This Blog post contains a Vagrantfile, a Python application, and a MinIO installation script, all containerized using Docker and orchestrated using Docker Compose.
Prerequisites
- Vagrant
- Docker
- Docker Compose
The Repo Link Here
For the people who think differently Welcome aboard
This Blog post contains a Vagrantfile, a Python application, and a MinIO installation script, all containerized using Docker and orchestrated using Docker Compose.
The Repo Link Here
This blog will focus on building a highly resilient and globally available architecture using Oracle Cloud Infrastructure (OCI) Load Balancer. We’ll cover setting up a multi-region architecture, configuring global load balancing, and managing failover to ensure uninterrupted service availability.
Regards
osama
This blog will focus on enhancing the security of your Oracle Cloud Infrastructure (OCI) environments using Oracle Cloud Guard. We’ll cover configuring Cloud Guard, creating custom detector and responder rules, and managing incidents for comprehensive cloud security.
oci cloud-guard detector-recipe list --compartment-id <compartment_OCID>
In this blog, we will explore how to build a scalable data pipeline on Oracle Cloud Infrastructure (OCI) using OCI Data Flow. We’ll cover the end-to-end process, from setting up OCI Data Flow to processing large datasets, and integrating with other OCI services.
3. Creating a Scalable Data Pipeline
val df = spark.read.json("oci://<bucket_name>@<namespace>/data/")
df.filter("age > 30").write.csv("oci://<bucket_name>@<namespace>/output/")
5. Integrating with Other OCI Services
This blog will guide you through setting up and managing serverless architectures using Oracle Cloud Infrastructure (OCI) Functions. We will cover creating, deploying, and managing serverless functions, integrating them with other OCI services, and best practices for efficient serverless deployments.
oci setup config
Create an Application for Functions:
def handler(ctx, data: io.BytesIO = None):
name = "World"
if data:
name = json.loads(data.getvalue()).get("name", "World")
return "Hello, {}!".format(name)
FROM fnproject/python:3.8
ADD . /function/
WORKDIR /function/
RUN pip install -r requirements.txt
ENTRYPOINT ["python3", "func.py"]
Build and Deploy:
fn build
fn deploy --app <your_app_name>
oci monitoring alarm create --compartment-id <compartment_OCID> --display-name "FunctionErrors" --metric-name "Errors" --threshold 1 --comparison ">" --enabled true
Regards
Osama
In this blog, we will delve into automating the deployment of Oracle Cloud Infrastructure (OCI) resources using Terraform. We will cover setting up Terraform, writing infrastructure as code, and managing OCI resources efficiently.
terraform -version command.oci setup config
Create Terraform Configuration File:
provider.tf file:provider "oci" {
tenancy_ocid = "<your_tenancy_ocid>"
user_ocid = "<your_user_ocid>"
fingerprint = "<your_api_key_fingerprint>"
private_key_path = "<path_to_your_private_key>"
region = "<your_region>"
}
resource "oci_core_vcn" "example_vcn" {
cidr_block = "10.0.0.0/16"
display_name = "example_vcn"
compartment_id = "<your_compartment_ocid>"
}
modules/
vcn/
main.tf
variables.tf
outputs.tf
main.tf
variables.tf
outputs.tf
terraform init to initialize the configuration.terraform plan to preview the changes:terraform plan
terraform apply:terraform apply
terraform apply.terraform destroy:terraform destroy
Regards
Osama
In this blog, we will explore the advanced configurations need to implement high availability (HA) and disaster recovery (DR) for Oracle Cloud Infrastructure (OCI) Autonomous Database. We will cover setting up Data Guard, configuring cross-region replication, and performing failover and switchover operations.
oci db autonomous-database get --autonomous-database-id <database_OCID>
Thank you
Osama
Introduction
This blog will focus on implementing advanced data security measures with Oracle Cloud Infrastructure (OCI) Autonomous Database. We’ll cover provisioning, security configurations, and monitoring to make sure robust data protection.
oci monitoring alarm create --compartment-id <compartment_OCID> --display-name "HighCPUUsage" --metric-name "CpuUtilization" --threshold 85 --comparison ">" --enabled true
Thank you
Osama
oci db autonomous-database create --compartment-id <compartment_OCID> --db-name "MyDatabase" --cpu-core-count 1 --data-storage-size-in-tbs 1 --admin-password "<password>" --display-name "MyAutonomousDB" --db-workload "OLTP" --license-model "BRING_YOUR_OWN_LICENSE" --wait-for-state AVAILABLE
2. Configuring Data Replication
oci db autonomous-database backup create --autonomous-database-id <db_OCID> --display-name "MyBackup" --wait-for-state COMPLETED
3. Setting Up Data Guard for High Availability:
oci db autonomous-database create-data-guard-association --compartment-id <compartment_OCID> --primary-database-id <primary_db_OCID> --standby-database-id <standby_db_OCID> --display-name "MyDataGuardAssociation"
4. Implementing Disaster Recovery
oci db autonomous-database update --autonomous-database-id <db_OCID> --backup-retention-period 30
oci db autonomous-database restore --autonomous-database-id <db_OCID> --restore-timestamp "2024-01-01T00:00:00Z" --display-name "RestoredDatabase"
4. Testing and Validating Disaster Recovery
oci db autonomous-database failover --autonomous-database-id <standby_db_OCID>
5. Automating and Monitoring
oci monitoring alarm create --compartment-id <compartment_OCID> --display-name "HighIOWaitTime" --metric-name "io_wait_time" --threshold 1000 --comparison ">" --enabled true
Setting Up Oracle AI Services
oci data-science project create --compartment-id <compartment_OCID> --display-name "MyMLProject" --description "Project for predictive analytics"
Creating and Uploading Datasets:
oci data-science dataset create --compartment-id <compartment_OCID> --display-name "MyDataset" --data-location <object_storage_location> --format CSV
Creating a Model Training Job:
oci data-science job create --compartment-id <compartment_OCID> --project-id <project_OCID> --display-name "MyModelTrainingJob" --job-type "CUSTOM" --arguments '{"training_script":"<script_location>", "hyperparameters": {"learning_rate": 0.01}}'
Deploying and Using the Model
Deploying the Model:
oci data-science model-deployment create --compartment-id <compartment_OCID> --display-name "MyModelDeployment" --model-id <model_OCID> --deployment-config '{"instance_type": "VM.Standard2.2"}'
Invoking the Model Endpoint:
curl -X POST <model_endpoint_url> -H "Content-Type: application/json" -d '{"features": [value1, value2, ...]}'
Integrating Predictive Analytics into Business Workflows
Automating Predictions:
fn deploy --app myapp --image <docker_image> --env "MODEL_ENDPOINT_URL=<model_endpoint_url>"
Monitoring and Managing Models
oci monitoring metric-data list --compartment-id <compartment_OCID> --metric-name "model_accuracy"
Updating and Retraining Models:
oci data-science job create --compartment-id <compartment_OCID> --project-id <project_OCID> --display-name "ModelRetrainingJob" --job-type "CUSTOM" --arguments '{"training_script":"<new_script_location>", "hyperparameters": {"learning_rate": 0.001}}'
Thank you
Osama