Data Engineering On Google Cloud Platform in Harare

Data Engineering with Google Cloud Platform Course Outline

Module 1: Google Cloud Dataproc Overview

• Dataproc Overview
• Creating and Managing Clusters
• Custom Machine Types and Preemptible Worker Nodes
• Scaling and Deleting Clusters
• Creating Hadoop Clusters with Google Cloud Dataproc

Module 2: Running Dataproc Jobs

• Running Pig and Hive Jobs
• Separation of Storage and Compute
• Running Hadoop and Spark Jobs with Dataproc
• Submit and Monitor Jobs

Module 3: Integrating Dataproc with Google Cloud Platform

• Customise Cluster with Initialisation Activities
• BigQuery Support
• GCP Services

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

• Machine Learning APIs
• Use Cases of ML
• Adding Machine Learning Capabilities to Big Data

Module 5: Serverless Data Analysis with BigQuery

• BigQuery Overview
• Functions and Queries
• Writing Queries in BigQuery

Module 6: Serverless, Autoscaling Data Pipelines with Dataflow

• Beam Programming Model Overview
• Data Pipelines
• Writing a Dataflow Pipeline
• Scalable Big Data Processing Using Beam
• MapReduce in Dataflow
• Phases in MapReduce in Dataflow
• Steps of Dataflow
• Incorporating Additional Data
• Side Inputs in Apache Beam
• Querying Multiple Tables

Module 7: Getting Started with Machine Learning

• What is Machine Learning (ML)?
• Types of Machine Learning
• Explore and Create ML Datasets

Module 8: Building ML Models with TensorFlow

• Understand TensorFlow
• TensorFlow Graphs and Loops
• Use Low-Level TensorFlow
• Monitoring ML Training
• Charts and Graphs of TensorFlow

Module 9: Scaling ML Models with CloudML

• Cloud ML Overview
• TensorFlow Model
• Running of ML Model

Module 10: Architecture of Streaming Analytics Pipelines

• Understand Stream Data Processing
• Handling Variable Data Volumes
• Designing a Streaming Pipeline

Module 11: Ingesting Variable Volumes

• Cloud Pub/Sub Overview
• Working of Cloud Pub/Sub

Module 12: Implementing Streaming Pipelines

• Stream Processing Overview
• Watermark
• Triggers

Module 13: Streaming Analytics and Dashboards

• Streaming Analytics Overview
• Querying Streaming Data with BigQuery
• Google Data Studio Overview

Module 14: High Throughput and Low-Latency with Bigtable

• Cloud Spanner Overview
• Designing Bigtable Schema
• Ingesting into Bigtable
• Streaming into Bigtable

Who Should Attend this Data Engineering with Google Cloud Platform?

This Google Cloud Certification is a valuable resource for individuals seeking to enhance their data management and processing skills using Google Cloud Platform Services (GCP). The Data Engineering with Google Cloud Platform Course can benefit professionals such as:

• Cloud Engineers
• Data Engineers
• BI Developers
• ETL Developers
• Data Managers
• Data Architects
• Big Data Engineers

Prerequisites for the Data Engineering with Google Cloud Platform

There are no formal prerequisites for this training course. However, prior experience with SQL, data modeling, ETL processes, Python, and familiarity with machine learning or statistics, along with completion of the Google Cloud Fundamentals course, would be beneficial.

Data Engineering with Google Cloud Platform Overview

Data Engineering with Google Cloud Platform focuses on designing and managing scalable data pipelines using Google Cloud services, covering data ingestion, transformation, automation, and governance to support reliable, data-driven decision-making.

Upskilling in this area strengthens your ability to handle large datasets efficiently, implement automated workflows, and maintain strong data governance practices. These skills are essential for organisations aiming to improve data quality, security, and operational efficiency.

This 2-Day course offered by The Knowledge Academy equips delegates with practical knowledge of Google Cloud tools used in modern data engineering.

Data Engineering with Google Cloud Platform Course Objectives

• To understand the core concepts of Data Engineering on Google Cloud Platform
• To learn to design and build data processing systems on the Google Cloud Platform
• To gain proficiency in creating autoscaling data pipelines for both batch and streaming data using Cloud Dataflow
• To explore the integration of Dataproc with the Google Cloud Platform for efficient data processing
• To acquire the technical knowledge needed to work with unstructured data using Google's Machine Learning APIs
• To develop the skills to design comprehensive data processing systems, construct end-to-end data pipelines, and analyse data and Machine Learning models

Upon completing the Data Engineering with Google Cloud Platform, delegates will be equipped to design and implement data processing solutions on Google Cloud. They will also gain the ability to work with diverse datasets and apply machine learning techniques to support data-driven decision-making.

What's Included in this Data Engineering with Google Cloud Platform?

• World-Class Training Sessions from Experienced Instructors
• Data Engineering with Google Cloud Platform Certificate
• Digital Delegate Pack