Data Engineering and Analytics on Google Cloud (BigQuery) Corporate Training
Instructor-led corporate training for data and analytics teams
A 24-hour instructor-led program that teaches your teams to design pipelines, enforce data quality, and publish governed analytics on Google Cloud with BigQuery, through guided hands-on labs and a capstone build.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a data-driven analytics team
The Edstellar DATA ENGINEERING & BIG DATA Capability Model
Diagnose
We map your team's current BigQuery and pipeline maturity against the analytics outcomes you need.
Standardize
We define common project layouts, naming, and quality standards for every pipeline your teams build.
Practice
Guided labs build real ingestion, transformation, and quality skills in live BigQuery environments.
Measure
Lab checkpoints and the capstone review verify each team can deliver a working governed pipeline.
Scale
We repeat the program across batches with the same standards so capability stays consistent.
Need more than one program?Get a group quote
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Program Overview: What is Data Engineering and Analytics on Google Cloud (BigQuery)?
Data Engineering & Analytics on Google Cloud (BigQuery) is an instructor-led corporate training program for data engineering and analytics teams who build on Google Cloud. Over 24 hours, participants work through BigQuery foundations, SQL optimization, ingestion pipeline design, transformation layers, and data quality engineering. The program closes with governed dashboard publishing, so teams ship analytics stakeholders can trust.
Every module pairs short concept sessions with guided labs inside live Google Cloud environments, so new techniques are practiced on realistic workloads from the first day. Instructors coach teams through query tuning, pipeline debugging, and access design, reviewing each lab before the cohort moves on. Three structured practice sessions build toward the capstone, where teams build an end-to-end analytics pipeline and defend their design choices. Feedback is specific to each team's Google Cloud projects, data sources, and governance rules.
Most analytics backlogs die in sprawl: ungoverned queries, duplicate pipelines, dashboards nobody trusts, and BigQuery bills nobody can explain. This program teaches teams to design once, govern everything, and publish analytics with lineage and access control baked in. They return treating data as a product, not a side effect.
The proof is the capstone: a working end-to-end analytics pipeline on BigQuery, built on the team's own data patterns, that ends with a cost and access review so it survives production economics. Participants defend their ingestion design, transformation logic, and governance choices in front of instructors, the way they would defend them to a CTO. What they take home is not a notebook of exercises but a pipeline blueprint, tuned queries, and the review habits that keep the next pipeline affordable and safe.
Over 24 hours, your teams move from BigQuery foundations and SQL optimization through pipeline design, data quality engineering, and governed dashboard publishing, ending with a capstone where they build an end-to-end analytics pipeline and defend their design choices. The program is tailored to your Google Cloud projects, data sources, and governance rules, and it closes with a cost and access review so pipelines stay affordable and safe in production.
What's Included in this Workshop for Your Enterprise Teams
- 1
Curriculum tailored to your stack
Modules map to your Google Cloud projects and data sources.
- 2
Hands-on labs in every module
Your team builds real pipelines and quality checks in BigQuery.
- 3
Capstone build with instructor review
Teams present a working pipeline and defend their design.
- 4
Workbooks and reusable templates
Take away query, pipeline, and governance templates.
- 5
Governance and cost guardrails
Access control, audit trails, and cost alerts covered throughout.
- 24 hours of live instructor-led training
- Guided labs in live Google Cloud environments
- Reusable query and pipeline templates
- Flexible virtual, onsite, or hybrid delivery
- Small-group batches for hands-on support
- Post-training support for your data team
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
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Data Engineering and Analytics on Google Cloud (BigQuery) Course Topics and Program Outline
A 24-hour instructor-led program across 6 modules, moving from BigQuery foundations through pipeline design and data quality engineering to governed dashboard publishing and a full end-to-end capstone build.
Built by Edstellar with Google Cloud data engineers
Built by practitioners who run production BigQuery pipelines, shaped around your stack and use cases.
We map your team's BigQuery and pipeline skills against your data goals before the first session.
Instructor-led sessions cover BigQuery SQL, pipeline design, and transformation patterns with live demos.
Guided hands-on labs let your team build ingestion, transformation, and quality checks in real environments.
The capstone has your team deliver a governed, end-to-end analytics pipeline for a real business question.
Your team finishes with a working, governed analytics pipeline reviewed by the instructor.
Delivered as
Delivered live by instructors with guided labs; each module ends with a checkpoint before the next begins.
Stand up a sandboxed Google Cloud project, create a dataset, and load a sample table from Cloud Storage with correct IAM roles.
learna)Google Cloud project setup for data workloads
b)BigQuery datasets, tables, and views
c)IAM roles for data teams and service accounts
d)Loading data from Cloud Storage into BigQuery
Rewrite a slow analytics query using partitioning and clustering, then verify the improvement in the BigQuery query plan.
learna)BigQuery standard SQL for analytics
b)Working with nested and repeated fields
c)Partitioning and clustering strategies
d)Reading the query plan and optimizing cost
Build an end-to-end ingestion pipeline that streams events through Pub/Sub into a partitioned BigQuery staging table.
practicea)Batch versus streaming ingestion patterns
b)Building pipelines with Dataflow and Pub/Sub
c)Staging layers and load job design
d)Scheduling and monitoring pipeline runs
Create a transformation layer with a data contract and a suite of automated quality checks that gate bad data.
practicea)Modeling curated marts for analytics teams
b)Authoring data contracts for upstream producers
c)Automated data quality checks and alerts
d)Handling schema evolution without breaking consumers
Publish a governed dashboard over curated marts with row-level security, then audit access and set a cost alert.
applya)Connecting BI tools and Looker Studio to BigQuery
b)Designing dashboards for business stakeholders
c)Access control and row-level security
d)Audit logging, data lineage, and cost governance
Build the full pipeline end to end, present the dashboard and cost review to the class, and defend your design choices.
applya)Scoping the business analytics question
b)Building the full ingest, transform, and quality pipeline
c)Publishing the stakeholder dashboard
d)Cost review and production handover plan
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Practical skills your team practices in guided labs and proves in the capstone build.
BigQuery SQL fluency
Write efficient standard SQL with nested fields, partitioning, and clustering for fast, low-cost queries.
Ingestion pipeline design
Build batch and streaming ingestion pipelines that land reliable data in BigQuery on schedule.
Transformation modeling
Model curated marts that analytics teams can query with confidence.
Data quality engineering
Author data contracts and automated checks that gate bad data before it reaches dashboards.
Schema evolution management
Evolve schemas safely so upstream changes never break downstream consumers.
Governed dashboard publishing
Publish dashboards with access control, row-level security, and audit trails.
Cost and access governance
Apply billing controls, IAM best practices, and monitoring to keep BigQuery spend predictable.
Teams that Benefit from this Data Engineering and Analytics on Google Cloud (BigQuery) Group Training
This program is built for data and analytics teams that need production-grade skills on Google Cloud and BigQuery. Content adapts to each role and experience level, so every cohort practices the workflows it owns, from ingestion to governed dashboards.
Build pipelines your team can run in production
Data engineers learn ingestion, transformation, and quality patterns that map directly onto your BigQuery workloads.
Workflow friction
- Brittle pipelines that break often
- Rising BigQuery query costs
- Untested transformation logic
How the program helps
- Production-ready pipeline patterns
- Reusable quality check templates
- Cost-aware design practices
Practice use cases
- Ingestion pipelines
- Schema evolution
- Pipeline monitoring
Turn trusted data into decisions faster
Analytics and BI teams learn to model curated marts and publish governed dashboards on BigQuery without waiting on tickets.
Workflow friction
- Slow dashboard turnaround
- Inconsistent metric definitions
- Stale or untrusted data
How the program helps
- Self-serve curated marts
- Faster dashboard delivery
- Consistent metric definitions
Practice use cases
- Curated marts
- Dashboard design
- Metric layers
Standardize the platform your teams build on
Platform teams learn access control, cost governance, and environment patterns that keep every BigQuery workload safe.
Workflow friction
- Sprawling ungoverned projects
- Access granted too broadly
- Surprise cloud bills
How the program helps
- Standardized project layouts
- Enforced access controls
- Predictable cloud spend
Practice use cases
- IAM design
- Cost alerts
- Environment templates
Keep analytics governed without slowing teams down
Governance teams learn data contracts, lineage, and audit practices that fit naturally into the engineering workflow.
Workflow friction
- No contract with producers
- Invisible data lineage
- Unauditable access
How the program helps
- Contracts with producers
- Visible data lineage
- Auditable access trails
Practice use cases
- Data contracts
- Lineage tracking
- Audit reviews
Each cohort's labs and capstone are mapped to the workflows the attending teams own.
Prerequisites: Data engineers, analytics engineers, and BI developers get the most from this program. Familiarity with SQL and cloud concepts helps, but is not required; we cover BigQuery fundamentals before moving to advanced labs. Each cohort's labs and capstone are adapted to the team's Google Cloud setup and data sources.
Data analyst to data engineer
Progression follows the curriculum: querying and ingestion foundations first, then transformation and quality depth, finishing with governed publishing and production ownership.
A program that grows with data careers, from SQL basics to owning production-grade pipelines.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every delivery mode is live and instructor-led, with the same hands-on labs and capstone review.
Hybrid Training
Some of your team joins in person and the rest join live online, with shared lab environments and one instructor for all.
- One instructor for the whole cohort
- Shared cloud lab environments
- Same capstone review for everyone
- Flexible attendance across offices
Onsite Training
The instructor runs sessions at your office, with lab support in the room while your team builds on your Google Cloud projects.
- In-person lab support
- Whiteboard design sessions
- Uses your Google Cloud setup
- Team building alongside learning
Live Virtual Training
Live virtual sessions over Zoom or Teams with breakout rooms for labs, screen-shared demos, and the same capstone review.
- Live instructor-led sessions
- Breakout rooms for lab work
- Screen-shared live demos
- No travel required
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, dates, and learning goals through the form above. A training advisor responds with a tailored quote, usually within one business day.
Yes. Use the form above to request a discovery call. We map your data stack, current skills, and goals before finalizing the curriculum.
Yes. The program runs live virtual with instructor-led labs, and can also be delivered onsite or in a hybrid format for distributed teams.
Yes. Onsite delivery is available for teams that prefer in-person sessions. Your team needs only laptops with cloud access; we handle the lab setup.
The standard program is 24 hours of instructor-led training, typically spread over three to four days. Timings can be adjusted to suit your team's calendar.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
BigQuery engineering builds on core Google Cloud knowledge, so that comes first. After your team can engineer and analyse data on BigQuery, Airflow schedules the pipelines and dbt brings tested, version-controlled models to the warehouse.
Why Choose Edstellar as Your Data Engineering and Analytics on Google Cloud (BigQuery) Corporate Training Provider
Edstellar delivers instructor-led corporate training that moves your data teams from skills to measurable pipeline outcomes.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
We tailor modules to your Google Cloud projects, data sources, and governance rules so labs mirror real work.
Proven Methodology
A learn-practice-apply structure moves teams from BigQuery basics to production-ready pipelines with instructor feedback.
Practice-Based Learning
Guided hands-on labs in live BigQuery environments build the ingestion, transformation, and quality skills you need.
Expert Trainers
Sessions are led by practitioners who run production data pipelines on Google Cloud and review your team's capstone.
Global Scale
One consistent curriculum and quality bar across regions, with flexible scheduling for distributed data teams.
Measurable Outcomes
Lab checkpoints and a capstone review show exactly what your team can build on BigQuery before the program ends.
Build BigQuery Analytics Pipelines With Verified Data and Governed Delivery
Edstellar connects BigQuery foundations with query optimization, event ingestion, and transformation quality. Participants develop curated data and secure dashboards before reviewing costs and access. The final pipeline defense tests whether design choices are supported by query evidence, validation results, and a clear understanding of operational and governance requirements.
- Step 01
Consult
We assess SQL and cloud familiarity to identify gaps in BigQuery data layout, ingestion, quality checks, access control, and query-cost interpretation.
- Step 02
Plan
We establish datasets and IAM before SQL optimization and Pub/Sub ingestion, then transformations, contracts, and governed dashboards within the end-to-end pipeline.
- Step 03
Align
We align Google Cloud analytics instruction with data, analytics-engineering, and BI roles, confirming sandbox access and SQL support matched to experience.
- Step 04
Customize
We tailor sample event streams, analytical queries, and reporting marts to relevant data sources while keeping cost assumptions and access requirements explicit.
- Step 05
Execute
Participants optimize queries, build ingestion, and enforce quality checks, receiving feedback on query plans, rejected data, row-level access, and dashboard consistency.
- Step 06
Evaluate
We assess the pipeline for reliable transformations, demonstrated quality controls, appropriate access boundaries, and evidence-supported explanations of performance and cost choices.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Data Engineering & Analytics on Google Cloud (BigQuery) Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Data Engineering and Analytics on Google Cloud (BigQuery) Training for Your Team
Tell us about your team size, timeline, and Google Cloud setup, and we will tailor the program to your data workflows.