The Syllabus
Curriculum Designed by Experts
- Creating and managing clusters.
- Leveraging custom machine types and preemptible worker nodes.
- Scaling and deleting Clusters.
- Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
- Running Pig and Hive jobs.
- Separation of storage and compute.
- Lab: Running Hadoop and Spark Jobs with Dataproc.
- Lab: Submit and monitor jobs.
- Customize cluster with initialization actions.
- BigQuery Support.
- Lab: Leveraging Google Cloud Platform Services.
- Google’s Machine Learning APIs.
- Common ML Use Cases.
- Invoking ML APIs.
- Lab: Adding Machine Learning Capabilities to Big Data Analysis.
- What is BigQuery.
- Queries and Functions.
- Lab: Writing queries in BigQuery.
- Loading data into BigQuery.
- Exporting data from BigQuery.
- Lab: Loading and exporting data.
- Nested and repeated fields.
- Querying multiple tables.
- Lab: Complex queries.
- Performance and pricing.
- The Beam programming model.
- Data pipelines in Beam Python.
- Data pipelines in Beam Java.
- Lab: Writing a Dataflow pipeline.
- Scalable Big Data processing using Beam.
- Lab: MapReduce in Dataflow.
- Incorporating additional data.
- Lab: Side inputs.
- Handling stream data.
- GCP Reference architecture.
- What is machine learning (ML)
- Effective ML: concepts, types.
- ML datasets: generalization.
- Lab: Explore and create ML datasets.
- Â
- Getting started with TensorFlow.
- Lab: Using tf.learn.
- TensorFlow graphs and loops + lab.
- Lab: Using low-level TensorFlow + early stopping.
- Monitoring ML training.
- Lab: Charts and graphs of TensorFlow training.
- Why Cloud ML?
- Packaging up a TensorFlow model.
- End-to-end training.
- Lab: Run a ML model locally and on cloud.
- Creating good features.
- Transforming inputs.
- Synthetic features.
- Preprocessing with Cloud ML.
- Lab: Feature engineering.
- Stream data processing: Challenges.
- Handling variable data volumes.
- Dealing with unordered/late data.
- Lab: Designing streaming pipeline.
- What is Cloud Pub/Sub?
- How it works: Topics and Subscriptions.
- Lab: Simulator.
- Challenges in stream processing.
- Handle late data: watermarks, triggers, accumulation.
- Lab: Stream data processing pipeline for live traffic data.
- Streaming analytics: from data to decisions.
- Querying streaming data with BigQuery.
- What is Google Data Studio?
- Lab: build a real-time dashboard to visualize processed data.
- What is Cloud Spanner?
- Designing Bigtable schema.
- Ingesting into Bigtable.
- Lab: streaming into Bigtable.
Enquire Now
Why Radical Technologies
Live Online Training
Live Classroom Training
Self Paced Training
Skills Covered
Like the Curriculum ? Let's Get Started
Why Enroll for GCP Data Engineer ?
In-Demand Skills
Looking to excel in data engineering? Enroll in our GCP Data Engineer Course Certification Training to master in-demand skills like BigQuery, Dataflow, and Cloud Storage. Discover how to use Google Cloud for designing, managing,and optimizing data systems. Boost your career with GCP's industry-recognized credentials.
Career Opportunities
Unlock exciting career opportunities with GCP Data Engineer Course Certification Training! Master cloud technologies like BigQuery and Dataflow. High-demand roles include Data Engineer, Cloud Architect, and Data Analyst. Enroll now to advance your career in Google Cloud's data ecosystem!
Cloud Adoption
Accelerate cloud adoption with GCP Data Engineer Course Certification Training! Master essential tools like BigQuery, Dataflow, and Pub/Sub to streamline data processing and storage. Enhance your skills to design scalable data solutions in Google Cloud. Enroll today and lead in the cloud-first world of tomorrow!
Scalability and Flexibility
Master scalability and flexibility with GCP Data Engineer Course Certification Training! Learn to design dynamic, scalable data systems using tools like BigQuery and Dataflow. Optimize performance, reduce costs, and enhance cloud infrastructure. Enroll now to lead in Google Cloud’s flexible data solutions!
Cost Management
Managing costs for GCP Data Engineer Course Training is crucial. Our course offers practical cost-saving strategies for cloud resources, ensuring efficient spending. Enroll today for certification and master Google Cloud, optimizing data engineering with expert guidance and real-world experience.
Security and Compliance
Security and compliance are essential in GCP Data Engineer Course Training. Learn how to secure data pipelines and adhere to industry regulations. Enroll now for certification and gain hands-on skills to ensure your Google Cloud projects meet security standards while staying compliant.
Course benefits
Who Can Apply for Red Hat Linux
- Information Architects and Statisticians
- Developers looking to master Machine Learning and Predictive Analytics
- Big Data, Business Analysis, Business Intelligence, and Software Engineering Professionals
- Aspirants who are looking to work as Machine Learning Experts, Data Scientists, etc.
- Anyone who wants to learn machine learning, artificial intelligence, data visualization, data analytics, data structures, and algorithms (DSA).
Why GCP Data Engineer ?
Scalability
Flexibility
Hybrid Capabilities
Security and Compliance
Cost-Effectiveness
Innovation
Global Certification
Red Hat Linux Fees in Bangalore
Online Classroom PREFERRED
- Live Classes from IIT Faculty & Industry Experts
- Certification from IHUB IIT Roorkee
- Career Services (Mock Interviews, Resume Preparation)
- Placement Assistance upon clearing PRT
- Dedicated Learning Manage
16 jul
20 jul
20 jul
Online Classroom PREFERRED
- Live Classes from IIT Faculty & Industry Experts
- Certification from IHUB IIT Roorkee
- Career Services (Mock Interviews, Resume Preparation)
- Placement Assistance upon clearing PRT
- Dedicated Learning Manage
career services
At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalized resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career. Join us and let your journey to a successful future begin with the right support.
At Radical Technologies, we ensure you’re ready to shine in any interview. Our comprehensive Interview Preparation program includes mock interviews, expert feedback, and tailored coaching sessions to build your confidence. Learn how to effectively communicate your skills, handle technical questions, and make a lasting impression on potential employers. With our guidance, you’ll walk into your interviews prepared and poised for success.
At Radical Technologies, we believe that a strong professional profile is key to standing out in the competitive IT industry. Our Profile Building services are designed to highlight your unique skills and experiences, crafting a resume and LinkedIn profile that resonate with employers. From tailored advice on showcasing your strengths to tips on optimizing your online presence, we provide the tools you need to make a lasting impression. Let us help you build a profile that opens doors to your dream career.
Red Hat Linux Course Projects
Infrastructure Provisioning
Implementing automated infrastructure provisioning and configuration management using Ansible. This may include setting up servers, networking devices, and other infrastructure components using playbooks and roles.Â
Applications Deployment
Automating the deployment and orchestration of applications across development, testing, and production environments. This could involve deploying web servers, databases. middleware, and other application components using Ansible
Continuous Integration
Integrating Ansible into CI/CD pipelines to automate software. build, test, and deployment processes. This may include automating the creation of build artifacts, running tests, and deploying applications to various environments.
- Course Completed ?
- Need Interview Supports?
- Need Job Assistance?
- Came from Any Other Institute ?
- Join our Brush up Session & Get Support until You find a Job!
Radical Learning Eco-System
Exam simulator
Cloud Send Borey
Hands - on Cloud Lab
Developer Coding Ground
Testimonials
Our Alumni
GCP Data Engineer FAQs
A GCP Data Engineer designs, builds, and manages data processing systems on the Google Cloud Platform (GCP). Their responsibilities include creating scalable data pipelines, optimizing performance, ensuring data security, and integrating machine learning models to transform raw data into actionable insights for businesses.
To become a GCP Data Engineer, you need strong skills in cloud computing, particularly in GCP services like BigQuery, Cloud Dataflow, and Pub/Sub. Proficiency in SQL, Python, and data engineering concepts like ETL processes, data warehousing, and real-time analytics is also essential. Experience with data security and compliance is valuable.
Common GCP services used by data engineers include BigQuery for data warehousing, Cloud Dataflow for real-time and batch processing, Pub/Sub for messaging, Cloud Dataproc for running Apache Spark and Hadoop jobs, and Cloud Storage for scalable storage solutions. Machine learning integration is done using AI Platform and TensorFlow.
Cloud Dataflow is a fully managed service for stream and batch data processing, ideal for handling real-time data flows. Cloud Dataproc, on the other hand, is a managed service for running Hadoop and Spark jobs, which is typically used for big data processing workloads. Both services are scalable but are used for different types of data processing.
BigQuery is a fully managed, serverless data warehouse that allows users to query large datasets using SQL. It is highly scalable and optimized for analytical queries, enabling fast performance even with petabyte-scale data. BigQuery also integrates with other GCP services for advanced data processing and machine learning.
GCP Data Engineers ensure data security by implementing access controls through IAM (Identity and Access Management), encrypting data both at rest and in transit, and setting up VPC Service Controls to limit data exfiltration. They also use audit logs to monitor and track access to sensitive data, ensuring compliance with security regulations.
ETL (Extract, Transform, Load) processes are crucial for transforming raw data into a usable format. In GCP, data engineers design automated ETL pipelines using tools like Cloud Dataflow and Cloud Composer. These pipelines extract data from various sources, transform it for analysis or storage, and load it into destinations such as BigQuery.
GCP Data Engineers use scalable services like BigQuery and Cloud Storage to manage large datasets. They optimize data storage with partitioning and clustering in BigQuery, use data compression techniques, and design efficient data pipelines to handle large-scale ingestion and processing without compromising performance.
Cloud Pub/Sub is a messaging service that enables real-time, asynchronous communication between independent applications. It is used in data pipelines to collect and distribute data streams from various sources, allowing systems to process data in real-time for tasks like monitoring, alerting, or analytics.
GCP Data Engineers integrate machine learning models using AI Platform and TensorFlow. They build pipelines that preprocess data and feed it into machine learning models for training or inference. These models are then deployed for tasks such as predictive analytics or automated decision-making, making data pipelines intelligent.
Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It allows GCP Data Engineers to automate, schedule, and monitor complex workflows, including ETL pipelines and data processing jobs. Cloud Composer helps ensure that data workflows run efficiently and at the right time.
GCP Data Engineers ensure compliance with GDPR and other regulations by implementing strict access controls using IAM, encrypting sensitive data, and setting up policies for data retention and deletion. They also use GCP’s built-in security features to monitor data access and ensure that data handling adheres to privacy standards.
Best practices for optimizing BigQuery performance include using partitioning and clustering to organize large tables, avoiding SELECT *, using query execution plans to identify bottlenecks, and storing frequently accessed data in cached results. GCP Data Engineers also ensure efficient schema design and optimize data ingestion processes.
Cloud Dataprep is a data preparation tool that allows GCP Data Engineers to visually explore, clean, and transform raw data without writing code. It integrates with various GCP data services and simplifies the ETL process, ensuring that data is in the right format and quality for analysis or further processing in BigQuery or other services.
In multi-cloud or hybrid cloud environments, GCP Data Engineers design architectures that integrate data across different cloud platforms and on-premise systems. They use tools like Anthos to manage data portability and interoperability, ensuring that data flows seamlessly between environments while maintaining security and performance standards.
Online Batches Available for the Areas
Basavanagudi | HSR Layout | Sadashivanagar | Jayanagar | Koramangala | Whitefield | Banashankari | Marathahalli | BTM Layout | Electronic City | Rajajinagar | Domlur | Indiranagar | Malleshwaram | Yelahanka | Cooke Town | Nagarbhavi | Bannerghatta Road | Chandapura | Dasarahalli | Devanahalli | Anandnagar | Avenue Road | Byatarayanapura
Most Probable Interview Questions Asked for GCP Data Engineer
Like the Curriculum ? Let's Get Started
Your Seniors Got Placed
About GCP Data Engineer Certification
The Google Cloud Professional Data Engineer Certification is designed for professionals who want to advance their skills in designing, building, and managing data systems on Google Cloud Platform (GCP). This certification validates your ability to handle large datasets, optimize data flows, ensure data security, and apply machine learning models on GCP. With a focus on data processing, storage, and analysis, this certification is a gateway to mastering Google Cloud’s data-centric tools, equipping you with the skills needed to make data-driven decisions and deliver business insights at scale.
Why Choose GCP Data Engineer Certification?
In today’s data-driven landscape, companies across industries rely on real-time analytics, machine learning, and massive-scale data pipelines to make informed decisions. By earning the GCP Data Engineer Certification, you demonstrate proficiency in using Google’s cutting-edge tools such as BigQuery, Cloud Storage, Pub/Sub, Dataflow, and Dataproc to transform data into actionable insights. This certification not only enhances your ability to work with big data solutions but also positions you as a key player in cloud-based data infrastructure, a critical asset for businesses seeking innovation.
Key Learning Objectives of GCP Data Engineer Certification
- Designing Data Processing Systems: Learn how to architect data processing systems that are secure, reliable, and scalable using GCP tools.
- Building and Optimizing Data Pipelines: Master how to design and implement data pipelines that manage massive datasets and ensure high-quality data flow.
- Data Transformation and Analysis: Gain the skills to apply ETL (Extract, Transform, Load) processes to convert raw data into structured, usable information for analysis.
- Machine Learning Integration: Learn to integrate machine learning into your data systems to predict trends and derive valuable insights using services like AI Platform.
- Data Storage Solutions: Explore the variety of GCP’s managed storage options, including Cloud Bigtable and Cloud SQL, to store and retrieve structured and unstructured data effectively.
- Ensuring Data Security and Compliance: Learn how to implement security measures and ensure compliance with legal and regulatory requirements for data governance.
- Monitoring and Optimizing Performance: Get equipped with tools for monitoring, troubleshooting, and optimizing the performance of data systems in real-time.
Career Opportunities and Growth
By earning the GCP Data Engineer Certification, you open doors to high-demand roles such as:
- Data Engineer
- Big Data Engineer
- Cloud Architect
- Machine Learning Engineer
- Analytics Engineer
With the rise of cloud-native solutions, organizations across all sectors are actively seeking certified professionals to help manage their data infrastructure. Certified GCP Data Engineers are among the highest-paid professionals in the industry, with companies like Google, Amazon, and IBM prioritizing cloud experts who can handle complex data operations.
Exam Structure and Preparation
The GCP Data Engineer exam assesses your proficiency in real-world tasks related to data engineering on GCP. The certification exam is multiple-choice, and you have two hours to complete it. The topics include data system design, data pipeline optimization, machine learning integration, and performance monitoring. To prepare, candidates are encouraged to:
- Enroll in GCP Data Engineer-focused courses and labs.
- Access Google’s official certification study guide and exam practice questions.
- Participate in hands-on labs with Qwiklabs to get real-world experience.
Why This Certification Matters
As data continues to grow exponentially, organizations require cloud professionals capable of processing, analyzing, and transforming vast amounts of information quickly. The GCP Data Engineer Certification equips you with the expertise to help companies harness the full power of data to drive business outcomes. With a focus on scalability, security, and real-time analytics, this certification makes you an indispensable part of any data-driven organization.
Applications of GCP Data Engineer
The role of a Google Cloud Platform (GCP) Data Engineer extends across various industries and sectors, where data processing, management, and analytics are critical to decision-making. As businesses increasingly adopt cloud-based solutions, GCP Data Engineers play a pivotal role in building, maintaining, and optimizing data pipelines. Here are some key applications of GCP Data Engineers:
1. Real-Time Data Analytics
One of the primary applications of GCP Data Engineers is in real-time data analytics. By leveraging tools like Cloud Pub/Sub and Cloud Dataflow, GCP Data Engineers can design systems that handle streaming data from various sources, such as IoT devices, social media platforms, or financial transactions. These systems enable businesses to process and analyze data in real-time, delivering immediate insights and actionable information. Industries like finance, retail, and healthcare benefit from real-time analytics for fraud detection, inventory management, and patient monitoring, respectively.
2. Big Data Processing
GCP Data Engineers are responsible for designing and implementing big data solutions that handle massive datasets. Using services such as BigQuery for data warehousing and Cloud Dataproc for distributed data processing, they create scalable, cost-efficient systems that can store, process, and query large amounts of structured and unstructured data. Big data processing is essential for industries like e-commerce, telecommunications, and media, where huge volumes of data must be processed to extract meaningful insights.
3. Data Pipelines for Machine Learning
A key responsibility of GCP Data Engineers is to build data pipelines that feed into machine learning models. By using Google Cloud AI Platform and TensorFlow, they create systems that ingest raw data, perform transformations, and prepare datasets for training machine learning models. This application is crucial in fields like healthcare, where machine learning models can predict disease outcomes, or in e-commerce, where personalized product recommendations are generated based on customer behavior. GCP Data Engineers ensure that these pipelines run efficiently, allowing data scientists to focus on model development.
4. Data Warehousing and Business Intelligence
GCP Data Engineers are also heavily involved in building and optimizing data warehouses. With BigQuery, they design highly scalable and efficient data storage systems that allow businesses to perform complex SQL queries on large datasets. This application is critical for business intelligence (BI) purposes, where organizations need to analyze past performance, track key performance indicators (KPIs), and generate reports that drive business decisions. Industries such as finance, retail, and manufacturing rely on these data warehousing solutions to gain insights into customer behavior, market trends, and operational efficiency.
5. ETL (Extract, Transform, Load) Operations
ETL is a core application of GCP Data Engineers, where they design and implement systems that extract data from various sources, transform it into a suitable format, and load it into a destination system like a data warehouse. Using Cloud Dataflow and Cloud Composer, GCP Data Engineers build automated ETL pipelines that handle diverse data types and formats. This ensures that the data is clean, consistent, and ready for analysis. ETL operations are used in industries like finance for regulatory reporting, or in retail for integrating sales data from multiple sources.
6. Data Security and Compliance
Data security and regulatory compliance are major concerns for organizations, especially in industries such as finance, healthcare, and government. GCP Data Engineers design systems with robust security measures, including encryption, access controls, and audit logs. By leveraging GCP’s built-in security features, such as Cloud Identity & Access Management (IAM) and VPC Service Controls, they ensure that sensitive data is protected from unauthorized access. Moreover, GCP Data Engineers implement data governance practices to comply with regulations like GDPR or HIPAA, ensuring that businesses adhere to legal standards for data privacy and security.
7. Cloud-Native Application Development
With the rise of cloud-native applications, GCP Data Engineers play a crucial role in integrating data systems with microservices and containerized applications. By using Kubernetes Engine and Cloud Functions, they develop data architectures that support scalable and resilient cloud-native apps. This application is particularly relevant in industries like software development, where continuous deployment and scalability are essential. Engineers ensure that data flows seamlessly between services, enabling real-time data access and efficient application performance.
8. Data Migration to the Cloud
Many organizations are migrating their on-premise data infrastructure to the cloud to take advantage of scalability, flexibility, and cost-efficiency. GCP Data Engineers are responsible for planning and executing data migrations using services like Transfer Appliance, Cloud Storage Transfer Service, and Cloud SQL. They ensure that data is securely transferred with minimal disruption to ongoing operations. Migrating to GCP allows companies to modernize their infrastructure, optimize costs, and improve data accessibility, making this application critical for organizations undergoing digital transformation.
9. Predictive Analytics and Forecasting
Predictive analytics is an essential application of GCP Data Engineers in industries such as finance, retail, and healthcare. By building data pipelines that integrate historical and real-time data, engineers support the development of predictive models that can forecast trends, customer behaviors, or market changes. For instance, retailers can predict stock shortages, while healthcare providers can anticipate patient needs. The use of BigQuery ML and AI Platform enables businesses to develop and deploy predictive models that drive better decision-making and improve business outcomes.
10. Managing Multi-Cloud and Hybrid Cloud Environments
GCP Data Engineers often work in environments where data is distributed across multiple cloud platforms or on-premise systems. They are responsible for designing architectures that integrate and manage data from diverse sources while ensuring seamless interoperability. Tools like Anthos help GCP Data Engineers manage multi-cloud and hybrid cloud environments, making sure that data flows efficiently between different infrastructures. This application is particularly important for large enterprises with complex IT landscapes that require a unified data management approach.
GCP Data Engineer Course Certification with Training in Bangalore
Radical Technologies is the leading institute in Bangalore, renowned for offering comprehensive training in the GCP Data Engineer Certification. As a pioneer in cloud computing education, we specialize in delivering world-class Google Cloud Platform Data Engineer Certification programs designed to equip professionals with the skills and knowledge required to excel in cloud data engineering roles.
Our GCP Data Engineer Course provides in-depth training on data processing, storage, and analytics using the Google Cloud Platform. We offer a highly practical, hands-on approach, ensuring students gain real-world experience with tools like BigQuery, Cloud Dataflow, Pub/Sub, and more. Our expert trainers, with vast industry experience, guide students through the entire process, from basic concepts to advanced techniques, preparing them for the Google Professional Data Engineer Certification exam.
We understand the importance of certification in advancing your career, which is why our Google Cloud Data Engineer Course is tailored to meet the demands of the certification exam. Whether you’re looking to earn your Google Cloud Data Engineer Professional Certificate or preparing for the GCP Cloud Engineer Certification, our training covers all essential aspects.
At Radical Technologies, we are committed to helping professionals achieve success in cloud data engineering by offering the best Google Cloud Data Engineer Training in Bangalore. Our GCP Data Engineer Certification Course is designed to transform students into skilled cloud engineers, ready to tackle real-world challenges. Join us to kickstart your journey towards becoming a certified Google Data Engineer Professional and advance your career in the rapidly growing field of cloud computing.
GCP Data Engineer Related Courses
Data Engineering on Microsoft Azure
advanced big data science
SPARK & SCALA
HADOOP ADMIN
HADOOP DEV + SPARK & SCALA
PySpark
Apache Cassandra
DP-203: Data Engineering on Microsoft Azure
Apache Kafka
microsoft azure data fundamentals- dp-900
Course Features
- Lectures 0
- Quizzes 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
