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Data Engineering, Big Data, and Machine Learning on GCP Specialization
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Data Engineering, Big Data, and Machine Learning on GCP Specialization
Data Engineering on Google Cloud. Launch your career in Data Engineering. Deliver business value with big data and machine learning.
Instructor: Google Cloud Training
123,790 already enrolled
Included with 
Get in-depth knowledge of a subject
4.6
(12,519 reviews)
Intermediate level
Some related experience required
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.6
(12,519 reviews)
Intermediate level
Some related experience required
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Skills you'll gain
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
24 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Google Cloud
Specialization - 4 course series
What you'll learn
Differentiate between data lakes and data warehouses.
Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.
Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.
Examine why data engineering should be done in a cloud environment.
Skills you'll gain
Category: Google Cloud Platform
Category: Data Warehousing
Category: Data Lakes
Category: Data Transformation
Category: Cloud Storage
Category: Data Pipelines
Category: Cloud Engineering
Category: Data Infrastructure
Category: Scalability
Category: SQL
Category: Data Processing
Category: Big Data
What you'll learn
Review different methods of data loading: EL, ELT and ETL and when to use what
Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
Build your data processing pipelines using Dataflow
Manage data pipelines with Data Fusion and Cloud Composer
Skills you'll gain
Category: Data Pipelines
Category: Google Cloud Platform
Category: Apache Spark
Category: Data Processing
Category: Data Transformation
Category: Extract, Transform, Load
Category: Apache Hadoop
Category: Big Data
Category: Data Integration
Category: Apache Airflow
Category: Data Migration
Category: Serverless Computing
What you'll learn
Interpret use-cases for real-time streaming analytics.
Manage data events using the Pub/Sub asynchronous messaging service.
Write streaming pipelines and run transformations where necessary.
Interoperate Dataflow, BigQuery and Pub/Sub for real-time streaming and analysis
Skills you'll gain
Category: Performance Tuning
Category: Data Processing
Category: Google Cloud Platform
Category: Data Pipelines
Category: Real Time Data
Category: Analytics
Category: Data Transformation
Category: Scalability
Category: SQL
Category: Big Data
What you'll learn
Differentiate between ML, AI and deep learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from notebooks.
Create ML models by using SQL syntax in BigQuery and without coding using Vertex AI AutoML.
Skills you'll gain
Category: Google Cloud Platform
Category: Machine Learning
Category: Applied Machine Learning
Category: Unstructured Data
Category: Jupyter
Category: Tensorflow
Category: Natural Language Processing
Category: Data Pipelines
Category: Big Data
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: MLOps (Machine Learning Operations)
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
This accelerated specialization is designed to be completed in only four weeks. Additionally, our Google Cloud Platform free trial ends after 60 days or when your $300 in credits are used up.
One (1) year of experience with one or more of the following:
• A common query language such as SQL
• Extract, transform, load activities
• Data modeling
• Machine learning and/or statistics
• Programming in Python
We strongly recommend you take these courses in order, beginning with Big Data and Machine Learning Fundamentals. This is especially important when completing the Qwiklabs projects, as these hands-on labs build upon the work you complete in preceding courses.
More questions
Financial aid available,