfigMap or Secret3.png




  • Adicione as environments SharedKey e WorkspaceId e no Select a resource , selecione o Secret que foi criado anteriormente fluent-bit-secret, deixe igual a imagem abaixo e clique no save.4.png




  • Para que a nova configuração seja aplicada, é necessário excluir os Pods atuais; execute o comando abaixo.


    kubectl delete pods -l app.kubernetes.io/instance=fluent-bit  -n logging



  • Após deletar os pods, você pode verificar que os novos pods já estão sendo criados com a nova configuração, para isso execute o comando abaixo.


    kubectl get pods -l app.kubernetes.io/instance=fluent-bit  -n logging
    # Utilize o nome do primeiro de pod que aparecer e execute o comando abaixo para ver os logs do pod.
    kubectl logs fluent-bit-xxxx -n logging | grep “customer_id=”



  • Vai mostrar os logs como abaixo, mostrando que o output para o Log Analytics workspace a foi enviado com sucesso.


      [2023/06/06 16:37:07] [ info] [output:azure:azure.1] customer_id=247446f4-e70c-4338-87d3-ba4f902a82c9, HTTP status=200
    [2023/06/06 16:37:07] [ info] [output:azure:azure.1] customer_id=247446f4-e70c-4338-87d3-ba4f902a82c9, HTTP status=200
    [2023/06/06 16:37:08] [ info] [output:azure:azure.1] customer_id=247446f4-e70c-4338-87d3-ba4f902a82c9, HTTP status=200
    [2023/06/06 16:37:08] [ info] [output:azure:azure.1] customer_id=247446f4-e70c-4338-87d3-ba4f902a82c9, HTTP status=200
    [2023/06/06 16:37:09] [ info] [output:azure:azure.1] customer_id=247446f4-e70c-4338-87d3-ba4f902a82c9, HTTP status=200



 


Vizualizando os logs de auditoria no Log Analytics workspace




  1. Entre no portal da azure, busque na barra de pesquisa do Log Analytics workspace e na lista selecione o Log Analytics workspace que foi criado nos passos anteriores.




  2. No menu lateral selecione logs como na imagem abaixo.







  3. Vai abrir uma tela de queries e feche a mesma.




  4. Em tables, abra custom logs e deve ter uma tabela com no nome AuditOpenshift_CL




  5. Vá no campo e coloque o comando abaixo e clique no Run


    AuditOpenshift_CL |
    take 100



  6. Após rodar o comando, irá mostrar todos os logs de auditoria que estão sendo enviados para o Log Analytics workspace6.png




 


Conclusão


 


Em resumo, o Fluent Bit é uma ferramenta poderosa para coletar e enviar logs para o Log Analytics Workspace da Azure. Com a configuração correta, você pode coletar logs de vários serviços e aplicativos em execução em seu cluster Kubernetes(OpenShift) e enviá-los para o Log Analytics Workspace para análise e monitoramento. Além disso, o Fluent Bit é altamente configurável e pode ser personalizado para atender às suas necessidades específicas. Esperamos que este guia tenha sido útil para você começar a usar o Fluent Bit em seu ambiente Kubernetes(OpenShift).


 


Referências


 


Integrating Microsoft Fabric with Azure Databricks Delta Tables

Integrating Microsoft Fabric with Azure Databricks Delta Tables

This article is contributed. See the original author and article here.

Introduction


Microsoft Fabric is a powerful unified analytics solution that allows users to seamlessly connect to various data sources, including Azure Databricks, and create insightful reports and visualizations without the need to move the data.


 


In this tutorial, we’ll show you step-by-step how to connect to Azure Databricks generated Delta Tables and create a report in Microsoft Fabric.


By the end of this tutorial, you’ll have the knowledge needed to read Azure Databricks generated Delta Tables from a Microsoft Fabric using Notebook and SQL query. You will also learn how to create a Power BI report that can help drive business decisions. So, let’s get started!


 


Anu_Singh_6-1693676822491.png


 


Prerequisites


Before you connect, complete these steps:



  • An Azure Databricks workspace

  • An ADLS Gen2 account to store delta table and a parquet file

  • A Fabric workspace. For more information, see Fabric trial docs.

  • A lakehouse in Fabric workspace. For more information, see Create a lakehouse with OneLake.


 


Create Delta Table in Databricks



  1. Create a delta table from Databricks Notebook.


   In this case, I am using parquet files stored in ADLS Gen2 account to create a delta table.


 

%SQL

--create external table from parquet files stored in ADLS Gen2
CREATE TABLE fact_internet_sales
LOCATION 'abfss://demo@[yourdatalake].dfs.core.windows.net/fact_internet_sales'
AS
SELECT * FROM PARQUET.`abfss://data@[yourdatalake].dfs.core.windows.net/fact_internet_sales`;

 


Anu_Singh_1-1693605529594.png


 


 

%Python

#Read parquet files stored in ADLS Gen2
df = spark.read.format('parquet')
    .load("abfss://data@[yourdatalake].dfs.core.windows.net/fact_internet_sales")

#Write to an external delta table
df.write 
    .format("delta") 
    .option("path", "abfss://demo@[yourdatalake].dfs.core.windows.net/fact_internet_sales") 
    .saveAsTable("fact_internet_sales")

 


Anu_Singh_2-1693605761876.png


 


 


Read the Delta Table in Fabric


Open the Fabric workspace to read, analyse and visualise the data.


 


Create shortcut to delta table


 



  1. In the lakehouse, click on ellipses (…) next to the Tables and select New Shortcut.

     Anu_Singh_1-1693606671369.png


     



  2. In the New shortcut wizard, select Azure Data Lake Storage Gen2 tile. For more information, see Create an Azure Data Lake Storage Gen2 shortcut                                                                                                                Anu_Singh_2-1693606905366.png

     



  3. Enter the storage account URL in Connection settings.












    Field



    Details



    URL



    https://StorageAccountName.dfs.core.windows.net



     Anu_Singh_3-1693607504533.png                                               



  4. Enter the connection details (sign in if required) and select Next

    In this case, I am using ‘Organization Account’ Authentication kind and hence need to sign in.






















    Field



    Details



    Connection



    Existing connections for the specified storage location will appear in the drop-down. If none exist, create a new connection.



    Connection name



    The Azure Data Lake Storage Gen2 connection name.



    Authentication kind



    The supported models are: Organizational account, Account Key, Shared Access Signature (SAS), and Service principal. For more information, see ADLS shortcuts.



     Anu_Singh_4-1693607697749.png                                                                                   



  5. Enter the Shortcut Name and Sub path details and then click Create




















    Field



    Details



    Shortcut Name



    Name of your shortcut



    URL



    The Azure Data Lake Storage Gen2 URL from the last page.



    Sub Path



    The directory where the delta table resides.



    Anu_Singh_5-1693607870447.png



  6. The shortcut pointing to the delta table (fact_internet_sales) created in the last section will now appear as a delta table under Tables in the Explorer pane.                                                                                                        


  7. Click on the table (fact_internet_sales) and the data in the table will show up.


     

    Anu_Singh_2-1693671634119.png

     




 


Read the data from Notebook – Lakehouse mode


The data in the table can now be queried directly from the notebook in Fabric.


 



  1. Right-click on the table or click on ellipses (…) next to the table, click Open in notebook and then New notebook.
     

     



     

    Anu_Singh_2-1693839332613.png
     

     



  2. New notebook will appear with the query automatically generated to read the data in the table.


  Select the Run Cell button or press Ctrl+Enter to execute the query and view the data.


Anu_Singh_8-1693673096446.png


 


 


Read the data using SQL – SQL Endpoint mode


The data in the table can also be queried directly using T-SQL query in Fabric.


 



  1. Browse to the SQL Endpoint created as part of Lakehouse provisioning from your workspace.


  2. After opening SQL Endpoint from the workspace, expand the database, schema and tables folder in the object Explorer to see all tables listed.




  3. Right-click on the table (fact_internet_sales) or click on ellipses (…) next to the table, click New SQL Query and then Select TOP 100 rows


     

    Anu_Singh_2-1693674552264.png

      



  4.  The script will be automatically generated and executed to show the data in the table or click on Run to execute the query.


    Anu_Singh_4-1693675947483.png


 


 


Visualise the data in Fabric using Power BI


The data in delta table can now be accessed and analysed in Power BI. You can either create a new dataset or use the default dataset created as part of lakehouse provisioning for a new report. For more information, see Direct Lake in Power BI and Microsoft Fabric


 


Using new dataset



  1. If you are in the Lakehouse mode, click on New Power BI dataset


      Anu_Singh_9-1693677766359.png


 


If you are in the SQL endpoint mode, click on New Power BI dataset from Reporting tab.


Anu_Singh_8-1693677639859.png


 



  1. In the New Dataset dialog, select the table to be included in the dataset and click Confirm.


Anu_Singh_12-1693599480206.png


 



  1. The dataset is automatically saved in the workspace, and then opens the dataset. In the web modelling experience page, click on New Report


Anu_Singh_5-1693839480214.png


 



  1. In the report authoring page, drag or select the attributes from Data pane to the left-hand side pane to be included in the visualization.


Anu_Singh_3-1693839409972.png


 


 


 


Using default dataset



  1. Select your workspace and open the default dataset.


Anu_Singh_10-1693677984415.png


 



  1. On the dataset page, click on Start from scratch to create a new report.


Anu_Singh_11-1693678126559.png


 



  1. In the report authoring page, drag or select the attributes from Data pane to the left-hand side pane to be included in the visualization.


Anu_Singh_4-1693839429999.png


 


 


 


 


Summary


In conclusion, this guide provides a seamless solution for accessing Azure Databricks generated delta tables from Microsoft Fabric and visualizing the data in Power BI without the need to move the data. By following the steps outlined in this guide, you can easily connect to your delta tables and extract valuable insights from your data. With the power of Azure Databricks and Microsoft Fabric combined, you can take your data analysis to the next level and make informed decisions that drive business success.


 


Give it a try and let me know if this was helpful.


 

MVP Rudy Ooms’s Dedication Solved Real-World Problem

MVP Rudy Ooms’s Dedication Solved Real-World Problem

This article is contributed. See the original author and article here.

Microsoft MVPs continue to help the community by sharing their profound technical knowledge and demonstrating leadership. In this article, we spotlight Rudy Ooms, an Enterprise Mobility MVP from the Netherlands, and explore his insightful troubleshooting story that made a significant impact in addressing a real-world challenge.


MVP Rudy Ooms.jpg


 


Rudy found a question in the Microsoft Management Customer Connection Program (MM CCP) regarding the 0x800705B4 error. The individual posting the question referenced a previous blog post by Rudy where he shared the same error, however, it was not exactly the same case. Therefore, he quickly decided to step in to help the person who posted this question.


 


“The moment I noticed the question popping up in the MM CCP, I became aware of the fact that the problem he was experiencing wasn’t a simple one and was giving him and his company a lot of issues and headaches. So, at that point in time, I really needed to help him out. When taking a closer look at the issue, I quickly understood that the Microsoft support desk could find it difficult to solve this case. Why? If you can’t reproduce it yourself it can become difficult to solve it and that’s where I come in”.


 


The issue was the device sync issue that impacted on new Autopilot enrollments due to error 0x800705b4. Rudy promptly set up his test device, started troubleshooting with his familiar tool Fiddler, went through a lot of trial and error such as using the SyncML tool, and he discovered that an illegal XML character was the culprit. By removing the assignment of the policy and the autopilot settings within the registry, he successfully mitigated the issue and made devices sync and new enrollments worked smoothly. For a comprehensive insight into his challenges and the adjustments he undertook, we highly recommend delving into the detailed narrative on his blog post. Rudy mentions that he helped another individual on the WinAdmins Discord channel facing the exact same issue.


https://call4cloud.nl/2023/06/the-0x800705b4-error-in-our-stars/


 


“After digging into the issue and finding the culprit within 24 hours yeah that felt pretty good,” Rudy is looking back on his contribution. Despite the lack of access to the questioner’s company’s tenants, the reason behind the early resolution of the situation was his desire to help, as well as his ability to improve his own troubleshooting skills by identifying the cause of the problem. This experience taught him a couple of things, ”It reminds me that you can learn new stuff every day… even when you thought you knew everything about SYNCML. And the MS community is strong and always willing to help – and so am I!”


 


The Product Group at Microsoft recognizes this wonderful troubleshooting story. Juanita Baptiste, Senior Program Manager, said of Rudy’s and the rest of the MVP community’s contributions, “The MVP community is more than just customers to bounce ideas off of. They are experts in their areas and cover more scenarios than we think. I have changed the design specs and features based on feedback from this community and it’s helped us build a better product. We can’t help everyone at the level of detail that MVPs (like Rudy) does, but the fact that they have each other for support and is an immense help to us!”


 


This story is the best practice of helping each other as a community. Whether you are an MVP or not, everyone has the ability to help others by sharing unique expertise and experience. Next, it is your turn. For example, the following sites (not limited to just the following, of course) can help you make a difference right away, starting today!


Microsoft Tech Community


Microsoft Q&A


GitHub


Feedback Hub