Data Science & Analytics · PPL

Google BigQuery Analytics Certification

A specialist program designed to develop practical Google BigQuery skills for cloud data warehousing, SQL analytics, data processing, optimization, and scalable analytics.

  • ⏱3 DaysDuration
  • ◈PPLAccredited
  • ◐3 LanguagesArabic · English · Hindi
  • ◆₹10,999.00 Per delegate

This course is accredited by PPL

This is for all ppl accredited courses
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4.8 ★ Average learner rating
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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Google BigQuery

  • BigQuery fundamentals
  • Cloud data warehousing
  • BigQuery use cases
  • Serverless analytics
  • Data analytics workflows
  • BigQuery ecosystem

Module 2: BigQuery Architecture

  • Storage architecture
  • Compute architecture
  • Separation of storage and compute
  • Distributed processing
  • Slots and workloads
  • BigQuery resources

Module 3: BigQuery Environment & Interface

  • Google Cloud Console
  • BigQuery Studio
  • Projects
  • Datasets
  • Tables
  • Query workspace

Module 4: Creating & Managing Datasets

  • Dataset creation
  • Dataset locations
  • Table creation
  • Schema definition
  • Table management
  • Dataset organization

Module 5: Loading Data into BigQuery

  • Batch loading
  • CSV files
  • JSON files
  • Parquet files
  • Cloud Storage integration
  • Load job management

Module 6: GoogleSQL Fundamentals

  • SELECT statements
  • WHERE clauses
  • ORDER BY
  • LIMIT
  • Aliases
  • SQL expressions

Module 7: Aggregation & Grouping

  • Aggregate functions
  • GROUP BY
  • HAVING
  • COUNT
  • SUM and AVG
  • Analytical summaries

Module 8: Joining Data

  • INNER JOIN
  • LEFT JOIN
  • FULL JOIN
  • Cross joins
  • Multiple-table joins
  • Join optimization

Module 9: Advanced GoogleSQL

  • Subqueries
  • Common table expressions
  • CASE expressions
  • Arrays
  • Structs
  • Advanced query patterns

Module 10: Window & Analytical Functions

  • Window functions
  • PARTITION BY
  • Ranking functions
  • Running totals
  • Moving calculations
  • Analytical queries

Module 11: Nested & Repeated Data

  • Nested structures
  • Repeated fields
  • Arrays
  • STRUCT data
  • UNNEST
  • Hierarchical data analysis

Module 12: Partitioned Tables

  • Partitioning concepts
  • Time-based partitioning
  • Integer-range partitioning
  • Partition pruning
  • Partition filters
  • Performance improvement

Module 13: Clustering in BigQuery

  • Clustering concepts
  • Clustered columns
  • Data organization
  • Query performance
  • Partitioning with clustering
  • Clustering strategies

Module 14: Data Transformation

  • SQL transformations
  • Data cleaning
  • Data standardization
  • Derived columns
  • Transformation pipelines
  • Analytics-ready datasets

Module 15: Views & Materialized Views

  • Logical views
  • Creating views
  • Materialized views
  • Reusable queries
  • Performance considerations
  • Analytics abstraction

Module 16: BigQuery Performance Optimization

  • Query execution plans
  • Data scanning
  • Query optimization
  • Efficient joins
  • Column selection
  • Performance monitoring

Module 17: BigQuery Cost Optimization

  • BigQuery pricing concepts
  • Bytes processed
  • Query cost estimation
  • Partition pruning
  • Storage optimization
  • Cost monitoring

Module 18: Security & Access Management

  • IAM concepts
  • Dataset permissions
  • Table permissions
  • Authorized views
  • Row-level security
  • Column-level security concepts

Module 19: BigQuery ML

  • BigQuery ML fundamentals
  • Creating ML models
  • Regression
  • Classification
  • Model evaluation
  • Predictions using SQL

Module 20: BigQuery Integration & Automation

  • Cloud Storage
  • BI tool integration
  • Scheduled queries
  • Data pipelines
  • Workflow automation
  • Analytics ecosystem

Module 21: Monitoring & Best Practices

  • Query monitoring
  • Job history
  • Performance analysis
  • Data quality
  • Naming conventions
  • Production best practices

Module 22: Practical BigQuery Analytics Project

  • Dataset creation
  • Data loading
  • SQL analysis
  • Partitioning and clustering
  • Query optimization
  • Analytics solution development
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • ✓ Live instructor on-site
  • ✓ Printed workbook & materials
  • ✓ Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • ✓ Live instructor via video call
  • ✓ Digital workbook & resources
  • ✓ Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • ✓ On-demand video lessons
  • ✓ Interactive quizzes
  • ✓ 24/7 access on any device
— What you will master

Course Objectives

01

Understand Google BigQuery architecture and cloud data warehousing concepts.

02

Create and manage datasets, tables, schemas, and analytical data structures.

03

Write GoogleSQL queries for complex data analysis and transformation.

04

Work effectively with joins, window functions, arrays, and nested data.

05

Apply partitioning and clustering to improve analytical performance.

06

Optimize BigQuery workloads for query performance and cost efficiency.

07

Implement appropriate security and access controls for analytical datasets.

08

Use BigQuery ML and automation capabilities within practical analytics workflows.

— Questions answered

Frequently Asked Questions

What is Google BigQuery?
Google BigQuery is a serverless cloud data warehouse designed to store, process, and analyze large datasets using SQL-based analytical workflows.
Who should attend this course?
The course is suitable for data analysts, data engineers, analytics engineers, BI professionals, cloud data professionals, and working professionals using large-scale data platforms.
Do I need previous SQL experience?
Basic SQL knowledge is recommended because BigQuery primarily uses GoogleSQL for querying, transforming, and analyzing data.
Which topics are covered?
The course covers BigQuery architecture, GoogleSQL, data loading, joins, window functions, nested data, partitioning, clustering, optimization, security, BigQuery ML, integration, and automation.
What practical skills will I develop?
You will develop skills in building BigQuery datasets, writing analytical SQL, processing large datasets, optimizing queries, managing costs, implementing security, and developing scalable cloud analytics workflows.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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