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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.
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( 4.2 Ratings )
Live Online Classes starting on 01 January, 1970
Data Engineer
Professional Data Engineer Certification Overview
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
( 4.2 Ratings )
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Live Online Training (Duration : 32 Hours)
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Industry experienced & certified trainers
Query Handling session by technical expert after 2 month completion of training
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Custom tailored training as per the requirement
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Exam Mock papers
100% Quality assurance with certified & industry experienced Trainer
Live Online Training (Duration : 32 Hours)
Guaranteed to run classes as per your convenient time zone
Industry experienced & certified trainers
Query Handling session by technical expert after 2 month completion of training
Career path counselling
Custom tailored training as per the requirement
Exam assistance
Exam Mock papers
100% Quality assurance with certified & industry experienced Trainer
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Our course is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
Professional Data Engineer Certification Overview
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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