Organize png and tpl in subfolders

blueprints/README.md

@@ -5,7 +5,7 @@ This section **[networking blueprints](./networking/)** that implement core patt
 Currently available blueprints:
 
 - **cloud operations** - [Resource tracking and remediation via Cloud Asset feeds](./cloud-operations/asset-inventory-feed-remediation), [Granular Cloud DNS IAM via Service Directory](./cloud-operations/dns-fine-grained-iam), [Granular Cloud DNS IAM for Shared VPC](./cloud-operations/dns-shared-vpc), [Compute Engine quota monitoring](./cloud-operations/quota-monitoring), [Scheduled Cloud Asset Inventory Export to Bigquery](./cloud-operations/scheduled-asset-inventory-export-bq), [Packer image builder](./cloud-operations/packer-image-builder), [On-prem SA key management](./cloud-operations/onprem-sa-key-management), [TCP healthcheck for unmanaged GCE instances](./cloud-operations/unmanaged-instances-healthcheck), [HTTP Load Balancer with Cloud Armor](./cloud-operations/glb_and_armor)
-- **data solutions** - [GCE/GCS CMEK via centralized Cloud KMS](./data-solutions/gcs-to-bq-with-least-privileges/), [Cloud Storage to Bigquery with Cloud Dataflow with least privileges](./data-solutions/gcs-to-bq-with-least-privileges/), [Data Platform Foundations](./data-solutions/data-platform-foundations/), [SQL Server AlwaysOn availability groups blueprint](./data-solutions/sqlserver-alwayson), [Cloud SQL instance with multi-region read replicas](./data-solutions/cloudsql-multiregion/), [Cloud Composer version 2 private instance, supporting Shared VPC and external CMEK key](./data-solutions/composer-2/)
+- **data solutions** - [GCE/GCS CMEK via centralized Cloud KMS](./data-solutions/cmek-via-centralized-kms/), [Cloud Storage to Bigquery with Cloud Dataflow with least privileges](./data-solutions/gcs-to-bq-with-least-privileges/), [Data Platform Foundations](./data-solutions/data-platform-foundations/), [SQL Server AlwaysOn availability groups blueprint](./data-solutions/sqlserver-alwayson), [Cloud SQL instance with multi-region read replicas](./data-solutions/cloudsql-multiregion/), [Cloud Composer version 2 private instance, supporting Shared VPC and external CMEK key](./data-solutions/composer-2/)
 - **factories** - [The why and the how of resource factories](./factories/README.md)
 - **GKE** - [GKE multitenant fleet](./gke/multitenant-fleet/), [Shared VPC with GKE support](./networking/shared-vpc-gke/), [Binary Authorization Pipeline](./gke/binauthz/), [Multi-cluster mesh on GKE (fleet API)](./gke/multi-cluster-mesh-gke-fleet-api/)
 - **networking** - [hub and spoke via peering](./networking/hub-and-spoke-peering/), [hub and spoke via VPN](./networking/hub-and-spoke-vpn/), [DNS and Google Private Access for on-premises](./networking/onprem-google-access-dns/), [Shared VPC with GKE support](./networking/shared-vpc-gke/), [ILB as next hop](./networking/ilb-next-hop), [Connecting to on-premise services leveraging PSC and hybrid NEGs](./networking/psc-hybrid/), [decentralized firewall](./networking/decentralized-firewall)

blueprints/data-solutions/README.md

@@ -13,7 +13,7 @@ They are meant to be used as minimal but complete starting points to create actu
 
 ### Cloud Storage to Bigquery with Cloud Dataflow with least privileges
 
-<a href="./gcs-to-bq-with-least-privileges/" title="Cloud Storage to Bigquery with Cloud Dataflow with least privileges"><img src="./gcs-to-bq-with-least-privileges/diagram.png" align="left" width="280px"></a> This [blueprint](./gcs-to-bq-with-least-privileges/) implements resources required to run GCS to BigQuery Dataflow pipelines. The solution rely on a set of Services account created with the least privileges principle.
+<a href="./gcs-to-bq-with-least-privileges/" title="Cloud Storage to Bigquery with Cloud Dataflow with least privileges"><img src="./gcs-to-bq-with-least-privileges/images/diagram.png" align="left" width="280px"></a> This [blueprint](./gcs-to-bq-with-least-privileges/) implements resources required to run GCS to BigQuery Dataflow pipelines. The solution rely on a set of Services account created with the least privileges principle.
 <br clear="left">
 
 ### Data Platform Foundations

blueprints/data-solutions/gcs-to-bq-with-least-privileges/README.md

@@ -23,7 +23,7 @@ Whether you’re transferring from another Cloud Service Provider or you’re ta
 
 ## Architecture
 
-![GCS to BigQuery High-level diagram](diagram.png "GCS to BigQuery High-level diagram")
+![GCS to BigQuery High-level diagram](images/diagram.png "GCS to BigQuery High-level diagram")
 
 The main components that we would be setting up are (to learn more about these products, click on the hyperlinks):
 
@@ -61,11 +61,11 @@ __Note__: To grant a user a role, take a look at the [Granting and Revoking Acce
 Click on the button below, sign in if required and when the prompt appears, click on “confirm”.
 
 
-[![Open Cloudshell](shell_button.png)](https://goo.gle/GoDataPipe)
+[![Open Cloudshell](images/shell_button.png)](https://goo.gle/GoDataPipe)
 
 This will clone the repository to your cloud shell and a screen like this one will appear:
 
-![cloud_shell](cloud_shell.png)
+![cloud_shell](images/cloud_shell.png)
 
 Before you deploy the architecture, make sure you run the following command to move your cloudshell session into your service project:
 
@@ -87,7 +87,7 @@ Before we deploy the architecture, you will need the following information:
 
 2. In the editor, edit the terraform.tfvars.sample file with the variables you gathered in the step above.
 
-![editor](editor.png)
+![editor](images/editor.png)
 
 * a. Fill in __data_eng_principals__ with the list of Users or Groups to impersonate service accounts.
 
@@ -105,7 +105,7 @@ Before we deploy the architecture, you will need the following information:
 
 The resource creation will take a few minutes, at the end this is the output you should expect for successful completion along with a list of the created resources:
 
-![output](output.png)
+![output](images/output.png)
 
 __Congratulations!__ You have successfully deployed the foundation for running your first ETL pipeline on Google Cloud.
 
@@ -168,16 +168,16 @@ This command will start a dataflow job called test_batch_01 that uses a Dataflow
 
 The expected output is the following:
 
-![second_output](second_output.png)
+![second_output](images/second_output.png)
 
 Then, if you navigate to Dataflow on the console, you will see the following:
 
-![dataflow_console](dataflow_console.png)
+![dataflow_console](images/dataflow_console.png)
 
 This shows the job you started from the cloudshell is currently running in Dataflow.
 If you click on the job name, you can see the job graph created and how every step of the Dataflow pipeline is moving along:
 
-![dataflow_execution](dataflow_execution.png)
+![dataflow_execution](images/dataflow_execution.png)
 
 Once the job completes, you can navigate to BigQuery in the console and under __SERVICE_PROJECT_ID__ → datalake → person, you can see the data that was successfully imported into BigQuery through the Dataflow job.