What will you learn in Data Engineering, Big Data, and Machine Learning on GCP Specialization Course
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Design and operationalize data pipelines using GCP services like Dataflow, Pub/Sub, BigQuery, BigTable, and Dataproc.
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Perform end-to-end data engineering: ingestion, transformation, storage, and analytics at scale on GCP.
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Apply machine learning using AutoML, BigQuery ML, Vertex AI, and custom model deployment pipelines.
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Design ML pipelines and MLOps workflows with Vertex AI feature store, hyperparameter tuning, online/batch inference, and model monitoring.
Program Overview
Module 1: Google Cloud Big Data and Machine Learning Fundamentals
⏳ ~5 hours
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Topics: Introduces GCP data-to-AI lifecycle; overview of BigQuery, Dataflow, Pub/Sub, Dataproc, and Vertex AI.
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Hands‑on: Complete cloud skills labs on Pub/Sub, Dataflow, BigQuery; earn badges demonstrating proficiency.
Module 2: Modernizing Data Lakes and Data Warehouses with Google Cloud
⏳ ~8 hours
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Topics: Differences between data lakes vs. warehouses; design patterns using Cloud Storage, BigQuery, Dataproc; role of data engineers.
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Hands‑on: Load data into BigQuery, run transformation jobs via Dataproc, optimize storage and schema using real datasets.
Module 3: Building Batch Data Pipelines on Google Cloud
⏳ ~17 hours
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Topics: Batch ETL vs. ELT, Apache Hadoop & Spark on Dataproc, Dataflow pipelines, orchestration via Cloud Composer and Data Fusion.
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Hands‑on: Create batch pipelines with Dataflow, deploy Hadoop jobs on Dataproc, orchestrate workflows using Composer.
Module 4: Building Resilient Streaming Analytics Systems on Google Cloud
⏳ ~8 hours
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Topics: Real‑time streaming use cases, Pub/Sub messaging, Dataflow streaming with windowing & transformations, integration with BigQuery.
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Hands‑on: Stream data via Pub/Sub → Dataflow → BigQuery; implement windowed processing and real-time data dashboards.
Module 5: Smart Analytics, Machine Learning, and AI on Google Cloud
⏳ ~6 hours
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Topics: ML vs AI vs deep learning; use of unstructured data APIs, building models via BigQuery ML and Vertex AI AutoML.
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Hands‑on: Train and evaluate models with BigQuery ML, experiment with AutoML in Vertex AI, build notebook-based predictive analytics.
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Job Outlook
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Equips learners for roles such as Cloud Data Engineer, Machine Learning Engineer, and MLOps Specialist.
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Ideal for professionals preparing for the Google Professional Data Engineer or Machine Learning Engineer certifications.
Explore More Learning Paths
Advance your data engineering and machine learning expertise with these carefully selected courses, designed to help you work with big data, build ML models, and leverage Google Cloud for scalable solutions.
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