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Data Engineering, Big Data, and Machine Learning on GCP

Google Cloud Training

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification.

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87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification.

Here's what you have to do

1) Complete the Coursera Data Engineering Professional Certificate

2) Review other recommended resources for the Google Cloud Professional Data Engineer certification exam

3) Review the Professional Data Engineer exam guide

4) Complete Professional Data Engineer sample questions

5) Register for the Google Cloud certification exam (remotely or at a test center)

Applied Learning Project

This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

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What's inside

Five courses

Google Cloud Big Data and Machine Learning Fundamentals

(0 hours)
This course introduces Google Cloud's big data and machine learning offerings, covering the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Modernizing Data Lakes and Data Warehouses with Google Cloud

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
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Building Batch Data Pipelines on Google Cloud

Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow.
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Building Resilient Streaming Analytics Systems on Google Cloud

Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis.
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Smart Analytics, Machine Learning, and AI on Google Cloud

Incorporating machine learning into data pipelines enhances data insights. This course explores using machine learning in data pipelines on Google Cloud. For minimal customization, it covers AutoML. For tailored capabilities, it introduces Notebooks and BigQuery ML. It also covers productionalizing machine learning solutions using Vertex AI.
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Time
12 hours
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Level
Intermediate
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