kovagoadi/hunfabric
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

More updates 2

Browse Source
This commit is contained in:
Julio Castillo
2022-09-09 16:40:37 +02:00
parent 834c5d53c9
commit 0ee32be2fa
30 changed files with 126 additions and 124 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
blueprints/data-solutions/README.md
View File

@@ -8,34 +8,36 @@ They are meant to be used as minimal but complete starting points to create actu
### GCE and GCS CMEK via centralized Cloud KMS
<a href="./cmek-via-centralized-kms/" title="CMEK on Cloud Storage and Compute Engine via centralized Cloud KMS"><img src="./cmek-via-centralized-kms/diagram.png" align="left" width="280px"></a> This [example](./cmek-via-centralized-kms/) implements [CMEK](https://cloud.google.com/kms/docs/cmek) for GCS and GCE, via keys hosted in KMS running in a centralized project. The example shows the basic resources and permissions for the typical use case of application projects implementing encryption at rest via a centrally managed KMS service.
<a href="./cmek-via-centralized-kms/" title="CMEK on Cloud Storage and Compute Engine via centralized Cloud KMS"><img src="./cmek-via-centralized-kms/diagram.png" align="left" width="280px"></a> This [blueprint](./cmek-via-centralized-kms/) implements [CMEK](https://cloud.google.com/kms/docs/cmek) for GCS and GCE, via keys hosted in KMS running in a centralized project. The blueprint shows the basic resources and permissions for the typical use case of application projects implementing encryption at rest via a centrally managed KMS service.
<br clear="left">
### 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 [example](./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/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
<a href="./data-platform-foundations/" title="Data Platform Foundations"><img src="./data-platform-foundations/images/overview_diagram.png" align="left" width="280px"></a>
This [example](./data-platform-foundations/) implements a robust and flexible Data Foundation on GCP that provides opinionated defaults, allowing customers to build and scale out additional data pipelines quickly and reliably.
This [blueprint](./data-platform-foundations/) implements a robust and flexible Data Foundation on GCP that provides opinionated defaults, allowing customers to build and scale out additional data pipelines quickly and reliably.
<br clear="left">
### SQL Server Always On Availability Groups
<a href="./sqlserver-alwayson/" title="SQL Server Always On Availability Groups"><img src="https://cloud.google.com/compute/images/sqlserver-ag-architecture.svg" align="left" width="280px"></a>
This [example](./data-platform-foundations/) implements SQL Server Always On Availability Groups using Fabric modules. It builds a two node cluster with a fileshare witness instance in an existing VPC and adds the necessary firewalling. The actual setup process (apart from Active Directory operations) has been scripted, so that least amount of manual works needs to performed.
This [blueprint](./data-platform-foundations/) implements SQL Server Always On Availability Groups using Fabric modules. It builds a two node cluster with a fileshare witness instance in an existing VPC and adds the necessary firewalling. The actual setup process (apart from Active Directory operations) has been scripted, so that least amount of manual works needs to performed.
<br clear="left">
### Cloud SQL instance with multi-region read replicas
<a href="./cloudsql-multiregion/" title="Cloud SQL instance with multi-region read replicas"><img src="./cloudsql-multiregion/images/diagram.png" align="left" width="280px"></a>
This [example](./cloudsql-multiregion/) creates a [Cloud SQL instance](https://cloud.google.com/sql) with multi-region read replicas as described in the [Cloud SQL for PostgreSQL disaster recovery](https://cloud.google.com/architecture/cloud-sql-postgres-disaster-recovery-complete-failover-fallback) article.
<a href="./cloudsql-multiregion/" title="Cloud SQL instance with multi-region read replicas"><img src="./cloudsql-multiregion/diagram.png" align="left" width="280px"></a>
This [blueprint](./cloudsql-multiregion/) creates a [Cloud SQL instance](https://cloud.google.com/sql) with multi-region read replicas as described in the [Cloud SQL for PostgreSQL disaster recovery](https://cloud.google.com/architecture/cloud-sql-postgres-disaster-recovery-complete-failover-fallback) article.
<br clear="left">
### Data Playground starter with Cloud Vertex AI Notebook and GCS
<a href="./data-playground/" title="Data Playground project with Cloud Vertex AI Notebook, BigQuery and GCS"><img src="./data-playground/diagram.png" align="left" width="280px"></a>
This [example](./data-playground/) creates a [Vertex AI Notebook](https://cloud.google.com/vertex-ai/docs/workbench/introduction) running on a VPC with a private IP and a dedicated Service Account. A GCS bucket and a BigQuery dataset are created to store inputs and outputs of data experiments.
This [blueprint](./data-playground/) creates a [Vertex AI
Notebook](https://cloud.google.com/vertex-ai/docs/workbench/introduction)
running on a VPC with a private IP and a dedicated Service Account. A GCS bucket and a BigQuery dataset are created to store inputs and outputs of data experiments.
<br clear="left">