Data Engineering on Google Cloud

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

Design and build data processing systems on Google Cloud Platform.  

  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.
  • Derive business insights from extremely large datasets using Google BigQuery.
  • Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.
  • Enable instant insights from streaming data
 

Course Outline
1 - INTRODUCTION TO DATA ENGINEERING
  • Explore the role of a data engineer.
  • Analyze data engineering challenges.
  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Demo: Federated Queries with BigQuery.
  • Transactional Databases vs Data Warehouses.
  • Website Demo: Finding PII in your dataset with DLP API.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.
  • Lab: Analyzing Data with BigQuery.
2 - BUILDING A DATA LAKE
  • Introduction to Data Lakes.
  • Data Storage and ETL options on GCP.
  • Building a Data Lake using Cloud Storage.
  • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
  • Securing Cloud Storage.
  • Storing All Sorts of Data Types.
  • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
  • Cloud SQL as a relational Data Lake.
  • Lab: Loading Taxi Data into Cloud SQL.
3 - BUILDING A DATA WAREHOUSE
  • The modern data warehouse.
  • Intro to BigQuery.
  • Demo: Query TB+ of data in seconds.
  • Getting Started.
  • Loading Data.
  • Video Demo: Querying Cloud SQL from BigQuery.
  • Lab: Loading Data into BigQuery.
  • Exploring Schemas.
  • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
  • Schema Design.
  • Nested and Repeated Fields.
  • Demo: Nested and repeated fields in BigQuery.
  • Lab: Working with JSON and Array data in BigQuery.
  • Optimizing with Partitioning and Clustering.
  • Demo: Partitioned and Clustered Tables in BigQuery.
  • Preview: Transforming Batch and Streaming Data.
4 - INTRODUCTION TO BUILDING BATCH DATA PIPELINES
  • EL, ELT, ETL.
  • Quality considerations.
  • How to carry out operations in BigQuery.
  • Demo: ELT to improve data quality in BigQuery.
  • Shortcomings.
  • ETL to solve data quality issues.
5 - EXECUTING SPARK ON CLOUD DATAPROC
  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.
  • Lab: Running Apache Spark jobs on Cloud Dataproc.
6 - SERVERLESS DATA PROCESSING WITH CLOUD DATAFLOW
  • Cloud Dataflow.
  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Lab: A Simple Dataflow Pipeline (Python/Java).
  • Lab: MapReduce in Dataflow (Python/Java).
  • Lab: Side Inputs (Python/Java).
  • Dataflow Templates.
  • Dataflow SQL.
7 - MANAGE DATA PIPELINES WITH CLOUD DATA FUSION AND CLOUD COMPOSER
  • Building Batch Data Pipelines visually with Cloud Data Fusion.
  • Components.
  • UI Overview.
  • Building a Pipeline.
  • Exploring Data using Wrangler.
  • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
  • Orchestrating work between GCP services with Cloud Composer.
  • Apache Airflow Environment.
  • DAGs and Operators.
  • Workflow Scheduling.
  • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
  • Monitoring and Logging.
  • Lab: An Introduction to Cloud Composer.
8 - INTRODUCTION TO PROCESSING STREAMING DATA
  • Processing Streaming Data.
9 - SERVERLESS MESSAGING WITH CLOUD PUB/SUB
  • Cloud Pub/Sub.
  • Lab: Publish Streaming Data into Pub/Sub.
10 - CLOUD DATAFLOW STREAMING FEATURES
  • Cloud Dataflow Streaming Features.
  • Lab: Streaming Data Pipelines.
11 - HIGH-THROUGHPUT BIGQUERY AND BIGTABLE STREAMING FEATURES
  • BigQuery Streaming Features.
  • Lab: Streaming Analytics and Dashboards.
  • Cloud Bigtable.
  • Lab: Streaming Data Pipelines into Bigtable.
12 - ADVANCED BIGQUERY FUNCTIONALITY AND PERFORMANCE
  • Analytic Window Functions.
  • Using With Clauses.
  • GIS Functions.
  • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
  • Performance Considerations.
  • Lab: Optimizing your BigQuery Queries for Performance.
  • Optional Lab: Creating Date-Partitioned Tables in BigQuery.
13 - INTRODUCTION TO ANALYTICS AND AI
  • What is AI?.
  • From Ad-hoc Data Analysis to Data Driven Decisions.
  • Options for ML models on GCP.
14 - PREBUILT ML MODEL APIS FOR UNSTRUCTURED DATA
  • Unstructured Data is Hard.
  • ML APIs for Enriching Data.
  • Lab: Using the Natural Language API to Classify Unstructured Text.
15 - BIG DATA ANALYTICS WITH CLOUD AI PLATFORM NOTEBOOKS
  • What's a Notebook.
  • BigQuery Magic and Ties to Pandas.
  • Lab: BigQuery in Jupyter Labs on AI Platform.
16 - PRODUCTION ML PIPELINES WITH KUBEFLOW
  • Ways to do ML on GCP.
  • Kubeflow.
  • AI Hub.
  • Lab: Running AI models on Kubeflow.
17 - CUSTOM MODEL BUILDING WITH SQL IN BIGQUERY ML
  • BigQuery ML for Quick Model Building.
  • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
  • Supported Models.
  • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
  • Lab Option 2: Movie Recommendations in BigQuery ML.
18 - CUSTOM MODEL BUILDING WITH CLOUD AUTOML
  • Why Auto ML?
  • Auto ML Vision.
  • Auto ML NLP.
  • Auto ML Tables.

Actual course outline may vary depending on offering center. Contact your sales representative for more information.

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Course Length: 4 Days

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Description

Design and build data processing systems on Google Cloud Platform.

 

  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.
  • Derive business insights from extremely large datasets using Google BigQuery.
  • Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.
  • Enable instant insights from streaming data

 

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