This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out machine learning. The course covers structured, unstructured, and streaming data.
This course teaches participants the following skills:
Design and build data processing systems on Google Cloud Platform
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
Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
Enable instant insights from streaming data
This class is intended for experienced developers who are responsible for managing big data transformations including:
Extracting, Loading, Transforming, cleaning, and validating data
Designing pipelines and architectures for data processing
Creating and maintaining machine learning and statistical models
Querying datasets, visualizing query results, and creating reports
To get the most of out of this course, participants should have:
Completed Google Cloud Fundamentals: Big Data and Machine Learning course OR have equivalent experience
Basic proficiency with common query language such as SQL
Experience with data modeling, extract, transform, load activities
Experience with developing applications using a common programming language such as Python
Familiarity with Machine Learning and/or statistics
All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer.
-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
-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
-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
-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
-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
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