Data Engineering with Google Cloud
The main goal of data engineering using Google Cloud is designing, constructing, and launching data processing systems. It entails gathering, transforming, and analyzing data at scale using tools like BigQuery, Dataflow, Dataproc, and Cloud Storage. Data engineers ensure that data pipelines are secure, scalable, and effective so that businesses can use data for machine learning and analytics.
- 10+ Courses
- 30+ Projects
- 400 Hours
Data Engineering with Google Cloud is suitable for the following target audiences:
Data Engineers: Data engineers are experts who want to learn more about creating and overseeing data pipelines on Google Cloud that guarantee effective data processing and storage.
Cloud Architects: Cloud architects are those in charge of creating cloud-based solutions who wish to focus on creating scalable data architectures on Google Cloud.
Data Scientists and Analysts: Using Google Cloud’s data engineering tools, analysts and data scientists aim to improve their capacity to prepare, manipulate, and analyze massive datasets.
Software Engineers: Developers of software who want to broaden their expertise to incorporate cloud-based data engineering for batch or real-time processing of large data sets.
IT Professionals: System engineers and IT administrators who wish to support organizational data strategies by comprehending and managing cloud-based data architecture.
Data Engineer: Data engineers work with Google Cloud’s data services, such as BigQuery and Dataflow, to design and maintain scalable data pipelines.
Cloud Data Architect: Developing and putting into practice cloud-based data solutions that provide effective processing, analysis, and storage on Google Cloud.
Big Data Engineer: Using Google Cloud tools to manage massive data processing, ensuring systems are dependable and performance-optimized.
Machine Learning Engineer: Working with data scientists to integrate Google Cloud’s AI and ML services while managing and preparing data for machine learning models.
Business Intelligence (BI) Developer: Converting unstructured data into insights with Google Cloud’s analytics technologies to aid in organizational decision-making.
Businesses in the USA and Canada that specialize in cloud data engineering include finance, healthcare, technology, retail, and entertainment. These positions provide competitive pay, chances for professional growth, and the ability to work on cutting-edge projects in the rapidly expanding field of cloud-based data processing.
“Are you prepared to investigate prospects in Data Engineering with Google Cloud? Speak with one of our knowledgeable staff members right now. They will offer tailored advice and information about our Data Engineering with Google Cloud Training. Take the first step towards a rewarding career in Data Engineering with Google Cloud technology. Get in touch with us right now!”
- An outline of the principles of cloud computing
- Overview of Google Cloud
- IAM (Identity & Access Management) with GCP project setup
- Overview of core services
- Using SDK and Cloud Shell
- Cloud storage (lifecycle management, buckets)
- Overview of BigQuery
- Unstructured versus structured data
- Partitioning and clustering data
- Data warehouse versus data lake
- Streaming versus batch data pipelines
- Google Cloud Pub/Sub messaging
- Methods for ingesting data
- Processing in real-time versus near-real-time
- Overview of ETL/ELT pipelines
- Basics of Apache Beam
- Making use of Google Cloud Dataflow
- Creation and modification of pipelines
- Triggers and windowing
- Pipelines for error management and monitoring
- BigQuery’s Advanced SQL
- Techniques for query optimization
- UDFs, or user-defined functions
- Utilizing huge datasets
- Best practices for data transformation and cleansing
- Overview of Cloud Composer (Apache Airflow)
- Pipeline scheduling
- Taking care of dependence
- Creating automated processes
- Observing and warning
- Roles and permissions in IAM
- Compliance and data encryption
- Data governance tactics
- Cloud Logging & Monitoring for Monitoring
- Optimization and cost control
- Getting Ready for the Google Professional Data Engineer Certification
- Capstone Project Presentation
- Portfolio and resume building guidance
Anyone with an interest in data engineering, particularly those with a background in data or basic programming.
Basic knowledge with SQL and databases. Knowledge of Python (useful but not required).
No, the course builds on the foundations of cloud computing.
- Platform for Google Cloud (GCP)
- BigQuery
- Dataflow
- Pub/Sub
- Composer for Clouds
Yes. The training includes practical labs, exercises and real-world projects to help you gain industry-relevant experience.
Yes. The course helps prepare learners for Google Cloud data engineering certifications and related cloud data roles.
You may pursue roles such as Data Engineer, Cloud Data Engineer, Big Data Engineer, ETL Developer, Analytics Engineer, and Data Platform Engineer.
Google Cloud provides scalable tools for data storage, processing, machine learning and analytics, enabling organizations to handle large volumes of data efficiently.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
You can register via our website https://checkmateittech.com/, or reach out to our support teams via phone, email, or WhatsApp. We’ll help you with batch schedules and payment options.
Email info@checkmateittech.com OR Call Us at +1-347-408-2054
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Job opportunities in USA and Canada
Data Engineer: Data engineers work with Google Cloud’s data services, such as BigQuery and Dataflow, to design and maintain scalable data pipelines.
Cloud Data Architect: Developing and putting into practice cloud-based data solutions that provide effective processing, analysis, and storage on Google Cloud.
Big Data Engineer: Using Google Cloud tools to manage massive data processing, ensuring systems are dependable and performance-optimized.
Machine Learning Engineer: Working with data scientists to integrate Google Cloud’s AI and ML services while managing and preparing data for machine learning models.
Business Intelligence (BI) Developer: Converting unstructured data into insights with Google Cloud’s analytics technologies to aid in organizational decision-making.
Businesses in the USA and Canada that specialize in cloud data engineering include finance, healthcare, technology, retail, and entertainment. These positions provide competitive pay, chances for professional growth, and the ability to work on cutting-edge projects in the rapidly expanding field of cloud-based data processing.
“Are you prepared to investigate prospects in Data Engineering with Google Cloud? Speak with one of our knowledgeable staff members right now. They will offer tailored advice and information about our Data Engineering with Google Cloud Training. Take the first step towards a rewarding career in Data Engineering with Google Cloud technology. Get in touch with us right now!”
