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Diploma in DataOps RQF Level 6 - Coggle Diagram
Diploma in DataOps RQF Level 6
AWS cloud services
AWS CodeStar + AWS Lambda + Amazon Kinesis: Develop, build, and deploy serverless applications that process streaming data in real-time using Kinesis as the data source.
AWS CodePipeline + AWS CodeDeploy + Amazon RDS: Automate deployment of database changes to RDS instances through a continuous integration and continuous deployment (CI/CD) pipeline.
AWS Elastic Beanstalk + Amazon CloudWatch + Amazon EMR: Deploy, monitor, and scale Big Data applications using Elastic Beanstalk and monitor performance with CloudWatch. Process large datasets using EMR.
AWS CloudFormation + AWS Config + Amazon Redshift: Automate provisioning and management of Redshift clusters, enforce compliance policies with Config, and analyze large-scale datasets.
AWS Step Functions + AWS Fargate + Amazon S3: Orchestrate containerized data processing workflows using Step Functions, run tasks on Fargate, and store intermediate and final results in S3.
AWS App Runner + AWS X-Ray + Amazon DynamoDB: Deploy and monitor serverless applications with App Runner and X-Ray, and use DynamoDB as a fast, flexible, and scalable NoSQL database.
AWS CodeCommit + AWS Glue + Amazon QuickSight: Store code in a private Git repository using CodeCommit, transform and load data with Glue, and visualize insights with QuickSight.
AWS CloudTrail + Amazon GuardDuty + AWS Lake Formation: Monitor infrastructure activity with CloudTrail, detect threats with GuardDuty, and build a secure data lake using Lake Formation.
Amazon ECS + AWS Auto Scaling + Amazon Kinesis Data Analytics: Run containerized applications on ECS, scale based on demand with Auto Scaling, and analyze streaming data with Kinesis Data Analytics.
AWS Amplify + AWS AppSync + Amazon Elasticsearch: Build full-stack applications using Amplify, synchronize and manage data with AppSync, and search and analyze data with Elasticsearch.
AWS Batch + AWS Systems Manager + Amazon Athena: Run batch processing workloads, manage infrastructure with Systems Manager, and query data stored in S3 using Athena.
AWS CodeBuild + AWS Secrets Manager + AWS IoT Analytics: Build and package code with CodeBuild, manage secrets with Secrets Manager, and analyze IoT data with IoT Analytics.
AWS Cloud9 + AWS OpsWorks + Amazon Managed Streaming for Kafka (MSK): Develop applications in a cloud-based IDE with Cloud9, manage infrastructure as code with OpsWorks, and process streaming data with MSK.
Amazon API Gateway + AWS WAF + Amazon SageMaker: Expose APIs using API Gateway, protect them with WAF, and train and deploy machine learning models with SageMaker.
Amazon S3 + AWS Backup + Amazon Macie: Store data in S3, automate backups with Backup, and discover and protect sensitive data with Macie.
Amazon ECR + AWS Lambda + AWS Data Pipeline: Store container images in ECR, run serverless functions with Lambda, and orchestrate data workflows with Data Pipeline.
AWS Transfer Family + AWS Organizations + Amazon Kinesis Data Firehose: Transfer data over SFTP, FTPS, or FTP with Transfer Family, manage multiple AWS accounts with Organizations, and load streaming data into data stores with Kinesis Data Firehose.
AWS CloudShell + AWS Security Hub + Amazon Neptune: Access AWS resources in a browser-based shell with CloudShell, centrally manage security and compliance with Security Hub
Azure Cloud Services
Azure Machine Learning combined with Azure DevOps and SysOps can be used to build and deploy machine learning models in a continuous integration and continuous deployment (CI/CD) pipeline. Big data can be used to train the models and provide insights for decision-making.
Azure SQL Database with Azure DevOps and SysOps can be used to automate the management of databases, including backups, monitoring, and scaling. Big data can be used for analyzing and optimizing database performance.
Azure Stream Analytics with Azure DevOps and SysOps can be used to build and deploy real-time data processing solutions. Big data can be used for analyzing and processing large volumes of streaming data.
Azure Data Factory with Azure DevOps and SysOps can be used to automate the orchestration of data pipelines. Big data can be used to process and analyze large amounts of data from various sources.
Azure Event Hubs with Azure DevOps and SysOps can be used to ingest and process millions of events per second. Big data can be used to analyze and gain insights from the events.
Azure Databricks with Azure DevOps and SysOps can be used to build and deploy big data processing and machine learning solutions. Azure DevOps can be used to automate deployment pipelines and SysOps can be used to manage and monitor the infrastructure.
Azure Synapse Analytics with Azure DevOps and SysOps can be used to build and deploy big data analytics solutions. Azure DevOps can be used to automate the deployment pipeline, and SysOps can be used to manage and monitor the infrastructure.
Azure HDInsight with Azure DevOps and SysOps can be used to build and deploy big data processing and analytics solutions. Azure DevOps can be used to automate deployment pipelines, and SysOps can be used to manage and monitor the infrastructure.
Azure Cosmos DB with Azure DevOps and SysOps can be used to automate the management of globally distributed databases. Big data can be used to analyze and optimize database performance.
Azure Kubernetes Service with Azure DevOps and SysOps can be used to deploy and manage containerized applications. Big data can be used to analyze and gain insights from the application logs.
Azure Functions with Azure DevOps and SysOps can be used to build and deploy serverless applications. Big data can be used to analyze and gain insights from the application logs.
Azure Logic Apps with Azure DevOps and SysOps can be used to automate workflows between different services. Big data can be used to analyze and optimize the workflows.
Azure Service Bus with Azure DevOps and SysOps can be used to decouple applications and services. Big data can be used to analyze and gain insights from the messaging data.
Azure Cognitive Services with Azure DevOps and SysOps can be used to build and deploy intelligent applications. Big data can be used to train the models and provide insights for decision-making.
Azure Search with Azure DevOps and SysOps can be used to build and deploy search solutions. Big data can be used to analyze and optimize the search performance.
Azure API Management with Azure DevOps and SysOps can be used to manage and monitor APIs. Big data can be used to analyze and gain insights from the API usage.
Azure Virtual Machines with Azure DevOps and SysOps can be used to deploy and manage virtual machines. Big data can be used to analyze and optimize the virtual machine performance.
Azure Load Balancer with Azure DevOps and SysOps can be used to distribute incoming traffic across multiple virtual machines. Big data can be used to analyze and optimize the load balancing performance.
Azure CDN with Azure DevOps and SysOps can be used to deliver content to users around the world. Big data can be used to analyze and optimize the content delivery performance, including identifying bottlenecks and optimizing the content delivery network configuration.
Azure Security Center with Azure DevOps and SysOps can be used to monitor and protect cloud resources. Big data can be used to analyze and detect anomalies and potential security threats.