1) Introduction to Data Engineering

• Understand the role of a data engineer

• Discuss benefits of doing data engineering in the cloud

• Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address

• Review and understand the purpose of a data lake versus a data warehouse, and when to use

2) Building a Data Lake

• Understand why Cloud Storage is a great option for building a data lake on Google Cloud

• Learn how to use Cloud SQL for a relational data lake

3) Building a Data Warehouse

• Discuss requirements of a modern warehouse

• Understand why BigQuery is the scalable data warehousing solution on Google Cloud

• Understand core concepts of BigQuery and review options of loading data into BigQuery

4) Introduction to Building Batch Data Pipelines

• Review different methods of loading data into your data lakes and warehouses: EL, ELT, and ETL

• Discuss data quality considerations and when to use ETL instead of EL and ELT

5) Executing Spark on Dataproc

• Review the parts of the Hadoop ecosystem

• Learn how to lift and shift your existing Hadoop workloads to the cloud using Dataproc

• Understand considerations around using Cloud Storage instead of HDFS for storage

• Learn how to optimize Dataproc jobs

6) Serverless Data Processing with Dataflow

• Understand how to decide between Dataflow and Dataproc for processing data pipelines

• Understand the features that customers value in Dataflow

• Discuss core concepts in Dataflow

• Review the use of Dataflow templates and SQL

7) Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

• Discuss how to manage your data pipelines with Data Fusion and Cloud Composer

• Understand Data Fusion’s visual design capabilities

• Learn how Cloud Composer can help to orchestrate the work across multiple Google Cloud services

8) Introduction to Processing Streaming Data

• Explain streaming data processing

• Describe the challenges with streaming data

• Identify the Google Cloud products and tools that can help address streaming data challenges

9) Serverless Messaging with Pub/Sub

• Describe the Pub/Sub service

• Understand how Pub/Sub works

• Gain hands-on Pub/Sub experience with a lab that simulates real-time streaming sensor data

10) Dataflow Streaming Features

• Understand the Dataflow service

• Build a stream processing pipeline for live traffic data

• Demonstrate how to handle late data using watermarks, triggers, and accumulation

 11) High-Throughput BigQuery and Bigtable Streaming Features

• Learn how to perform ad hoc analysis on streaming data using BigQuery and dashboards

• Understand how Cloud Bigtable is a low-latency solution

• Describe how to architect for Bigtable and how to ingest data into Bigtable

• Highlight performance considerations for the relevant services

12) Advanced BigQuery Functionality and Performance

• Review some of BigQuery’s advanced analysis capabilities

• Discuss ways to improve query performance

13) Introduction to Analytics and AI

• Understand the proposition that ML adds value to your data

• Understand the relationship between ML, AI, and Deep Learning

• Identify ML options on Google Cloud

14) Prebuilt ML Model APIs for Unstructured Data

• Discuss challenges when working with unstructured data

• Learn the applications of ready-to-use ML APIs on unstructured data

15) Big Data Analytics with Notebooks

• Introduce Notebooks as a tool for prototyping ML solutions

• Learn to execute BigQuery commands from Notebooks

16) Production ML Pipelines with Kubeflow 

• Describe options available for building custom ML models

• Understand the use of tools like Kubeflow

17) Custom Model Building with SQL in BigQuery ML 

• Learn how to create ML models by using SQL syntax in BigQuery

• Demonstrate building different kinds of ML models using BigQuery ML

18) Custom Model Building with AutoML

• Explore various AutoML products used in machine learning

• Learn to use AutoML to create powerful models without coding