I suggest you to run these labs to understand more about Data Engineering via Google Cloud. I still believe the best way to learn is to make your hands dirty! Please open https://www.cloudskillsboost.google/ and try these labs!
BEST LABS FOR DATA ENGINEERING:
- Lab: Exploring a Public BigQuery Dataset
- Lab: Analyzing Billing Data with BigQuery.
- Lab: Loading Taxi Data into Cloud SQL.
- Lab: Loading Data into BigQuery.
- Lab: Working with JSON and Array data in BigQuery.
- Lab: Running Apache Spark jobs on Cloud Dataproc.
- Lab: A Simple Dataflow Pipeline (Python/Java).
- Lab: MapReduce in Dataflow (Python/Java).
- Lab: Side Inputs (Python/Java).
- Lab: Building and executing a pipeline graph in Cloud Data Fusion.
- Lab: An Introduction to Cloud Composer.
- Lab: Publish Streaming Data into Pub/Sub.
- Lab: Streaming Data Pipelines.
- Lab: Streaming Analytics and Dashboards.
- Lab: Streaming Data Pipelines into Bigtable.
- Lab: Optimizing your BigQuery Queries for Performance.
- Lab: Using the Natural Language API to Classify Unstructured Text.
- Lab: BigQuery in Jupyter Labs on AI Platform.
- Lab: Running AI models on Kubeflow.
- Lab: Predict Bike Trip Duration with a Regression Model in BQML.
- Lab: Movie Recommendations in BigQuery ML.
- Lab: Classifying Images of Clouds in the Cloud with AutoML Vision
BEST DEMOS FOR DATA ENGINEERING:
- Demo: Federated Queries with BigQuery.
- Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
- Demo: Running federated queries on Parquet and ORC files in BigQuery.
- Demo: Query TB+ of data in seconds.
- Demo: Querying Cloud SQL from BigQuery.
- Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
- Demo: Nested and repeated fields in BigQuery.
- Demo: Partitioned and Clustered Tables in BigQuery.
- Demo: ELT to improve data quality in BigQuery.
- Demo: Train a model with BigQuery ML to predict NYC taxi fares.
- Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.