Some #free #online #courses from #Google, #Microsoft, #Meta, #AWS, and other big #universities

Google

• Google AI Crash Course: https://developers.google.com/machine-learning/crash-course
• Google Cloud Skills Fundamentals: https://www.cloudskillsboost.google/course_templates/60
• Android Developer Fundaments: https://google-developer-training.github.io/android-developer-fundamentals-course-concepts/
• Google Analytics for Beginners: https://analytics.google.com/analytics/academy/course/6
• Google Ads for Beginners: https://blog.hootsuite.com/google-ads/
• Google Data Science Professional Certificate: https://www.coursera.org/professional-certificates/google-data-analytics
• Google IT Support Professional Certificate: https://www.coursera.org/professional-certificates/google-it-support
• Google Project Management Professional Certificate: https://grow.google/certificates/project-management/
• Google Cybersecurity Professional Certificate: https://grow.google/certificates/cybersecurity/
• Google Digital Marketing & E-commerce Professional Certificate: https://www.coursera.org/professional-certificates/google-digital-marketing-ecommerce

Microsoft

• Microsoft Azure Fundamentals: https://learn.microsoft.com/en-us/credentials/certifications/azure-fundamentals/
• Microsoft Power BI: https://powerbi.microsoft.com/en-us/
• Microsoft Excel: https://www.microsoft.com/en-us/microsoft-365/excel
• Microsoft PowerPoint: https://www.microsoft.com/en-us/microsoft-365/powerpoint
• Microsoft Teams: https://www.microsoft.com/en-us/microsoft-teams/group-chat-software
• Microsoft Data Science Professional Certificate: https://learn.microsoft.com/en-us/credentials/certifications/azure-data-scientist/
• Microsoft Power Platform Professional Certificate: https://learn.microsoft.com/en-us/credentials/certifications/power-platform-fundamentals/
• Microsoft Azure AI Fundamentals: https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/
• Microsoft Security, Compliance, and Identity Fundamentals: https://learn.microsoft.com/en-us/credentials/certifications/security-compliance-and-identity-fundamentals/
• Microsoft Azure DevOps Fundamentals: https://azure.microsoft.com/en-us/solutions/devops/tutorial

Meta

• Meta Blueprint: https://www.facebookblueprint.com/
• Meta Spark: https://spark.meta.com/
• Meta AI: https://ai.meta.com/
• Meta AR/VR: https://www.meta.com/
• Meta Social Media Marketing: https://www.coursera.org/professional-certificates/facebook-social-media-marketing
• Meta Digital Marketing Professional Certificate: https://www.coursera.org/professional-certificates/facebook-social-media-marketing
• Meta Data Science Professional Certificate: https://anyinstructor.com/meta-marketing-analytics-professional-certificate/
• Meta Software Engineering Immersive: https://blockclubchicago.org/2022/06/09/meta-chicago-formerly-facebook-launches-software-engineering-courses-to-bring-more-black-people-into-tech/

AWS

• AWS Cloud Practitioner Essentials: https://aws.amazon.com/training/classroom/aws-cloud-practitioner-essentials/
• AWS Developer Fundamentals: https://aws.amazon.com/training/learn-about/developer/
• AWS Certified Solution Architect - Associate: https://aws.amazon.com/certification/certified-solutions-architect-associate/
• AWS Certified Data Analytics - Associate: https://aws.amazon.com/certification/certified-data-analytics-specialty/
• AWS Certified Machine Learning - Specialist: https://aws.amazon.com/certification/certified-machine-learning-specialty/
• AWS Cloud Practitioner Professional Certificate: https://aws.amazon.com/certification/certified-cloud-practitioner/
• AWS Solutions Architect Professional Certificate: https://aws.amazon.com/certification/certified-solutions-architect-professional/
• AWS Data Analytics Professional Certificate: https://aws.amazon.com/certification/certified-data-analytics-specialty/
• AWS Machine Learning Professional Certificate: https://aws.amazon.com/certification/certified-machine-learning-specialty/

Other big universities

• Harvard University: CS50's Introduction to Computer Science: https://pll.harvard.edu/course/cs50-introduction-computer-science
• Stanford University: Introduction to Artificial Intelligence: https://www.guru99.com/artificial-intelligence-tutorial.html
• Massachusetts Institute of Technology: Introduction to Computer Science and Programming Using Python: https://www.edx.org/learn/computer-science/massachusetts-institute-of-technology-introduction-to-computer-science-and-programming-using-python
• University of California, Berkeley: Data Science Specialization: https://www.coursera.org/specializations/jhu-data-science
• University of Michigan: Python for Data Science: https://www.coursera.org/learn/python-for-applied-data-science-ai
• Georgia Institute of Technology: Machine Learning: https://en.wikipedia.org/wiki/Machine_learning
• University of Toronto: Neural Networks and Deep Learning: http://neuralnetworksanddeeplearning.com/

Comparing #Microsoft #Azure, #AWS (#Amazon #WebServices), and #GCP (#Google #Cloud #Platform)

1. Popularity and Market Share:

   - AWS: The most popular and widely adopted cloud platform with the largest market share.

   - Azure: A close second in terms of market share and popularity.

   - GCP: Growing rapidly but still behind AWS and Azure in terms of market share.

2. Global Data Centers and Regions:

   - AWS: Operates in 25 geographical regions worldwide.

   - Azure: Operates in 60+ regions, with plans for further expansion.

   - GCP: Operates in 24 regions globally.

3. Compute Services:

   - AWS: Offers Amazon EC2 for virtual machines.

   - Azure: Provides Azure Virtual Machines.

   - GCP: Offers Google Compute Engine.

4. Container Services:

   - AWS: Amazon ECS and EKS for container management.

   - Azure: Azure Kubernetes Service (AKS) and Azure Container Instances.

   - GCP: Google Kubernetes Engine (GKE) for container orchestration.

5. Serverless Computing:

   - AWS: AWS Lambda for serverless functions.

   - Azure: Azure Functions.

   - GCP: Google Cloud Functions.

6. Storage Services:

   - AWS: Amazon S3 for object storage, Amazon EBS for block storage.

   - Azure: Azure Blob Storage, Azure Disk Storage.

   - GCP: Google Cloud Storage, Google Persistent Disk.

7. Databases:

   - AWS: Amazon RDS (Relational Database Service), Amazon DynamoDB (NoSQL).

   - Azure: Azure SQL Database, Azure Cosmos DB (NoSQL).

   - GCP: Cloud SQL, Cloud Spanner (globally distributed relational), Firestore (NoSQL).

8. Networking:

   - AWS: Amazon VPC (Virtual Private Cloud), AWS Direct Connect.

   - Azure: Azure Virtual Network, ExpressRoute.

   - GCP: Virtual Private Cloud (VPC), Dedicated Interconnect.

9. Analytics and Big Data:

   - AWS: Amazon EMR, Redshift, Athena, Glue.

   - Azure: Azure HDInsight, Azure Databricks, Azure Data Lake.

   - GCP: BigQuery, Dataprep, Dataflow.

10. Machine Learning and AI:

    - AWS: Amazon SageMaker, Lex, Rekognition.

    - Azure: Azure Machine Learning, Cognitive Services.

    - GCP: AI Platform, Vision AI, Natural Language Processing.

11. Identity and Security:

    - AWS: AWS Identity and Access Management (IAM), Cognito.

    - Azure: Azure Active Directory (AD), Azure Identity Protection.

    - GCP: Google Identity and Access Management (IAM), Identity-Aware Proxy.

12. DevOps and Management Tools:

    - AWS: AWS CloudFormation, AWS CodeDeploy.

    - Azure: Azure DevOps, Azure Resource Manager.

    - GCP: Google Cloud Deployment Manager, Cloud Source Repositories.

13. Pricing and Cost Management:

    - Pricing models vary across services and regions. Detailed cost analysis is essential to choose the most cost-effective solution.

14. Support and Documentation:

    - All three providers offer extensive documentation, a range of support plans, and active user communities.

15. Hybrid and Multi-Cloud:

    - AWS offers AWS Outposts for hybrid deployments.

    - Azure provides Azure Arc for managing resources across on-premises, multi-cloud, and edge environments.

    - GCP offers Anthos for managing applications across hybrid and multi-cloud environments.

16. Compliance and Certifications:

    - AWS, Azure, and GCP comply with various industry standards and hold certifications like ISO, SOC, and HIPAA.

17. Specialized Services:

    - Each cloud provider offers unique, specialized services. For example, AWS has AWS Lambda, Azure has Azure DevTest Labs, and GCP has BigQuery ML.

18. Customer Base:

    - AWS has a diverse customer base, including startups, enterprises, and government agencies.

    - Azure is popular among enterprises, especially those already using Microsoft products.

    - GCP attracts startups and enterprises looking for machine learning and data analytics capabilities.

19. Ecosystem and Partnerships:

    - AWS has a vast ecosystem of partners and integrations.

    - Azure leverages Microsoft's partnerships, especially in the enterprise space.

    - GCP focuses on partnerships for AI and data analytics.

20. Licensing and Vendor Lock-In:

    - Consider the implications of vendor lock-in when choosing a cloud provider. Each has its own set of proprietary services.

In summary, the choice between AWS, Azure, and GCP depends on your specific needs, existing technologies, and preferences. It's often a good idea to perform a thorough evaluation of your requirements and conduct cost comparisons before making a decision. Each provider offers a free tier and credits to help you get started and explore their services.

#List of some popular #AWS #services

1. Amazon EC2 (Elastic Compute Cloud):

   - Description: Virtual servers in the cloud that you can configure and scale as needed.

   - #AWS #EC2 #VirtualServers

2. Amazon S3 (Simple Storage Service):

   - Description: Scalable object storage for storing and retrieving data, such as files or backups.

   - #AWS #S3 #ObjectStorage

3. Amazon RDS (Relational Database Service):

   - Description: Managed database service for popular databases like MySQL, PostgreSQL, and more.

   - #AWS #RDS #ManagedDatabases

4. Amazon Lambda:

   - Description: Serverless computing service that lets you run code in response to events.

   - #AWS #Lambda #Serverless

5. Amazon VPC (Virtual Private Cloud):

   - Description: Networking service that allows you to create isolated networks in the AWS cloud.

   - #AWS #VPC #Networking

6. Amazon ECS (Elastic Container Service):

   - Description: Managed container orchestration service for running Docker containers.

   - #AWS #ECS #Containers

7. Amazon Redshift:

   - Description: Data warehousing service for analyzing large datasets.

   - #AWS #Redshift #DataWarehousing

8. Amazon SNS (Simple Notification Service):

   - Description: Messaging service for sending notifications and alerts.

   - #AWS #SNS #Notifications

9. Amazon SQS (Simple Queue Service):

   - Description: Managed message queue service for decoupling components in applications.

   - #AWS #SQS #MessageQueue

10. Amazon CloudWatch:

    - Description: Monitoring and logging service to gain insights into AWS resources and applications.

    - #AWS #CloudWatch #Monitoring

11. Amazon Elastic Beanstalk:

    - Description: Platform as a Service (PaaS) for deploying and managing web applications.

    - #AWS #ElasticBeanstalk #PaaS

12. Amazon DynamoDB:

    - Description: Managed NoSQL database service for fast and scalable data storage.

    - #AWS #DynamoDB #NoSQL

13. AWS IAM (Identity and Access Management):

    - Description: Service for controlling access to AWS resources securely.

    - #AWS #IAM #Security

14. AWS Glue:

    - Description: Fully managed ETL (Extract, Transform, Load) service for data preparation.

    - #AWS #Glue #ETL

15. Amazon Route 53:

    - Description: Scalable and highly available domain name system (DNS) web service.

    - #AWS #Route53 #DNS

16. AWS Lambda Layers:

    - Description: Share code and resources across multiple Lambda functions.

    - #AWS #LambdaLayers #Serverless

17. AWS Elastic Load Balancing:

    - Description: Automatically distribute incoming traffic across multiple EC2 instances.

    - #AWS #ELB #LoadBalancing

18. AWS Step Functions:

    - Description: Coordinate multiple AWS services into serverless workflows.

    - #AWS #StepFunctions #Serverless

19. Amazon Aurora:

    - Description: High-performance relational database compatible with MySQL and PostgreSQL.

    - #AWS #Aurora #Database

20. Amazon CloudFront:

    - Description: Content delivery network (CDN) service for faster content delivery.

    - #AWS #CloudFront #CDN

#AWS #Identity and #Access #Management (#IAM)

AWS Identity and Access Management (IAM) is like having a set of keys to access different rooms in a building. Let me explain it in simple terms:

__1. IAM Users:__

   - Imagine the building as your AWS account, and each room inside the building as a specific service or resource in AWS, like a storage area, a database, or a server.

   - Now, think of IAM users as people who need to access these rooms. Each user gets their own key.

__2. IAM Groups:__

   - Sometimes, you have groups of people who need access to the same rooms. IAM groups are like groups of users who share access to specific services.

   - Instead of giving each person a key, you can give a key to the group leader, and everyone in the group gets access to the same rooms.

__3. IAM Policies:__

   - Policies are like rules or instructions written on a piece of paper that say who can access which rooms and what they can do inside those rooms.

   - For example, you can have a policy that says, "User A can access Room 1 and Room 2 but can only read in Room 1, not delete anything."

__4. IAM Roles:__

   - Roles are like special passes that you give to temporary visitors. They are not for users but for services or applications.

   - These passes specify what a service can do and which rooms it can access. For example, you can have a role for a backup service that allows it to access your storage room for backup purposes.

__5. Root User:__

   - The root user is like the owner of the building who has access to all rooms and can give keys and set rules for others.

   - It's essential to keep the root user's key safe and not use it for everyday tasks to ensure security.

__6. Multifactor Authentication (MFA):__

   - MFA is like adding an extra layer of security. It's like needing both a key and a fingerprint to open a door.

   - With MFA, even if someone gets hold of your key, they can't access a room without your fingerprint (or another authentication method).

__7. Access Control:__

   - IAM helps you control who can enter which rooms, what they can do there, and when they can access them.

   - You can specify permissions precisely, so someone might have access to one room but not another.

In summary, AWS IAM is like being the manager of a building, deciding who can access which rooms, what they can do in those rooms, and keeping everything secure by providing keys, passes, and rules to the right people or services. It helps you control access to your AWS resources and keep your AWS account safe.

#IAMBasics ,#AWSIAM ,#AccessControl ,#SecurityInAWS ,#IAMUsers ,#IAMGroups ,#IAMPolicies ,#IAMRoles ,#AWSRootUser ,#MFAinAWS ,#AccessManagement ,#AWSAccessKeys ,#AWSPermissions ,#IAMBestPractices ,#AWSIdentity ,#AWSAuthorization ,#IAMSecurity ,#IAMRolesandPolicies ,#IAMAuthentication ,#IAMSecurityModel

how to setup #kerberos in #emr

 To set up Kerberos in Amazon Elastic MapReduce (EMR), you need to follow these steps:

1. Create a Kerberos realm: You can use an existing Kerberos realm or create a new one. A Kerberos realm is a collection of Kerberos principals and services that share a common security policy.

2. Create a KDC (Key Distribution Center): The KDC is the server that issues and manages Kerberos tickets. You can use an existing KDC or create a new one.

3. Set up the EMR cluster to use Kerberos: You need to enable Kerberos authentication for the EMR cluster. This can be done by specifying the Kerberos realm, KDC hostname, and other Kerberos-related configuration parameters when creating the EMR cluster.

4. Configure EMR applications to use Kerberos: You need to configure the applications running on the EMR cluster to use Kerberos authentication. This can be done by specifying the appropriate configuration settings for each application.

5. Test the Kerberos setup: Once you have configured Kerberos for EMR, you should test the setup to ensure that it is working correctly. You can do this by running sample jobs that use Kerberos authentication.

The exact steps for setting up Kerberos in EMR will depend on your specific requirements and environment. Amazon provides detailed documentation on how to set up Kerberos in EMR, which you can refer to for more information.

how to integrate #ldap with #emr

 To integrate LDAP with Amazon EMR (Elastic MapReduce), follow these steps:

1.       Create an LDAP directory service in AWS. You can use Amazon Managed AD or Simple AD to create a directory service.

2.       Create an IAM role for EMR to access the LDAP directory. You will need to create a policy that grants the EMR service access to the LDAP directory. Here's an example policy that you can use:

{

                "Version": "2012-10-17",

                "Statement": [{

                                "Effect": "Allow",

                                "Action": [

                                                "ds:DescribeDirectories",

                                                "ds:CreateComputer",

                                                "ds:DeleteComputer",

                                                "ds:DescribeComputers",

                                                "ds:JoinDirectory"

                                ],

                                "Resource": "*"

                }]

}

 

3.       Launch an EMR cluster and configure it to use the IAM role that you created in step 2.

4.       Configure the EMR cluster to join the LDAP directory. You can do this by adding the following configuration to the EMR cluster:

[{

                "Classification": "directory-service",

                "Properties": {

                                "directory_service_name": "<directory_service_name>",

                                "directory_service_password": "<directory_service_password>",

                                "directory_service_username": "<directory_service_username>",

                                "directory_service_domain_name": "<directory_service_domain_name>",

                                "directory_service_dns_ips": "<directory_service_dns_ips>"

                },

                "Configurations": []

}]

 

Replace the following variables with your own values:

 

<directory_service_name>: the name of the LDAP directory service that you created in step 1.

<directory_service_password>: the password for the user that you want to use to join the EMR cluster to the LDAP directory.

<directory_service_username>: the username for the user that you want to use to join the EMR cluster to the LDAP directory.

<directory_service_domain_name>: the domain name of the LDAP directory.

<directory_service_dns_ips>: the IP addresses of the DNS servers for the LDAP directory.

 

Start the EMR cluster.

 

Once the EMR cluster is running, you should be able to authenticate users against the LDAP directory. You can test this by SSHing into the EMR cluster and running an LDAP search using the ldapsearch command.