Course Overview of BigQuery for Data Analysts

BigQuery for data analysts course is designed for data analysts looking to leverage BigQuery for high-performance data analysis. Through a mix of videos, hands-on labs, and demos, you will learn to navigate the BigQuery ecosystem, write optimized SQL for large-scale datasets, and utilize advanced features like Dataform and BigQuery Studio to derive actionable business insights.

After completing BigQuery for Data Analysts

  • Communicate the value and architecture of the BigQuery enterprise data warehouse.
  • Write complex SQL queries to analyze and transform petabyte-scale datasets.
  • Ingest data from various sources, including external and cloud-native databases.
  • Automate and version-control data pipelines using Dataform.
  • Utilize generative AI assistants to accelerate SQL development and data exploration.
  • Build interactive dashboards and reports using Google’s visualization tools.

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Key Features of BigQuery for Data Analysts

  • Comprehensive Learning Path: Includes 9 modules, 44 videos, 10 hands-on labs, and 3 live-action demos.

  • SQL-First Approach: Deep dive into cleaning, transforming, and analyzing data using standard SQL. 

  •  Modern Data Engineering: Integration with Dataform for developing scalable data transformation pipelines.

  • Next-Gen AI Assistance: Hands-on experience with Gemini and Duet AI (BigQuery Studio) for SQL explanation and generation. 

  • Visualization Ecosystem: Learn to bridge the gap between raw data and insights using Connected Sheets and Looker Studio.

Who should Attend BigQuery for Data Analysts?

  • Data analysts who need to use BigQuery for business intelligence and data exploration.

Prerequisites of BigQuery for Data Analysts

Completion of "Introduction to Data Analytics on Google Cloud" or equivalent experience.

Why choose CloudThat as your training partner for BigQuery for Data Analysts?

  • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
  • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
  • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
  • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
  • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
  • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
  • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
  • Positive Reviews and Testimonials: Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training. 

Learning Objectives of BigQuery for Data Analysts

  • Architecture & Value: Discuss BigQuery's data analytics features and storage-compute separation.
  • Data Manipulation: Clean, transform, and query datasets using standard SQL functions. 
  • Ingestion Strategies: Manage data ingestion and external data source connections.
  • Pipeline Orchestration: Create repositories, workspaces, and SQL workflows in Dataform.
  • Unified Analytics: Use BigQuery Studio for asset management and embedded AI assistance.
  • Insights Visualization: Apply visualization principles to data via Looker Studio.

Course Outline for BigQuery for Data Analysts Download Course Outline

Lecture Content

  • BigQuery serverless architecture and decoupled storage vs. compute engine design
  • Purpose, core business value, and cloud enterprise scalability advantages
  • Navigating the storage tier, computing model, and key administrative features

Learning Objectives

  • Articulate the purpose, value proposition, and decoupled architecture of BigQuery
  • Explain how serverless execution provides multi-petabyte analysis capabilities

Lecture Content

  • Writing highly performant GoogleSQL syntax for querying large datasets
  • Managing common data patterns: filtering, aggregating, and sorting complex schemas
  • Cost estimation strategies, structural query dry runs, and billing awareness

Learning Objectives

  • Construct standard SQL queries to extract insights from massive, multi-million row tables
  • Evaluate query resource consumption and estimate analysis costs prior to execution

Lab Content

  • Querying and Filtering Large-Scale Datasets in BigQuery

Lecture Content

  • Advanced SQL techniques: Analytic functions, Window functions, and conditional handling
  • De-duplicating data records, parsing structural anomalies, and handling null exceptions
  • Flattening nested structures, arrays, and complex semi-structured JSON records

Learning Objectives

  • Apply advanced SQL windowing and analytic functions to clean messy source data
  • Manipulate complex structural schemas, arrays, and nested JSON fields natively in SQL

Lab Content

  • Data Cleansing and Schema Transformations Using Advanced SQL

Lecture Content

  • Ingestion modalities: Batch loading, streaming inserts, and schema auto-detection
  • Setting up external data sources and federated querying (Cloud Storage, Bigtable, Cloud SQL)
  • Standard file format ingestions: Parquet, Avro, CSV, and JSON schema configurations

Learning Objectives

  • Provision new datasets and load files from external data storage repositories
  • Execute federated queries against external sources without moving data into BigQuery

Lab Content

  • Loading Data and Querying External Federated Storage Systems

Lecture Content

  • Bridging the gap between spreadsheet modeling and big data using Connected Sheets
  • Connecting BigQuery to Looker Studio for enterprise dashboard engineering
  • Best practices for caching query data, performance optimization, and asset sharing

Learning Objectives

  • Analyze millions of rows of data using familiar spreadsheet formulas via Connected Sheets
  • Architect real-time, interactive data visualizations and executive dashboards in Looker Studio

Lab Content

  • Creating Interactive Enterprise Reports with Connected Sheets and Looker Studio

Lecture Content

  • Introduction to Dataform for scalable, version-controlled SQL workflow orchestration
  • Managing Dataform repositories, initializing continuous release environments, and workspaces
  • Defining dataset definitions, dependency graphs, assertions, and testing scripts via SQLX

Learning Objectives

  • Modularize and build production-ready data transformation pipelines within Dataform
  • Implement Git-based version control, repository branches, and data testing assertions

Lab Content

  • Organizing and Executing Scalable SQL Workflows in Dataform

Lecture Content

  • Navigating the unified analytics workspace for data scientists, engineers, and analysts
  • Accelerating development cycles using built-in generative AI (Gemini / Duet AI code assistance)
  • Architecting real-time streaming pipelines using BigQuery continuous queries

Learning Objectives

  • Use BigQuery Studio as a centralized collaborative interface for data and AI workloads
  • Leverage Gemini generative AI tools to explain, build, and optimize complex queries
  • Formulate continuous streaming queries to analyze continuous, real-time event logs

Lab Content

  • Leveraging Gemini Assistance and Continuous Queries in BigQuery Studio

Lecture Content

  • Comprehensive retrospective review of key data analysis and data engineering topics
  • BigQuery design patterns for production architectures, enterprise partitioning, and clustering
  • Google Cloud-recommended analytics governance, safety, and operational best practices

Learning Objectives

  • Synthesize core capabilities of BigQuery, Dataform, and visualization tools to build scalable, end-to-end data analytics solutions

Certification Details of BigQuery for Data Analysts

    CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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FAQs for BigQuery for Data Analysts

It focuses on using BigQuery for data ingestion, transformation, analysis, and visualization.

Yes, it includes labs on using Gemini and Duet AI assistance within BigQuery Studio.

Dataform is a tool covered in Module 06 for developing scalable data transformation pipelines.

Yes, the course includes 10 hands-on labs, including Connected Sheets and Looker Studio exercises.

The ILT version is delivered over 2 days.

Labs are conducted in a provided sandbox environment with pre-loaded datasets.

Looker Studio and Connected Sheets (Google Sheets integration).

Yes, specifically using Dataform to create repositories and execute workflows.

A new unified interface for analytics, which is explored in Module 07.

While useful for engineers, it is specifically tailored to the workflow of a Data Analyst.

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