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Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration

WGAC-GGL-DWBQ-SDQA

Google Cloud Training Courses Certification

Schedule

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Description

Module 01 BigQuery Architecture Fundamentals
Topics
  • Introduction
  • BigQuery Core Infrastructure
  • BigQuery Storage
  • BigQuery Query Processing
  • BigQuery Data Shuffling
Objectives
  • Explain the benefits of columnar storage.
  • Understand how BigQuery processes data.
  • Explore the basics of BigQuery’s shuffling service to improve query efficiency.
Activities
  • Labs and demos
Module 02 Storage and Schema Optimizations
Topics
  • BigQuery Storage
  • Partitioning and Clustering
  • Nested and Repeated Fields
  • ARRAY and STRUCT syntax
  • Best Practices
Objectives
  • Compare the performance of different schemas (snowflake, denormalized, and nested and repeated fields).
  • Partition and cluster data for better performance
  • Improve schema design using nested and repeated fields.
  • Describe additional best practices such as table and partition expiration
Activities
  • Labs and demos
Module 03 Ingesting Data
Topics
  • Data Ingestion Options
  • Batch Ingestion
  • Streaming Ingestion
  • Legacy Streaming API
  • BigQuery Storage Write API
  • Query Materialization
  • Query External Data Sources
  • Data Transfer Service
Objectives
  • Ingest batch and streaming data.
  • Query external data sources.
  • Schedule data transfers.
  • Understand how to use Storage Write API.
Activities
  • Labs and demos
Module 04 Changing Data
Topics
  • Managing Change in Data Warehouses
  • Handling Slowly Changing Dimensions (SCD)
  • DML statements
  • DML Best Practices and Common Issues
Objectives
  • Write DML statements.
  • Address common DML performance problems and bottlenecks.
  • Identify slowly changing dimensions (SCD) in your data and make updates.
Module 05 Improving Read Performance
Topics
  • BigQuery’s Cache
  • Materialized Views
  • BI Engine
  • High Throughput Reads
  • BigQuery Storage Read API
Objectives
  • Explore BigQuery’s cache.
  • Create materialized views.
  • Work with BI Engine to accelerate your SQL queries.
  • Use the Storage Read API for fast access to BigQuery-managed storage.
  • Explain the caveats of using external data sources.
Activities
  • Labs and demos
Module 06 Optimizing and Troubleshooting Queries
Topics
  • Simple Query Execution
  • SELECTs and Aggregation
  • JOINs and Skewed JOINs
  • Filtering and Ordering
  • Best Practices for Functions
Objectives
  • Interpret BigQuery execution details and the query plan.
  • Optimize query performance by using suggested methods for SQL statements and clauses.
  • Demonstrate best practices for functions in business use cases.
Activities
  • Labs and demos
Module 07 Workload Management and Pricing
Topics
  • BigQuery Slots
  • Pricing Models and Estimates
  • Slot Reservations
  • Controlling Costs
Objectives
  • Define a BigQuery slot.
  • Explain pricing models and pricing estimations (BigQuery UI, bq dry_run, jobs API).
  • Understand slot reservations, commitments, and assignments.
  • Identify best practices to control costs.
Activities
  • Demos
Module 08 Logging and Monitoring
Topics
  • Cloud Monitoring
  • BigQuery Admin Panel
  • Cloud Audit Logs
  • INFORMATION_SCHEMA
  • Query Path and Common Errors
Objectives
  • Use Cloud Monitoring to view BigQuery metrics.
  • Explore the BigQuery admin panel.
  • Use Cloud Audit logs.
  • Work with INFORMATION_SCHEMA tables to get insights for your BigQuery entities.
Activities
  • Labs and demos
Module 09 Security in BigQuery
Topics
  • Secure Resources with IAM
  • Authorized Views
  • Secure Data with Classification
  • Encryption
  • Data Discovery and Governance
Objectives
  • Explore data discovery using Data Catalog.
  • Discuss data governance using DLP API and Data Catalog.
  • Create IAM policies (e.g., authorized views) to secure resources.
  • Secure data with classifications (e.g., row-level policies).
  • Understand how BigQuery uses encryption.
Activities
  • Labs and demos
Module 10 Automating Workloads
Topics
  • Scheduling Queries
  • Scripting
  • Stored Procedures
  • Integration with Big Data Products
Objectives
  • Schedule queries.
  • Use scripting and stored procedures to build custom transformations.
  • Describe how to integrate BigQuery workloads with other Google Cloud big data products.
Activities
  • Demos
Module 11 Machine Learning in BigQuery
Topics
  • Introduction to BigQuery ML
  • How to Make Predictions with BigQuery ML
  • How to Build and Deploy a Recommendation System with BigQuery ML
  • How to Build and Deploy a Demand Forecasting Solution with BigQuery ML
  • Time-Series Models with BigQuery ML
  • BigQuery ML Explainability
Objectives
  • Describe some of the different applications of BigQuery ML.
  • Build and deploy several categories of machine learning models with BigQuery ML.
  • Use AutoML Tables to solve high-value business problems.
Activities
  • Labs and demos

Big Data and Machine Learning Fundamentals

  • Describe BigQuery architecture fundamentals.
  • Implement storage and schema design patterns to improve performance.
  • Use DML and schedule data transfers to ingest data.
  • Apply best practices to improve read efficiency and optimize query performance.
  • Manage capacity and automate workloads.
  • Understand patterns versus anti-patterns to optimize queries and improve read performance.
  • Use logging and monitoring tools to understand and optimize usage patterns.
  • Apply security best practices to govern data and resources.
  • Build and deploy several categories of machine learning models with BigQuery ML.

  • BigQuery Architecture Fundamentals
  • Storage and Schema Optimizations
  • Ingesting Data
  • Changing Data
  • Improving Read Performance
  • Optimizing and Troubleshooting Queries
  • Workload Management and Pricing
  • Logging and Monitoring
  • Security in BigQuery
  • Automating Workloads
  • Machine Learning in BigQuery
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