Update: As of June 2019: I am maintaining my collection of flowcharts here
I have no association with any training companies or third parties linking through to this post. These posts are freely available to help folks understand Google Cloud!
I’m a big fan of flowcharts and back in November 2017 I wrote a post collating the GCP flowcharts that I was of aware that existed then (as well as adding one of my own). It’s an insanely popular post ( Thank you readers) as who doesn’t like flowcharts after all 😃 I promised that I would add more to the collection for you when I had enough to add to the original and so here you are. Part III of the collection can be found here
Attribution: All graphics & flowcharts ( apart from the choosing size & scope of K8s cluster flowchart & the modified data transfer flowchart) cheerfully copied from the Google Cloud platform or blog site
How to select the appropriate way to transfer data sets to GCP for your use case
Transferring large data sets to GCP ( or indeed any cloud) means that you have to consider two initial questions How much data do you need to transfer? and how long have you got to get that data to GCP? In this case we are really focusing on getting large volumes of data to Cloud Storage. This then leads onto the other questions that you need to consider to allow you to determine what transfer method may meet your use case . How are you connected to GCP? How much bandwidth is actually available between your source and GCP? The article on Transferring big data sets to GCP discusses the information you need to determine the connectivity required and what methods to choose. It has a flowchart and the one below is a slightly modified version of the one found in the article.
What annotations(labels) should you use for which use case ?
GCP has a number of ways of annotating or labelling( this can get slightly over loaded hence the use of the word annotation) resources. Each annotation has different functionality and scope, they are not mutually exclusive and you will often use a combination of them to meet your requirements so I wrote a post with added flow chart to help you navigate which annotation(s) to use for what use case. Here’s the flow chart :
Read the post for the words .
Choosing the floating IP address pattern that maps to your use case
Floating IP’s are a way to allow you to move an IP address from one server to another . Typically this pattern is usually required for HA deployments or for disaster recovery scenarios. For example where you have one active server or appliance such as databases with a non serving replica /hot standby . When you have to swap to the secondary server you point the floating IP to it. This negates the need to update clients to use an alternative IP to point to the alternative server. The article On best practices for floating IP addresses has a list of uses cases for on premises and provides a number of options for implementing the pattern for Compute engine instances and yes has a flowchart to help you choose the solution for your use case . Here’s the flow chart
Sizing & scoping GKE clusters to meet your use case
Determining the number of GKE ( Google kubernetes engine) clusters and the size of the clusters required for your workloads requires looking at a number of factors. The article Choose size and scope of Kubernetes engine discusses these factors. Alas it’s sadly lacking a flowchart so I’ve addressed that for you ( maybe at some point the article will include a flowchart ) . I know it seems I have created 2 mini charts but then it was a post about sizng & scoping your GKE clusters !
The words discussing the decision points are all in the article
