Live classes, 2026 edition
Build real batch and streaming pipelines on BigQuery, Dataflow, Dataproc, Cloud Composer and Pub/Sub, and prepare for the Google Cloud Professional Data Engineer certification.
Master GCP data engineering with live classes!
Live batch, weekday classes
Description:
This course is designed to provide live classes for aspiring data engineers looking to specialize in Google Cloud Platform (GCP) technologies. Participants will gain hands-on experience in designing, building, and maintaining data processing systems on GCP.
Key Highlights:
What you will learn:
A collaborative coaching program. Sagar and Manoj teach together and bring the production data work they do every day into class.
Founder, GeekCoders · Lead Data Engineer
Teaching data engineering through GeekCoders since 2022, and a founding member of a startup building a data platform on Databricks since 2024. Brings the same production approach to Google Cloud: pipelines, failures, governance and interviews, in the order you need them.
Co-instructor · Senior Data Engineer, Deloitte USI
Senior Data Engineer at Deloitte USI with 6+ years building data platforms. Has taught more than 1,000 students and leads the labs, troubleshooting sessions and interview practice.
Every module follows the same rhythm: concepts with the internals explained, a hands-on build, a lab on realistic data, and an independent challenge. Tap a module to see what it covers.
For anyone comfortable with Python who is new to data engineering or to Google Cloud.
Lab: write 20 progressive SQL queries and build a cohort-retention analysis on a 1M-row dataset.
Lab: build an API-to-JSON-to-CSV script, then set up a GCP project with budget alerts.
Security, storage and data lake design, BigQuery, streaming and batch pipelines, orchestration and monitoring.
Lab: create least-privilege service accounts, store credentials in Secret Manager, and configure Workload Identity.
Lab: build a three-zone data lake on GCS, stand up a Cloud SQL instance, and stream its changes into BigQuery with Datastream.
Lab: load a 10M-row dataset, build a star schema, and analyse query execution.
Lab: cut a simulated BigQuery bill by 60% or more using partitioning, clustering and scheduled queries.
Lab: build a batch GCS-to-BigQuery pipeline and a streaming Pub/Sub-to-BigQuery pipeline, then deploy a Flex Template.
Lab: run a PySpark ETL job on Dataproc, then build an event-driven order pipeline at 50K events per hour.
Lab: build a 15-task production DAG for a daily data-warehouse refresh, with SLA alerting.
Lab: build an end-to-end observability dashboard with alerting, and run a cost-optimisation audit.
CI/CD, infrastructure as code, data-quality gates and incident response, built the way production teams run them.
Lab: build a CI/CD pipeline that tests and deploys a Dataflow Flex Template behind a quality gate.
The capstone, Professional Data Engineer exam strategy, mock exams and career preparation.
Lab: submit a deployed capstone, pass two timed mock exams, and complete a reviewed resume.
Each tool below is used in a lab or a project, not only shown on a slide.
Each project matches the work GCP data engineers do at product companies and MNCs, and ships with a GitHub repository, a README, an architecture diagram and measurable impact statements.
Pub/Sub, Dataflow, BigQuery
Stream clickstream and order events through a Beam pipeline into a partitioned BigQuery warehouse, with deduplication, late-data handling and a Looker Studio dashboard, deployed through GitHub Actions.
GCS, Dataproc, BigQuery
Land raw CSVs in GCS, transform them with PySpark on Dataproc through Bronze, Silver and Gold, and serve a curated BigQuery layer tuned with partitioning and clustering.
Cloud Composer, Dataform, Great Expectations
Schedule multi-step ETL DAGs with Cloud Composer, run Dataform SQL transformations, enforce quality gates, and alert on SLA breach or failure.
Cloud SQL, Datastream, BigQuery, Terraform
Stream live changes from Cloud SQL (MySQL) into BigQuery with Datastream, with VPCs, IAM, buckets and datasets provisioned and torn down by Terraform.
An end-to-end platform, set for each cohort. A typical brief: an e-commerce retailer with hundreds of thousands of daily orders and millions of product events, ingested through Pub/Sub, processed with Dataflow and Dataproc, modelled in BigQuery, orchestrated with Composer and shipped through CI/CD with monitoring.
Google's flagship data engineering certification validates that you can design, build, secure and monitor data processing systems on Google Cloud. It maps directly to the BigQuery, Dataflow, Dataproc and Composer work in this program.
Google administers and awards the certification. You register and pay for the exam directly with Google, separately from the course fee, online or at an authorised test centre.
Exam results, job placement or a specific salary. Pay depends on experience, employer, city and the market. What you get is current, job-ready skills, certification preparation and a portfolio that helps you get shortlisted.
Have a question before you enrol? Join our WhatsApp group for batch updates and Q&A: Join our WhatsApp group
Every module, lab, project and the certification mapping in one guide you can share with your manager.
