The Data Engineer certification focuses on designing and managing data infrastructure, including data pipelines, storage solutions, and data processing frameworks. It equips professionals with skills to handle large-scale data systems, optimize data workflows, and ensure efficient data management. Ideal for those looking to advance their expertise in data engineering and analytics.
This four-day instructor-led course offers a comprehensive hands-on introduction to designing and building data processing systems on the Google Cloud Platform (GCP). Participants will engage in a combination of presentations, demos, and labs to learn how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning solutions. The course covers handling structured, unstructured, and streaming data effectively.
Audience: This course is ideal for experienced developers responsible for managing big data transformations, including:
Extracting, loading, transforming, cleaning, and validating data
Designing data processing pipelines and architectures
Creating and maintaining machine learning and statistical models
Querying datasets, visualizing results, and generating reports
Prerequisites: To maximize learning, participants should have:
Completed the Google Cloud Fundamentals: Core Infrastructure (GCPFCI) course or have equivalent experience
Basic proficiency in SQL or a similar query language
Experience in data modeling and ETL (Extract, Transform, Load) activities
Experience developing applications using a common programming language such as Python
Familiarity with basic statistics and machine learning concepts
Learning Outcomes: By the end of this course, participants will be able to:
Design and build data processing systems on Google Cloud Platform
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Process batch and streaming data with autoscaling pipelines on Cloud Dataflow
Derive insights from large datasets using Google BigQuery
Train, evaluate, and deploy machine learning models with TensorFlow and Cloud ML
This four-day instructor-led course offers a comprehensive hands-on introduction to designing and building data processing systems on the Google Cloud Platform (GCP). Participants will engage in a combination of presentations, demos, and labs to learn how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning solutions. The course covers handling structured, unstructured, and streaming data effectively.
Audience: This course is ideal for experienced developers responsible for managing big data transformations, including:
Extracting, loading, transforming, cleaning, and validating data
Designing data processing pipelines and architectures
Creating and maintaining machine learning and statistical models
Querying datasets, visualizing results, and generating reports
Prerequisites: To maximize learning, participants should have:
Completed the Google Cloud Fundamentals: Core Infrastructure (GCPFCI) course or have equivalent experience
Basic proficiency in SQL or a similar query language
Experience in data modeling and ETL (Extract, Transform, Load) activities
Experience developing applications using a common programming language such as Python
Familiarity with basic statistics and machine learning concepts
Learning Outcomes: By the end of this course, participants will be able to:
Design and build data processing systems on Google Cloud Platform
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Process batch and streaming data with autoscaling pipelines on Cloud Dataflow
Derive insights from large datasets using Google BigQuery
Train, evaluate, and deploy machine learning models with TensorFlow and Cloud ML
Generate instant insights from streaming data
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Live Online Training (Duration : 32 Hours)
