Professional Cloud Network Engineer (PCNE)
Next, let’s look at a specialized profession: the Professional Cloud Network Engineer.
Do you know what it’s like to be a Professional Cloud Network Engineer?
According to Google, you’ll be working with architects who develop the infrastructure to execute and maintain Google Cloud Platform network designs. This person provides effective cloud deployments utilizing the command line interface or the Google Cloud Platform Console by using expertise creating VPCs, hybrid connectivity, network services, and security for existing network architectures.”
Before we continue, I’d want to address that final sentence. It’s vital to remember that both the position and the test need the ability to operate with both the command line and the web console UI. However, I’d want to draw attention to what’s missing: In contrast to previous networking expert professions, this one doesn’t include any physical hardware. Software-defined networking (SDN) is the focus here. Isn’t that a Cloud Network Engineer?
How to ace the Professional Cloud Network Engineer certification exam
When it comes to developing, planning, and prototyping networks in GCP, this position clearly includes the “get your hands dirty and do it” kind of individual rather than the “sit back and figure it out” type. In the test guide, that’s the first domain mentioned.
However, the rest of the domains are primarily concerned with day-to-day implementation and management:
A VPC (Virtual Private Cloud) network is required to link everything together.
Many things that connect to VPC’s network, such as VMs, GKE clusters, and corporate data centers, do the same.
This Cloud Network Engineer is a step up from the Cloud Security Engineer in terms of networking expertise, but that’s not all. Internet Protocol (TCP/IP), SSL (HTTPS), GKE IPs (NAT), VLANs (VLAN), IPSec (VPN), SSH (CDN), BBQ (BBQ), LOL OK, I’ll take responsibility for the final two. On the other hand, it’s important to pay attention to the fine print.)
As a result, this function is also engaged in certain aspects of identity management (IAM). Remember, this is a software-defined network. Thus all access control is handled centrally. Many old-school networking people who migrate to the Google Cloud find it a delight to work with these wonderful new gadgets that enhance their influence.
Professional Data Engineer (PDE)
On to the defining characteristics of a Professional Data Engineer (PDE).
Do you know what it means to be a Professional Data Engineer?
Data is the first step, isn’t it? However, the question that has to be asked is “Why?” What’s the point of having too much information? “A Professional Data Engineer helps data-driven decision making by gathering, processing, and disseminating data,” Google adds.
There you have it: “data-driven decision making.” As a sector, we’ve learned that data has a wealth of commercial value that must be unlocked and made available to the rest of the company.
As they say, “A Data Engineer should be able to design and implement data processing systems that are reliable, efficient, and secure as well as flexible so that they may be used in a variety of environments.”
Many “ilities” there. Because compromises are at the heart of every IT function, this one is no exception.
When working with sensitive information, you have to be aware of privacy and security problems, which is like a Security Engineer. However, as we previously said, everyone has a role to play in ensuring the safety of themselves and their loved ones. With this Professional Data Engineer post, you’ll be responsible for designing all Big Data systems across the whole data lifecycle, from ingesting, to processing, to analysis to exploration and visualization.
How to become a certified Professional Data Engineer
DataFlow, DataProc, BigTable, and BigQuery are some of the most important GCP products for this function and the corresponding Google Cloud certification.
DataPrep, DataLab, Data Studio, Cloud Storage, Cloud SQL, and Cloud Pub/Sub are all equally significant, but that doesn’t mean the others aren’t. Anything that serves as a data repository or processing center. Setting these things up, though, isn’t all that’s required here. These pipelines must be monitored and maintained and debugged, and improved over time.
This function will also need the usage of non-Google technology, such as Hadoop for MapReduce-processing massive datasets and SQL for creating all kinds of data queries, so it’s worth noting here.
A Data Engineer should be able to use, deploy and constantly train pre-existing machine learning models,” says Google in their description of this profession.
Even if there’s a separate certification for Machine Learning, this one clearly encompasses it. If you want to use GCP’s pre-trained models, including Vision API and Translation API, you’ll need to familiarize yourself with them. However, you should feel at ease using an AI platform to train your models. “Auto” APIs like AutoML Vision and Tables, where Google performs most of the hard work, but you still get to participate in training, are also available.
Designing your bespoke models from the ground up to tackle new challenges is not included in this scope; it is the purview of the specialized Machine Learning Engineer.