Google Data Engineer - Professional Training

Google Data Engineer - Professional Certification Training is designed for data professionals and individuals preparing for the Google Professional Data Engineer exam. We, at Multisoft Systems, are engaged in providing this course with certified subject matter experts as per industry defined standards.

Google Data Engineer - Professional Training is designed to help data professionals and individuals prepare for the Google Professional Data Engineer exam. It is going to be a great option for you if you want to gain a solid understanding of the various data processing components of the Google Cloud Platform. You can go for this course if you have a basic knowledge of database management. We, at Multisoft Systems, are offering the course with certified industry experts.

You will learn the techniques of building data structures and databases, designing data processing systems, analyzing data and enabling machine learning, maintaining data structures and databases, visualizing data and designing secure data processing systems, and ensuring the reliability of data processing infrastructure. To go for this certification exam, you need at least 3 years of industry experience in designing and managing solutions with the use of Google Cloud.

Google Data Engineer Professional Course Objective

• How to design a data processing system?
• How to build data structures and databases?
• How to maintain data structures and databases?
• How to analyze data and enable machine learning?
• How to optimize data representations, data infrastructure performance, and cost?
• How to visualize data and design secure data processing systems?
• How to ensure the reliability of data processing infrastructure?

Google Data Engineer Professional Online Training

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

Target Audience

• Data professionals
• Individuals preparing for the Google Professional Data Engineer exam

Google Data Engineer Professional Course Prerequisites

• Basic database knowledge

Google Data Engineer Professional Course Certification

• Multisoft Systems will provide you with a training completion certificate after completing this Google Data Engineer Professional Certification Training.

Module 1: Google Cloud Dataproc Overview

• Creating and managing clusters.
• Leveraging custom machine types and preemptible worker nodes.
• Scaling and deleting Clusters.

Module 2: Running Dataproc Jobs

• Running Pig and Hive jobs.
• Separation of storage and compute.

Module 3: Integrating Dataproc with Google Cloud Platform

• Customize cluster with initialization actions.
• BigQuery Support.

Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs

• Google’s Machine Learning APIs.
• Common ML Use Cases.
• Invoking ML APIs.

Module 5: Serverless data analysis with BigQuery

• What is BigQuery.
• Queries and Functions.
• Loading data into BigQuery.
• Exporting data from BigQuery.
• Nested and repeated fields.
• Querying multiple tables.
• Performance and pricing.

Module 6: Serverless, autoscaling data pipelines with Dataflow

• The Beam programming model.
• Data pipelines in Beam Python.
• Data pipelines in Beam Java.
• Scalable Big Data processing using Beam.
• Incorporating additional data.
• Handling stream data.
• GCP Reference architecture.

Module 7: Getting started with Machine Learning

• What is machine learning (ML).
• Effective ML: concepts, types.
• ML datasets: generalization.

Module 8: Building ML models with Tensorflow

• Getting started with TensorFlow.
• TensorFlow graphs and loops + lab.
• Monitoring ML training.

Module 9: Scaling ML models with CloudML

• Why Cloud ML?
• Packaging up a TensorFlow model.
• End-to-end training.

Module 10: Feature Engineering

• Creating good features.
• Transforming inputs.
• Synthetic features.
• Preprocessing with Cloud ML.

Module 11: Architecture of streaming analytics pipelines

• Stream data processing: Challenges.
• Handling variable data volumes.
• Dealing with unordered/late data.

Module 12: Ingesting Variable Volumes

• What is Cloud Pub/Sub?
• How it works: Topics and Subscriptions.

Module 13: Implementing streaming pipelines

• Challenges in stream processing.
• Handle late data: watermarks, triggers, accumulation.

Module 14: Streaming analytics and dashboards

• Streaming analytics: from data to decisions.
• Querying streaming data with BigQuery.
• What is Google Data Studio?

Module 15: High throughput and low-latency with Bigtable

• What is Cloud Spanner?
• Designing Bigtable schema.
• Ingesting into Bigtable.