GCDEC: Difference between revisions
From charlesreid1
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Course 1 - Google Cloud Platform Big Data and Machine Learning Fundamentals | Course 1 - Google Cloud Platform Big Data and Machine Learning Fundamentals | ||
* [[GCDEC/Fundamentals/Notes]] | |||
Course 2 - Leveraging Unstructured Data with Cloud Dataproc | Course 2 - Leveraging Unstructured Data with Cloud Dataproc | ||
* [[GCDEC/Unstructured Data/Notes]] | |||
Course 3 - Serverless Data Analysis with BigQuery and Dataflow | Course 3 - Serverless Data Analysis with BigQuery and Dataflow | ||
* [[GCDEC/BigQuery Dataflow/Notes]] | |||
Course 4 - Serverless machine Learning with Tensorflow | Course 4 - Serverless machine Learning with Tensorflow | ||
* [[GCDEC/Tensorflow/Notes]] | |||
Course 5 - Building Resilient Streaming Systems | Course 5 - Building Resilient Streaming Systems | ||
* [[GCDEC/Streaming/Notes]] | |||
[[Category:Google Cloud]] | [[Category:Google Cloud]] | ||
[[Category:Data Engineering]] | [[Category:Data Engineering]] | ||
Revision as of 22:32, 19 September 2017
GCDEC: Google Cloud Data Engineer Certification
Basic Info
Certification overview: https://cloud.google.com/certification/data-engineer
Sample case study: https://cloud.google.com/certification/guides/data-engineer/casestudy-flowlogistic
What this certification "certifies" you can do:
- Build and maintain data structures and databases
- Design data processing systems
- Analyze data and enable machine learning
- Model business processes for analysis and optimization
- Design for reliability
- Visualize data and advocate policy
- Design for security and compliance
Underlying goal: building data-handling capabilities (pipelines to ingest, process, and analyze data, and build models)
- Data engineers enable better decision-making
- Cloud services enable you to do more stuff with less knowledge and work - more infrastructure and better models, without getting bogged down by rote devops tasks or slogging through low-level statistics
What is a Data Engineer
Data engineers do any number of things:
- Design, build, and maintain data structures, databases, data processing systems, data pipelines
- Move data from one place to another
- Data science
- Enabling machine learning to happen, doing machine learning themselves
- Model the process
- Enable data-driven decision making in a company
Google Cloud Services
See Google Cloud#Google Cloud Services
Technology Stack
Case study with an example of the kind of technology stack that you might see in use at a company:
List of all pages related to Google Cloud platform:
Training Resources
Coursera
Course 1 - Google Cloud Platform Big Data and Machine Learning Fundamentals
Course 2 - Leveraging Unstructured Data with Cloud Dataproc
Course 3 - Serverless Data Analysis with BigQuery and Dataflow
Course 4 - Serverless machine Learning with Tensorflow
Course 5 - Building Resilient Streaming Systems