Google BigQuery Data Analytics and SQL Reporting Training Course

Course overview

Teams often hold valuable operational, customer, finance and product data in BigQuery but struggle to turn it into reliable reporting. Analysts may rely on copied spreadsheets, inefficient SQL, inconsistent metric definitions or dashboards that scan more data than necessary. This course addresses the practical work of querying cloud-scale data, validating results, controlling query cost and publishing reports that decision-makers can trust.

Participants learn to use Google BigQuery and GoogleSQL to inspect datasets, write and optimise analytical queries, join large tables, aggregate measures, handle dates and nested records, and build reusable reporting logic. The course covers window functions, common table expressions, query parameters, views, scheduled queries, partitioning and clustering, query execution plans, access controls and cost-aware design. Participants also connect BigQuery results to Looker Studio for governed, refreshable dashboards.

Delivery combines instructor demonstrations with guided SQL labs based on a realistic multi-table business dataset. Each day participants write, test and improve queries in BigQuery, diagnose inaccurate or expensive results, and discuss design choices with peers. By the end of the week, each participant produces a documented BigQuery reporting solution: a set of validated GoogleSQL queries, a reusable view or scheduled output, a cost-conscious table design recommendation and a Looker Studio report for a defined business audience.

The course is suited to analysts, reporting specialists, data professionals and technical business users who already work with relational data and need to deliver production-ready analysis in Google Cloud. It is particularly valuable for organisations moving reporting workloads from spreadsheets, on-premise databases or manually maintained extracts into BigQuery.

Course objectives

By the end of this course, participants will be able to:

• Write GoogleSQL queries using SELECT, JOIN, GROUP BY, CASE and common table expressions against BigQuery datasets
• Analyse time-based performance using date functions, rolling measures and window functions
• Query nested and repeated fields using STRUCT, ARRAY and UNNEST patterns
• Validate reporting metrics by reconciling row counts, joins, null handling and aggregation logic
• Optimise BigQuery workloads using partition pruning, clustering, query plans and dry-run cost estimates
• Create reusable reporting assets with views, query parameters and scheduled queries
• Apply BigQuery access controls and authorised-view principles to protect reporting data
• Build a Looker Studio dashboard backed by validated BigQuery queries and documented metric definitions

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to turn BigQuery tables into accurate, repeatable business analysis
• BI Analysts — who build governed datasets and dashboards for operational and management reporting
• Reporting Analysts — who are replacing spreadsheet-based reporting with scheduled cloud queries
• Business Intelligence Developers — who need to optimise GoogleSQL and prepare dashboard-ready data models
• Data Engineers — who support analysts and need practical BigQuery query, table-design and access patterns
• Finance, Operations or Product Analysts — who query organisational data and must explain metrics to decision-makers

Requirements and prerequisites

Participants should be comfortable working with tabular data and should already understand basic SQL concepts: SELECT statements, filtering with WHERE, simple joins, aggregates and GROUP BY. Experience with a relational database, spreadsheet analysis or another SQL platform such as SQL Server, PostgreSQL or Snowflake is suitable preparation. Participants should also be able to interpret business measures such as revenue, orders, customers or service volumes. Prior Google Cloud, BigQuery, Python, data engineering and dashboard-development experience is not required. The course teaches BigQuery-specific workflows from the ground up, but it is not designed as a first introduction to SQL.

Training methodology

The instructor introduces each BigQuery capability through a short demonstration, followed by guided work in Google Cloud using a shared business dataset. Participants write GoogleSQL in the BigQuery editor, compare alternative query patterns, inspect execution details and calculate likely scan costs before running queries. Scenario-based labs cover sales, customer and operational reporting requirements, with peer review of metric logic and dashboard choices. Daily exercises build toward an end-of-course reporting solution, followed by an application-planning session in which participants identify suitable datasets, reporting audiences and cost controls for their own workplace.

Course outline

Day 1: BigQuery foundations and reliable GoogleSQL

• Google Cloud project, dataset and table hierarchy
• BigQuery console navigation and query editor workflow
• GoogleSQL SELECT, aliases, filters and sort order
• Aggregations with GROUP BY, HAVING and conditional CASE logic
• INNER, LEFT and FULL joins with reporting grain control
• Null values, duplicate records and data-type conversion
• Query validation using sample limits and reconciliation checks

Workshop: Build and validate a daily sales and customer summary query, producing a checked set of core reporting measures.

Day 2: Analytical SQL for business reporting

• Common table expressions for staged analytical logic
• Date, timestamp and time-zone functions in GoogleSQL
• Window functions using OVER, PARTITION BY and ORDER BY
• Ranking, running totals and period-on-period comparisons
• LEAD, LAG and rolling-window calculations
• Reusable query parameters and named query patterns
• Metric definitions and dimensional reporting grain

Workshop: Create a monthly performance report with year-on-year comparison, rolling revenue and ranked product categories.

Day 3: BigQuery data structures and performance control

• Schema inspection and table metadata in BigQuery
• STRUCT and ARRAY data types for nested source data
• UNNEST patterns for repeated records
• Partitioned tables and partition-filter requirements
• Clustered tables and selective query predicates
• Query plan stages, bytes processed and slot consumption
• Dry runs, maximum bytes billed and cost estimation

Workshop: Refactor an expensive customer-events query using UNNEST, partition filters and clustering recommendations, then document the cost reduction.

Day 4: Reusable reporting pipelines and governed access

• Logical views and standard views for reusable SQL
• Materialized view use cases and refresh behaviour
• Scheduled queries and destination table configuration
• Incremental reporting tables and append-versus-overwrite choices
• BigQuery IAM roles at project, dataset and table level
• Authorised views for controlled data sharing
• Data quality checks and report-refresh monitoring

Workshop: Create a governed reporting view and scheduled daily summary table, including an access and refresh-control design.

Day 5: Dashboard delivery and production reporting design

• Preparing dashboard-ready datasets and semantic fields
• Connecting Looker Studio to BigQuery
• Calculated fields, filters and date controls in Looker Studio
• Dashboard layout for executive and operational audiences
• Query efficiency considerations for dashboard interactions
• Documenting metric lineage, assumptions and limitations
• Production reporting checklist and stakeholder handover

Workshop: Deliver a Looker Studio management dashboard backed by BigQuery, with metric definitions, refresh notes and a prioritised workplace implementation plan.

Tools & standards covered

Google BigQuery, Google Cloud Console, Looker Studio, GoogleSQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions