Project 01
Retail e-commerce analytics pipeline
Turn order, product, and customer data into daily sales and customer-behaviour dashboards.
Pipeline
- GCS
- Dataproc (PySpark)
- Cloud Composer
- BigQuery
- Looker
- CSV / JSON ingestion
- Daily ETL
- Sales dashboards
Data Engineering · Data pipelines on Google Cloud
Professional Data Engineer certification prep
Build production data pipelines on Google Cloud
Start from SQL and Python, then master BigQuery, Dataflow, Dataproc, PySpark, Cloud Composer, and dbt. Finish with three end-to-end capstone projects you can show in interviews.
- Program fee
- ₹25,000
- EMI available on credit cards
- Next batch
- Coming soon
- Timings
- 7 to 8:15 AM IST
- Weekday
- Duration
- 2.5 Months
- Live classes
Program fee
₹25,000
EMI available on credit cards
- Next batch
- Coming soon
- Timings
- 7 to 8:15 AM IST
- Weekday
- Duration
- 2.5 Months
- Live classes
- syllabus modules
- 22
- syllabus modules
- Google Cloud data tools
- 15+
- Google Cloud data tools
- capstone projects
- 3
- capstone projects
BigQuery · retail_analytics
SELECT region, SUM(amount) AS revenue FROM retail.orders WHERE order_date >= '2026-01-01' GROUP BY region ORDER BY revenue DESC;
Query results
- South₹4.8 Cr
- West₹4.1 Cr
- North₹3.2 Cr
- East₹2.3 Cr
Pipeline: GCS → Dataproc → Composer → BigQuery → Looker
Our alumni work at leading companies worldwide
+many more companies across India, the US & beyond
The stack
The Google Cloud data stack you will use
Every stage of a modern data platform, hands-on, from landing raw data to the dashboard.
Ingest
- Cloud Storage
- Pub/Sub
Process
- Dataflow (Beam)
- Dataproc
- PySpark
- Databricks
Transform
- dbt
- Cloud Data Fusion
Store & analyse
- BigQuery
- Bigtable
- Cloud Spanner
- BigQuery ML
Orchestrate
- Cloud Composer (Airflow)
Visualise
- Looker
- LookML
Ops, governance & CI/CDCloud LoggingCloud MonitoringData CatalogPolicy TagsGitCloud BuildJenkinsGitHub Actions
Who it's for
Built for people moving into data engineering
Database engineers
Move beyond traditional RDBMS into BigQuery and Spanner.
Big data / Hadoop engineers
Upskill into Google Cloud's modern data stack.
ETL / data warehouse engineers
Design scalable pipelines with Dataflow and Data Fusion.
Application programmers
Work with real-time data and analytics.
Test engineers
Transition into high-growth data engineering roles.
Data analysts
Step into advanced engineering and automation.
Fresh graduates with an interest in cloud and data can join too: we start from fundamentals and build step by step.
Capstone projects
Three end-to-end projects, three industries
Each project runs the full pipeline, from raw data to dashboards, on Google Cloud. Add them to your GitHub portfolio as real-world case studies.
Project 02
Online banking: fraud detection & customer insights
Detect anomalies in transaction logs, run hourly fraud checks, and report on risk, with governed sensitive data.
Pipeline
- GCS
- Dataproc (Spark ML)
- Cloud Composer
- BigQuery
- Looker
- Anomaly detection
- Data Catalog
- Policy Tags
Project 03
Healthcare: patient & hospital analytics
Join de-identified patient, admission, and lab data to track bed utilisation, patient inflow, and predicted wait times.
Pipeline
- GCS
- Dataproc (PySpark)
- Spark ML
- Cloud Composer
- BigQuery
- Looker
- Wait-time prediction
- Weekly reporting
- Data Catalog
Curriculum
What you'll learn
22 syllabus modules, from SQL and Python to certification, grouped into ten areas.
- 01
Cloud, SQL, and Python foundations
Syllabus M1–M3Get set up on Google Cloud and build the SQL and Python every data engineer needs.
- GCP projects & CLI
- Joins & window functions
- Query optimization
- Python & pandas
- GCP client libraries
- 02
Data lake and data warehouse
Syllabus M4–M5Land data in Cloud Storage and model it in BigQuery.
- GCS data lake
- BigQuery loading & MERGE
- Partitioning & clustering
- External tables
- Views & materialized views
- 03
Orchestration with Cloud Composer
Syllabus M6Schedule and run pipelines end to end with managed Airflow.
- DAGs & operators
- Sensors & branching
- XCom & templating
- Secrets
- Retries, SLAs & alerts
- 04
Transformation with dbt
Syllabus M7Build, test, and document ELT models on BigQuery.
- Models, seeds & snapshots
- Tests
- Jinja & macros
- Docs
- Orchestrated dbt runs
- 05
Big data processing with Spark
Syllabus M8–M10Process data at scale on Dataproc with PySpark, batch and streaming.
- Big data concepts
- Dataproc clusters & jobs
- PySpark DataFrames
- Performance tuning
- Structured streaming
- 06
Databricks on Google Cloud
Syllabus M11Run enterprise data engineering workloads in Databricks.
- Workspaces & clusters
- Notebooks & Spark SQL
- Workflows
- BigQuery & GCS integration
- 07
Streaming with Pub/Sub and Dataflow
Syllabus M12–M13Ingest events in real time and build batch and streaming pipelines.
- Topics & subscriptions
- Dead-letter topics
- Apache Beam
- Batch & streaming pipelines
- 08
Governance and databases
Syllabus M14–M16Catalog and secure data, and pick the right database for the workload.
- Data Catalog
- Policy tags
- Bigtable
- Cloud Spanner
- 09
Operations and visual ETL
Syllabus M17–M18Monitor and debug pipelines, and build visual pipelines where they fit.
- Logging & monitoring
- Debugging Dataflow, Dataproc & Composer
- Pipeline health dashboards
- Cloud Data Fusion
- 10
Analytics, CI/CD, and certification
Syllabus M19–M22Serve insights in Looker, try BigQuery ML, ship with CI/CD, and prepare for the exam.
- Looker & LookML
- BigQuery ML basics
- Git & CI/CD
- Professional Data Engineer prep
Outcomes
Where it leads
Certification path
- Google Cloud Professional Data Engineer
Roles this prepares you for
- Google Cloud Data Engineer
- Big Data Engineer
- Cloud Data Platform Engineer
- Analytics Engineer
- DataOps / Pipeline Engineer
Frequently asked questions
No prior experience is required. We start with SQL and Python fundamentals before moving into Google Cloud and big data tools.
Still have questions? Ask us on WhatsApp
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