Data Engineering on Google Cloud Platform Course Overview

Target Audience for Data Engineering on Google Cloud Platform

Learn how to leverage Google Cloud for data processing, machine learning, and analytics in Koenig Solutions' comprehensive Data Engineering course.

• Data Engineers looking to specialize in Google Cloud services
• Cloud Solution Architects seeking knowledge in data services and ML integration
• IT Professionals aiming to transition into data-centric roles
• Big Data Analysts interested in utilizing Google Cloud tools
• Data Scientists requiring expertise in Google Cloud’s ML capabilities
• Machine Learning Engineers exploring scalable cloud solutions
• Software Developers and Engineers who work with data pipelines
• DevOps Engineers focusing on continuous integration of data services
• Business Intelligence Professionals seeking to leverage cloud analytics
• Systems Administrators aiming to manage scalable data clusters
• Data Analytics Managers needing to understand cloud-based data platforms
• Technical Project Managers overseeing data engineering projects
• Graduates in computer science or related fields aspiring to work with cloud data services
• IT Consultants providing strategic advice on cloud data solutions

Learning Objectives - What you will Learn in this Data Engineering on Google Cloud Platform?

Introduction to Learning Outcomes

This course equips students with skills in data engineering on Google Cloud Platform, covering tools like Dataproc, BigQuery, Dataflow, TensorFlow, and Cloud ML for processing, analyzing, and leveraging big data and machine learning.

Learning Objectives and Outcomes

• Understand the management and scaling of clusters using Google Cloud Dataproc, including creating, customizing, and deleting clusters.
• Execute data processing jobs with Dataproc, utilizing Hadoop, Spark, Pig, and Hive, and grasp the concept of separation of storage and compute.
• Integrate Dataproc with GCP services and employ initialization actions to customize clusters, with a focus on leveraging BigQuery.
• Add machine learning capabilities to data analysis with Google's ML APIs and identify common use cases for machine learning.
• Perform serverless data analysis using BigQuery, including writing complex queries, managing data ingestion/exporting, and optimizing for performance and cost.
• Develop scalable, autoscaling data pipelines with Google Dataflow and Apache Beam, and process both batch and stream data.
• Get introduced to machine learning fundamentals, create ML datasets, and start building ML models with TensorFlow.
• Experience hands-on training with TensorFlow for building, training, and monitoring ML models, including the use of low-level TensorFlow operations.
• Scale ML models with GCP's Cloud ML, understanding the end-to-end training process and the packaging of TensorFlow models for cloud-based training.
• Apply feature engineering techniques to improve model performance and learn data preprocessing with Cloud ML.
• Design streaming analytics pipelines to handle real-time data processing, understanding challenges such as handling variable data volumes and late-arriving data.
• Implement streaming pipelines using Cloud Pub/Sub and Google Dataflow, focusing on robust handling of live data streams.
• Create streaming analytics and dashboards using tools like BigQuery and Google Data Studio for real-time data visualization and decision-making.
• Learn about high throughput and low-latency data solutions with Bigtable and Cloud Spanner, including schema design and data ingestion strategies.