Without the recommended three years of practical experience
*Note: this article focuses on the Google Cloud Professional Data Engineer certification exam, which was valid until March 29, 2019. After that, there were some changes - they are described in the section "Additionally"*
Hoodie Google: yes. Serious facial expression: yes. Photo from the video version of this article .
Would you like to get a brand new sweatshirt like in my photo?
Or maybe you are interested in a certificate and you're trying to figure out how to get it?
Over the past few months, I have taken several courses and worked with Google Cloud in parallel - to prepare for the Professional Data Engineer exam. Then I went to the exam and passed it. A few weeks later the sweatshirt arrived - but the certificate came faster.
This article will provide some information that you may find helpful and the steps I took to get my Google Cloud Professional Data Engineer certification.
Translated to
Why should I get a Google Cloud Professional Data Engineer certification?
Data surrounds us, it is everywhere. Therefore, today there is a demand for specialists who know how to create systems that can process and use data. And Google Cloud provides the infrastructure to build these systems.
If you already have Google Cloud skills, how can you demonstrate them to a future employer or client? This can be done in two ways: by having a portfolio of projects or by getting certified.
The certificate tells potential clients and employers that you have certain skills and that you have put in the effort to get them officially approved.
This is also stated in the official description of the exam.
Demonstrate your ability to design and build data systems and machine learning models on the Google Cloud platform.
If you don't already have the skills, the Certification Tutorials will teach you everything you need to know about how to build top-notch data systems with Google Cloud.
Who needs to be certified as a Google Cloud Professional Data Engineer?
You've seen the numbers - cloud technologies are growing, they're with us for a long time. If you're not familiar with statistics, just trust me, the "clouds" are on the rise right now.
If you are already working as a data scientist, data scientist, machine learning engineer, or want to move into the data science industry, then the Google Cloud Professional Data Engineer certification is what you need.
The ability to use cloud technologies is becoming a mandatory requirement for all data professionals.
Do I need a certificate to be a professional in data science, data science or machine learning?
No.
You can use Google Cloud to work with data processing solutions without having a certificate.
A certificate is just one way to validate your skills.
How much does it cost?
The exam fee is $200. If you fill it up, you will have to pay again.
In addition, you will have to spend money on preparatory courses and using the platform itself.
Platform costs are charges for using Google Cloud services. If you are an active user, you are well aware of this. If you're new and just starting out with the tutorials in this article, you can create a Google Cloud account and get everything done for the $300 Google credits when you sign up.
We will move on to the cost of courses in just a moment.
How long is the certificate valid?
Two years. After this period, the exam must be taken again.
And since Google Cloud is constantly evolving, it is likely that certification requirements will change as well (this happened just when I started writing the article).
What do you need to prepare for the exam?
For professional-level certification, Google recommends having more than three years of industry experience and more than a year in developing and managing solutions using GCP.
I didn't have any of that.
The relevant experience was about six months in each case.
To fill the gap, I used several online learning resources.
What courses did I take?
If your case is similar to mine and you do not meet the recommended requirements, then you can take some of the courses below to improve your level.
These are the ones I used in preparation for certification. They are listed in order of passage.
For each, I indicated the cost, timing and usefulness for passing the certification exam.
Some of the great online learning resources I used to improve my skills before an exam are, in order: , , .
Cost: $49 per month (after 7-day free trial).
Time: 1-2 months, more than 10 hours per week.
Utility: 8 out of 10.
The course powered by Coursera and developed in partnership with Google Cloud.
It is divided into five nested courses, each of which is about 10 hours of study time per week.
If you are not familiar with data processing in Google Cloud, this specialization will just give you the skills you need. You will complete a series of practical exercises using an iterative platform called QwikLabs. Before that, there will be lectures by experts using the Google Cloud on how to use various services such as Google BigQuery, Cloud Dataproc, Dataflow and Bigtable.
Cost: at no extra charge.
Time: 1 week, 4-6 hours.
Utility: 4 out of 10.
A low usefulness rating does not mean that the course is generally useless - it is not at all. The only reason it's rated so low is that it's not geared towards the Professional Data Engineer certification (as the name suggests).
I took it to refresh my knowledge after completing the Coursera specialization as I used Google Cloud in some limited cases.
If you've worked with another cloud provider before or have never used Google Cloud, you might find this course helpful as it's a great introduction to the Google Cloud platform in general.
Cost: $49 per month (after 7-day free trial).
Time: 1-4 weeks, more than 4 hours per week.
Utility: 10 out of 10.
After passing the exam and reflecting on the courses I took, I can say that the Linux Academy Google Certified Professional Data Engineer was the most useful.
Video tutorials and (a great free learning resource that comes with the course) and practice exams make this one of the best courses I've ever taken.
I even recommended it as a reference in Slack notes for the team after the exam.
Notes in Slack
• Some exam questions weren't covered in the Linux Academy course, A Cloud Guru, or Google Cloud Practice exams (to be expected).
• One question had a graph of data points. The question was, what equation can they be grouped (for example, cos (X) or X² + Y²).
• Be sure to know the differences between Dataflow, Dataproc, Datastore, Bigtable, BigQuery, Pub/Sub and understand how they can be used.
• The two specific examples on the exam are the same as on the practice ones, although I didn't read them at all during the exam (the questions themselves were enough to answer).
• It is useful to know the basic syntax of SQL queries, especially for BigQuery questions.
• The practice exams in the Linux Academy and GCP courses are very similar in style to the questions in the exam - you should take them several times to find your own weaknesses.
• It must be remembered that dataproc works with Hadoop, Spark, Hive и Pigs.
• dataflow works with Apache Beam.
• Cloud Spanner is a database originally designed for the cloud, it is compatible with ACID and works anywhere in the world.
• It is useful to know the names of "oldies" - equivalents of relational and non-relational databases (for example, MongoDB, Cassandra).
• IAM roles are slightly different for services, but it would be nice to understand how to separate the ability for users to see data and design workflows (for example, in the Dataflow Worker role, you can design workflows, but you cannot see data).
For now, this is probably enough. Each exam will be held differently. The Linux Academy course will provide 80% of the required knowledge.
Cost: at no extra charge.
Time: 1–2 hours.
Utility: 5 out of 10.
These videos were recommended on the A Cloud Guru forums. Many of them are not related to the Professional Data Engineer certification, so I just picked the ones that sounded familiar to me.
When going through the course, some services may seem complicated, so it was nice to see how a particular service was described in just a minute.
Cost: $49 for certificate or free (without certificate).
Time: 1-2 weeks, more than six hours per week.
Utility: not evaluated.
I found this resource the day before my scheduled exam date. There was not enough time to go through it - hence the lack of a utility assessment.
However, after reviewing the course overview page, I can say that this is a great resource where you can repeat everything you learned about Data Engineering in Google Cloud and find your weaknesses.
I told about this course to one of my colleagues who is preparing for certification.
Cost: at no extra charge.
Time: unknown
Utility: not evaluated.
Another resource I stumbled upon after an exam. It looks comprehensive, but the presentation is rather short. Besides, it's free. You can refer to it between practice exams and even after certification to refresh your knowledge.
What did I do after the course?
As I neared the end of my courses, I booked the exam with a week's notice.
Having a deadline is a great motivation to review what you have learned.
I took the Linux Academy and Google Cloud practice exams several times until I started to consistently score over 95%.
First time passing the Linux Academy practice exam with a score of over 90%.
The tests for each of the platforms are similar; I wrote down and analyzed questions in which I constantly made mistakes - this helped to eliminate weaknesses.
During the exam itself, the topic was the development of data processing systems in Google Cloud using two examples (the content of the exam has changed since March 29, 2019). The entire exam had multiple choice questions.
The exam took two hours to complete and seemed about 20% harder than the practice exams I was familiar with.
However, the latter are a very valuable resource.
What would I change if I took the exam again?
More practice exams. More practical knowledge.
Of course, you can always prepare a little better.
The recommended requirements list more than three years of GCP experience, which I didn't have, so I had to deal with what I had.
Additionally
The exam was updated on March 29th. The material in the article will still provide a good basis for preparation, but it is important to note some changes.
Google Cloud Professional Data Engineer exam sections ()
1. Designing data processing systems.
2. Construction and support of data structures and databases.
3. Data analysis and machine learning connection.
4. Business process modeling for analysis and optimization.
5. Ensuring reliability.
6. Data visualization and decision support.
7. Design with a focus on safety and compliance.
Google Cloud Professional Data Engineer exam sections ()
1. Designing data processing systems.
2. Construction and operation of data processing systems.
3. Exploitation of machine learning models (most of the changes happened here) [NEW].
4. Ensuring the quality of decisions.
In version 2, sections 1, 2, 4, and 6 of version 1 are merged into sections 1 and 2, sections 5 and 7 are merged into section 4. Section 3 in version 2 has been expanded to cover all the new machine learning capabilities in Google Cloud.
These changes have occurred quite recently, so many training materials have not had time to update.
However, if you use the materials from the article, this should be enough to cover 70% of the necessary knowledge. I would also self-refer to the following topics (they appeared in the second version of the exam):
- (hardware developed by Google specifically for machine learning).
As you can see, the exam update is primarily due to the machine learning capabilities in Google Cloud.
Update as of 29.04.2019/XNUMX/XNUMX. I received a message from a Linux Academy course instructor (Matthew Ulasien).
Just for reference, we plan to update the Linux Academy Data Engineer course to reflect the new goals sometime in mid or late May.
After exam
After passing the exam, you will receive a “pass” or “fail” result. Practice exams advise aiming for a minimum of 70%, so I aimed for 90%.
After successfully passing the exam, you will receive an activation code by email along with the official Google Cloud Professional Data Engineer certificate. Congratulations!
The activation code can be used in the exclusive Google Cloud Professional Data Engineer store, where you can get a good deal: there are t-shirts, backpacks and sweatshirts (some may be out of stock by the time of delivery). I chose a sweatshirt.
Once certified, you can showcase your skills (officially) and get back to the job you do best: building systems.
See you in two years for re-certification.
P.S. Many thanks to the wonderful teachers of the above courses and for providing resources and time to study and prepare for the exam.
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Source: habr.com