Data management, machine learning, artificial intelligence, data analytics, etc., are becoming vital elements of an organization’s tech infrastructures across industries. Are you looking to tap into these domains and be a part of growing businesses as a data engineer? The best way is to upskill yourself with leading certifications and make your resume shine from your peers.
Speaking of which, Google Certified: Professional Data Engineer is one of the most sought-after certifications that not only help you take the next step in the cloud career but also validate your knowledge of some of the most demanding skills in the industry.
However, qualifying for this cert would need to dive deeper into data engineering concepts and machine learning. While theory will help familiarize you with the subject, you still need a grip on the real-world challenges of these elements. That’s where hands-on labs come in. This blog will help you discover some of the top GCP data engineer hands on labs.
Time to dig in!
Overview of Google Certified: Professional Data Engineer
The Google Certified: Professional Data Engineer imparts skills to make informed, data-driven decisions by gathering, converting, and publishing data. You will learn how to design, develop, operationalize, protect, and monitor data processing systems. Besides, the cert especially emphasizes on:
- Security and Compliance
- Fidelity and reliability
- Flexibility and portability
GCP Data Engineer hand on labs can help you to familiarize yourself with deploy, leverage, and train already existing machine learning models. The cert exam will test your ability to perform mission-critical tasks like:
Read More: Free Questions & Answers on Google Cloud Certified Professional Data Engineer Exam
- Create data processing systems
- Process data streaming in real time
- Operationalize models for machine learning
- Store and access data in cloud infra
- Ensure quality of solutions
- Build data processing systems
Apart from this, taking the GCP Data Engineer hands-on labs will help you:
- Gain in-demand skills preferred across industries. Become a sought-after GCP professional skilled in secure and efficient data processing system design, building, operationalization, and monitoring. Master how to deploy, train, and use machine learning models for business apps.
- Learn the data-driven approach to logging success. Develop real-time app decision-making skills by gathering, converting, and publishing data.
- Advance the career ladder. Enhance your market value, unlock better career opportunities, and secure higher salary prospects.
Salary for Google Cloud Professional Data Engineers
The google certified professional data engineer’s salary can vary depending on a number of variables, including their level of experience, location, and the business or sector that they work in. Here are some typical salary ranges for professional data engineers working for Google Cloud:
Depending on their region and industry, entry-level Google Cloud Professional Data Engineers might get an average salary of $80,000 to $100,000 annually.
Again, depending on their region and business, mid-level Google Cloud Professional Data Engineers with many years of experience might anticipate getting an average pay of between $100,000 and $150,000 annually.
Senior-level Google Cloud Professional Data Engineers can earn an average income of roughly $150,000 to $200,000 per year with sufficient experience and skill.
Job opportunities for Google Cloud Professional Data Engineers
The requirement for Google Cloud Professional Data Engineers is rising as more businesses adopt cloud-based solutions for their information processing requirements. Some of the job openings for Google Cloud Professional Data Engineers are listed below:
Data Engineer: As a Google Cloud Professional Data Engineer, you may be in charge of planning and constructing Google Cloud Platform data processing systems. In order to recognize requirements and execute data solutions, they collaborate with other data experts, including data scientists and analysts.
Data Analyst: Google Cloud Professional Data Engineers may be employed by some businesses to serve in the capacity of data analysts, who are tasked with analyzing data to find trends, patterns, and insights that may be applied to inform business choices.
Cloud Architects: Those who are Google Cloud Professional Data Engineers possess the skills and expertise to take on the role of designing and implementing cloud-based solutions for organizations. In this capacity, they are responsible for selecting the most suitable Google Cloud Platform services and configuring them to meet the specific needs of the organization.
Machine Learning Engineers: In addition, as Google Cloud Professional Data Engineers, they can also work as Machine Learning Engineers. In this role, they are entrusted with the responsibility of designing and constructing machine learning models on the Google Cloud Platform. Collaborating closely with data scientists, they identify the appropriate machine learning algorithms and develop the required infrastructure to support these models effectively.
Exam format for GCP Data Engineer Certification
GCP Data Engineer hands-on labs
GCP Data Engineer Hands-on labs allow you to play around with demo Google environments right in your browser. They are perfect for mastering your skills and techniques to ace the cert exam. Here are a few top labs for the data engineer cert.
Relying on theory alone isn’t sufficient to be fully skilled in handling Google data engineer domains. Real-world scenarios can appear different from what you read in theory. So it’s important you apply your knowledge in practical situations. That’s why Google Cert has some awesome hands-on labs created by industry pros.
1. Cloud SQL Migration using Database Migration Service
This lab will lead you through utilizing Database Migration Service to migrate a Google Cloud SQL instance.
Task Details
- Establishing a database and SQL instance on Google Cloud.
- Creating a table in the Google Cloud SQL Database and adding data to it.
- Make a migration job for the SQL instance on Google Cloud.
- Testing the migration for the SQL instance in the Google Cloud.
2.Creating views in BigQuery
This lab walks you through different types of views in BigQuery.
Task Details
- Make a table and a bigquery dataset.
- Data loading using an external CSV.
- View creation, view authorization, and view materialization.
3. How to use the bq tool for BigQuery
This lab explains how to use the bq command-line tool.
Task Details
- Establishing a Private Dataset.
- Updating the Table with new data.
- SQL Query for reading data from the Table.
- Transferring table data to a bucket of Google Cloud Storage.
- Cleaning up the resource usage.
4. Partitioning Vs. Clustering in BigQuery
This lab guides you through creating effective BigQuery queries using strategies such as partitioning and clustering.
Task Details
- Establish a BigQuery Dataset.
- Conducting partitioning in the table.
- Cluster data in the table.
5. Basic SQL Functions in BigQuery
You will discover how to use BigQuery’s fundamental SQL features in this lab.
Task Details
- Constructing a BigQuery data collection.
- Establishing a table.
- Executing simple SQL queries against the table.
6. Streaming Cloud SQL Data into BigQuery in near real-time
We will connect Cloud SQL and BigQuery to analyze data in this lab.
Task Details
- Make a database and a cloud SQL instance.
- Manually enter the data into the database.
- Establish a connection between Cloud SQL and BigQuery.
- Use BigQuery to run simple queries and scheduled queries.
7. Create Batch Flow using GCS, Dataflow, and BigQuery
This lab demonstrates how to create a batch workflow or pipeline using bigquery, cloud dataflow, and cloud storage.
Task details
- Create a Bucket and add the necessary files to it.
- BigQuery Dataset and Table Creation.
- Design Batch Pipeline from Dataflow
- Data analysis using BigQuery
8. Design a Composer Environment and navigating in the Airflow UI
You will learn how to use Google Cloud Composer in this lab.
Task Details
- Create an environment for Cloud Composer to be created.
- Moving to the Airflow UI.
9. Using Airflow DAG to print Hello World
In this lab, you will learn how to use Google Cloud Composer to make DAGs.
Task Details
- The setting up of a Cloud Composer setup.
- Accessing the Airflow UI.
- To print Hello World, create a DAG.
FAQs
What are the prerequisites of the Google Certified: Professional Data Engineer certification?
To be eligible for the Google data engineer cert, you need more than three years of industry experience, including over one year of experience in managing and designing solutions of Google Cloud. Also, you must have:
- Basic understanding of the Google Cloud Platform, notably in the areas of computing, storage, and security
- Basic coding skills (Python or Go)
- Basic knowledge of machine learning concepts
- Familiarity with databases and how they work
Who can take the Google certified: Professional Data Engineer certification?
You are eligible to take the exam if you are:
- Data Engineer
- Data scientist
- Cloud Architect
- Entry-level professional familiar with GCP
What are the domains for the Google certified: Professional Data Engineer cert?
- Design data processing system
- Operationalize data processing systems
- Operationalize machine learning models
- Enhance solution quality
How difficult is Google Professional Data Engineer certification?
It isn’t that difficult if you are focused in studying and gone through the practice tests.
Is GCP professional Data Engineer certification worth it?
Yes! Google Professional Data Engineer Certification is a valuable one which enhances your data engineering skills and advance your career.
Summary
This blog aims to provide you with a better understanding of the hands-on labs for the Google Certified Professional Data Engineer certification. However, please note that the labs mentioned here are just a selection; more are available. Make sure to explore and make the most of these demo Google environments.
However, when it comes to overall preparation for the Google data engineer certification, it is crucial to have a solid foundation of theoretical knowledge. To achieve this, it is recommended to utilize updated and reliable resources. Besides hands-on labs and Google Sandbox, Whizlabs offers comprehensive exams with numerous practice questions, round-the-clock support from domain experts, and unlimited access to exclusive resources.
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