4 Days

Data Engineering on Google Cloud Platform Certification Course Overview:

Whether you’re a data engineer seeking to upskill or a professional exploring cloud-based data processing, this comprehensive program equips you with the practical tools and knowledge to design, build, and manage robust data pipelines on GCP. This course helps you in: 

  • Master the principles and considerations for architecting efficient and scalable data pipelines on GCP. 
  • Dive into the practicalities of constructing real-world data pipelines using powerful GCP services like Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. 
  • Leverage various GCP tools and techniques to explore, analyze, and gain insights from your structured, unstructured, and streaming data. 
  • Discover how to seamlessly integrate machine learning capabilities into your data pipelines, unlocking the potential for advanced data-driven applications. 

Data Engineering on Google Cloud Platform - What You'll Learn:

  • Become an expert in crafting efficient and scalable data processing systems tailored to your specific needs.
  • Seamlessly process both batch and streaming data using cutting-edge GCP services like Cloud Dataflow. Implement auto-scaling features to ensure your pipelines handle any data volume with ease.
  • Unleash the power of BigQuery to delve into massive datasets and uncover hidden business insights, even with limited coding experience.
  • Leverage Spark and ML APIs on Dataproc to unlock the value hidden within your unstructured data sources.
  • Gain the ability to generate instant insights from streaming data, enabling real-time decision-making and agile business responses.

Upcoming Batches

Enroll Online
Start Date End Date

2025-03-03

2025-03-06

2025-03-10

2025-03-13

2025-03-17

2025-03-20

2025-03-24

2025-03-27

2025-03-31

2025-04-03

Key Features: Data Engineering on Google Cloud Platform

  • Our Google Cloud Platform training modules have 50% – 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).

  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).

  • GCP-certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).

  • Well-structured use cases to simulate challenges encountered in a Real-World environment during Google Cloud Platform training.

  • Integrated teaching assistance and support through an experts-designed Learning Management System (LMS) and ExamReady platform.

  • Being an official Google Cloud Platform Training Partner, we offer authored curricula aligned with industry standards.

Who Should Attend this Course on Data Engineering on Google Cloud Platform:

    • This course is intended for developers who are responsible for:
  • Extracting, Loading, Transforming, cleaning, and validating data
  • Designing pipelines and architectures for data processing
  • Integrating analytics and machine learning capabilities into data pipelines
  • Querying datasets, visualizing query results and creating reports.

Prerequisites:

    To get the most out of this course, participants should have:
  • Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience.
  • Basic proficiency with common query language such as SQL.
  • Experience with data modeling, extract, transform, load activities.
  • Developing applications using a common programming language such as Python.
  • Familiarity with Machine Learning and/or statistics.

    • Why choose CloudThat as your Training Partner?

    • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, Case-Studies, best practices, and hands-on experience tailored specifically for GCP.
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios.
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set.
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills.
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course.
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals.
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge.
    • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training.

    Learning objective of the course :

    • Design and build robust data pipelines on GCP, handling both batch and streaming data with confidence.
    • Extract valuable insights from massive datasets using BigQuery and advanced analytics tools.
    • Leverage unstructured data for valuable insights through Spark and ML integration.
    • Generate real-time insights from streaming data, fueling agile decision-making.
    • Build powerful machine learning models using Cloud AutoML and BigQuery ML, even without extensive coding experience.

    Course Outline: Download Course Outline

    Topics:

    • Explore the role of a data engineer.
    • Analyze data engineering challenges.
    • Intro to BigQuery.
    • Data Lakes and Data Warehouses.
    • Demo: Federated Queries with BigQuery.
    • Transactional Databases vs Data Warehouses.
    • Website Demo: Finding PII in your dataset with DLP API.
    • Partner effectively with other data teams.
    • Manage data access and governance.
    • Build production-ready pipelines.
    • Review GCP customer case study.

    Activities

    • Lab: Analyzing Data with BigQuery.

    Topics:

    • Introduction to Data Lakes.
    • Data Storage and ETL options on GCP.
    • Building a Data Lake using Cloud Storage.
    • Securing Cloud Storage.
    • Storing All Sorts of Data Types.
    • Cloud SQL as a relational Data Lake.

    Activities:

    • Lab: Loading Taxi Data into Cloud SQL.
    • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
    • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.

    Topics:

    • The modern data warehouse.
    • Intro to BigQuery.
    • Demo: Query TB+ of data in seconds.
    • Getting Started.
    • Loading Data.
    • Exploring Schemas.
    • Schema Design.
    • Optimizing with Partitioning and Clustering.

    Activities:

    • Video Demo: Querying Cloud SQL from BigQuery.
    • Lab: Loading Data into BigQuery.
    • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
    • Nested and Repeated Fields.
    • Demo: Nested and repeated fields in BigQuery.
    • Lab: Working with JSON and Array data in BigQuery.
    • Demo: Partitioned and Clustered Tables in BigQuery.
    • Preview: Transforming Batch and Streaming Data.

    Topics:

    • EL, ELT, ETL.
    • Quality considerations.
    • How to carry out operations in BigQuery.
    • Demo: ELT to improve data quality in BigQuery.
    • Shortcomings.
    • ETL to solve data quality issues.

    Topics:

    • The Hadoop ecosystem.
    • Running Hadoop on Cloud Dataproc.
    • GCS instead of HDFS.
    • Optimizing Dataproc.

    Activities:

    • Lab: Running Apache Spark jobs on Cloud Dataproc.

    Topics:

    • Cloud Dataflow.
    • Why customers value Dataflow.
    • Dataflow Pipelines.
    • Dataflow Templates.
    • Dataflow SQL.

    Activities:

    • Lab: A Simple Dataflow Pipeline (Python/Java).
    • Lab: MapReduce in Dataflow (Python/Java)
    • Lab: Side Inputs (Python/Java).

    Topics:

    • Building Batch Data Pipelines visually with Cloud Data Fusion.
    • Components.
    • UI Overview.
    • Building a Pipeline.
    • Exploring Data using Wrangler.
    • Orchestrating work between GCP services with Cloud Composer.
    • Apache Airflow Environment.
    • DAGs and Operators.
    • Workflow Scheduling.
    • Monitoring and Logging.

    Activities:

    • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
    • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
    • Lab: An Introduction to Cloud Composer.

    Topics:

    • Processing Streaming Data.

    Topics:

    • Introduction to Pub/Sub

    Activities:

    • Lab: Publish Streaming Data into Pub/Sub.

    Topics:

    • Cloud Dataflow Streaming Features.

    Activities:

    • Lab: Streaming Data Pipelines.

    Topics:

    • BigQuery Streaming Features.
    • Cloud Bigtable.

    Activities:

    • Lab: Streaming Analytics and Dashboards.
    • Lab: Streaming Data Pipelines into Bigtable.

    Topics:

    • Analytic Window Functions.
    • Using With Clauses.
    • GIS Functions.
    • Performance Considerations.

    Activities:

    • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.Lab: Streaming Data Pipelines into Bigtable.
    • Lab: Optimizing your BigQuery Queries for Performance.
    • Optional Lab: Creating Date-Partitioned Tables in BigQuery.

    Topics:

    • What is AI?.
    • From Ad-hoc Data Analysis to Data Driven Decisions.
    • Options for ML models on GCP.

    Topics:

    • Unstructured Data is Hard.
    • ML APIs for Enriching Data.

    Activities:

    • Lab: Using the Natural Language API to Classify Unstructured Text.

    Topics:

    • What’s a Notebook.
    • BigQuery Magic and Ties to Pandas.

    Activities:

    • Lab: BigQuery in Jupyter Labs on AI Platform.

    Topics:

    • Ways to do ML on GCP.
    • Kubeflow.
    • AI Hub.

    Activities:

    • Lab: Running AI models on Kubeflow.

    Topics:

    • BigQuery ML for Quick Model Building.
    • Supported Models.

    Activities:

    • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
    • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
    • Lab Option 2: Movie Recommendations in BigQuery ML.

    Topics:

    • Why Auto ML?
    • Auto ML Vision.
    • Auto ML NLP.
    • Auto ML Tables.

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    Course ID: 19458

    Course Price at

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