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Master GCP Data Engineering — BigQuery, Dataflow, Dataproc & Pub/Sub
Master Google Cloud Data Engineering — BigQuery for analytics, Dataflow for ETL, Dataproc for Spark, and Cloud Composer for orchestration — all with Trainer Venu. Includes GCP Professional Data Engineer exam prep.
No prior experience needed
7-day money-back guarantee
Placement support included
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₹20,000
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Trainer Venu's team will call you within 2 hours.
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🎥 Live Online + Recorded Sessions
🌐 Real GCP Project Labs
📂 4 End-to-End Projects
📜 GCP PDE Cert Prep
🤝 Placement Support
♾️ Lifetime Recording Access
✅ Free Demo Before Enroll
Who Is This For
Is This Course Right For You?
🌐
Cloud Engineers
Build production GCP data pipelines using BigQuery, Dataflow and Dataproc.
📊
BigQuery Analysts
Move from SQL analysis to building full data engineering pipelines on GCP.
⚡
PySpark Developers
Run Spark workloads on Dataproc with BigQuery as the data warehouse.
🎓
Freshers
Target GCP data engineering roles at Google, Wipro, TCS with great salaries.
🔄
Airflow Engineers
Add Cloud Composer (managed Airflow) expertise to your skill set.
🏢
Data Architects
Design enterprise analytics platforms on Google Cloud.
Tools Covered
📊 BigQuery
🌊 Dataflow (Apache Beam)
⚡ Dataproc (Spark)
📨 Cloud Pub/Sub
🔄 Cloud Composer
🗄️ Cloud Storage (GCS)
🔍 Looker Studio
🧊 BigLake
📋 Data Catalog
🔐 IAM & VPC
🔥 Databricks on GCP
🐍 PySpark / Apache Beam
📜 GCP PDE Cert
Course Curriculum
9 Modules — Key Concepts
Here are the core topics you'll master. Each module includes hands-on labs with real GCP Data Engineering access.
Module 01
BigQuery — Cloud Data Warehouse
- BigQuery architecture — slots, reservations, datasets
- Partitioned and clustered tables for cost optimization
- BigQuery ML — train ML models with SQL
- BigQuery Omni — query S3/Azure data
- Authorized Views and row-level security
Module 02
Dataflow — Serverless ETL
- Apache Beam programming model — PCollections, PTransforms
- Batch and streaming Dataflow pipelines
- Dataflow Flex Templates — reusable pipelines
- Dataflow → BigQuery — streaming inserts
- Auto-scaling and windowing strategies
Module 03
Dataproc — Managed Spark
- Dataproc cluster setup — master, worker nodes
- PySpark jobs on Dataproc
- Dataproc Serverless — no cluster management
- Dataproc Metastore — Hive-compatible catalog
- BigQuery connector for Dataproc
Module 04
Cloud Pub/Sub & Streaming
- Pub/Sub topics, subscriptions, push vs pull
- Pub/Sub to Dataflow streaming pipelines
- Pub/Sub → BigQuery direct subscription
- Dead-letter topics and retry policies
- Eventarc — event-driven pipelines
Module 05
Cloud Composer — Orchestration
- Cloud Composer = managed Apache Airflow on GCP
- DAG deployment to Cloud Composer
- GCP operators — BigQuery, GCS, Dataflow, Dataproc
- Composer environments — Small, Medium, Large
- Monitoring with Cloud Monitoring
Module 06
Data Catalog & Governance
- Data Catalog — search, tag, lineage
- BigLake — unified access control
- Column-level security in BigQuery
- VPC Service Controls — data perimeter
- Dataplex — data mesh on GCP
M01
GCP Fundamentals for Data Engineers
⏱️ 4 Hours● Beginner
▾
GCP Console, projects, billing accounts
IAM — service accounts, roles, custom roles
Cloud Storage (GCS) — buckets, lifecycle, ACLs
VPC and private connectivity for data services
Cloud SDK — gcloud, gsutil, bq CLI
Cloud Monitoring and Logging basics
🔬 GCP Environment Setup📝 Quiz: GCP Fundamentals
M02
BigQuery — Enterprise Data Warehouse
⏱️ 10 Hours● Intermediate
▾
BigQuery architecture — serverless, columnar storage
Datasets, tables, views — organization
Partitioned tables — date, range, ingestion-time
Clustered tables — multi-column clustering
BigQuery DML — INSERT, UPDATE, DELETE, MERGE
BigQuery Scripting — variables, loops, procedures
BigQuery ML — train models with SQL
BigQuery Omni — cross-cloud queries
Authorization — row-level, column-level security
Cost optimization — partitioning, clustering, reservations
🔬 BigQuery Data Warehouse Build🔬 BigQuery ML Lab🏗️ Project: BigQuery Analytics Platform
M03
Google Cloud Storage & Data Lake
⏱️ 4 Hours● Beginner
▾
GCS storage classes — Standard, Nearline, Coldline
Object lifecycle management and versioning
Signed URLs and IAM access control
GCS as data lake — Parquet, ORC, Avro, Delta
Gsutil parallel uploads and transfers
GCS notifications — Pub/Sub and Cloud Functions
🔬 GCS Data Lake Setup
M04
Cloud Dataflow — Apache Beam ETL
⏱️ 9 Hours● Advanced
▾
Apache Beam fundamentals — PCollections, transforms
ParDo, GroupByKey, CoGroupByKey, Combine
Batch Dataflow pipelines — GCS→BigQuery
Streaming Dataflow — Pub/Sub→BigQuery
Windowing — Fixed, Sliding, Session windows
Watermarks and late data handling
Dataflow Flex Templates — containerized pipelines
Dataflow Shuffle — Batch Pipeline optimization
🔬 Batch Pipeline: GCS→BigQuery🔬 Streaming: Pub/Sub→Dataflow→BQ🏗️ Project: Real-time Dataflow Pipeline
M05
Cloud Dataproc — PySpark on GCP
⏱️ 8 Hours● Intermediate
▾
Dataproc cluster — master, worker, preemptible nodes
Submit PySpark, Hive, SparkSQL jobs
Dataproc Serverless — no cluster management
Dataproc Metastore — fully managed Hive Metastore
BigQuery connector — Spark→BigQuery reads/writes
Cloud Storage connector — HDFS replacement
Initialization actions — custom packages
Autoscaling policies for cost optimization
🔬 PySpark on Dataproc Lab🏗️ Project: Dataproc + BigQuery Pipeline
M06
Cloud Pub/Sub & Event-Driven Pipelines
⏱️ 6 Hours● Advanced
▾
Pub/Sub architecture — topics, subscriptions
Push vs Pull subscriptions
Pub/Sub ordering keys and message deduplication
Dead-letter topics — error handling
Pub/Sub → BigQuery direct subscription
Eventarc — trigger Cloud Functions/Run from events
Real-time CDC: Pub/Sub → Dataflow → BigQuery
🔬 Pub/Sub Streaming Pipeline
M07
Cloud Composer — Orchestration
⏱️ 5 Hours● Intermediate
▾
Cloud Composer = managed Airflow on GCP
Composer 2 — auto-scaling, workload identity
DAG deployment — GCS-backed storage
GCP operators — BigQueryOperator, GCSToGCSOperator, DataflowOperator, DataprocOperator
XComs, Variables, Connections on Composer
Monitoring with Cloud Monitoring and Alerting
🔬 Cloud Composer DAG Lab
M08
Data Catalog, Dataplex & Governance
⏱️ 5 Hours● Advanced
▾
Data Catalog — tagging, search, policy tags
BigLake — unified storage access control
Dataplex — data mesh zones and lakes
Column masking and row-level access
VPC Service Controls — perimeter security
Audit logging for compliance
Data lineage with Dataplex
📝 Quiz: Data Governance & Security
M09
Projects & GCP Professional Data Engineer Prep
⏱️ 9 Hours● Advanced
▾
Project 1 — Batch ETL: GCS → Dataflow → BigQuery → Looker
Project 2 — Streaming: IoT Pub/Sub → Dataflow → BigQuery
Project 3 — Big Data: Dataproc PySpark + BigQuery
Project 4 — Full Platform: Composer + Dataflow + BigQuery + Dataplex
GCP Professional Data Engineer exam prep
100+ practice questions with explanations
Resume and LinkedIn optimization for GCP roles
🏗️ 4 Real GCP Projects📝 PDE Mock Tests
Career Outcomes
GCP Data Engineering Professionals Earn Top Salaries
GCP Data Engineers with BigQuery and Dataflow expertise are in high demand. Companies like Google, Wipro, TCS, and Deloitte actively hire GCP-certified data engineers.
Entry Level
₹10–16 LPA
0–2 Years
Mid Level
₹16–30 LPA
2–5 Years
Senior Level
₹30–60+ LPA
5+ Years
Complete Curriculum
What You Will Learn
A practical, industry-aligned curriculum covering every GCP service a modern Data Engineer needs — from BigQuery pipelines to production data platform architectures.
📊
BigQuery — Cloud Data Warehouse
Google's serverless data warehouse — partitioned & clustered tables, BigQuery ML, Omni cross-cloud queries and row-level security.
- ▸BigQuery architecture — slots, reservations, datasets
- ▸Partitioned & clustered tables for cost optimization
- ▸BigQuery ML — train ML models with SQL
- ▸BigQuery Omni — query S3/Azure data
- ▸Authorized Views and row-level security
10 Topics
→
🌊
Cloud Dataflow — Serverless ETL
Apache Beam-based serverless data processing — batch and streaming pipelines, Flex Templates and auto-scaling with windowing strategies.
- ▸Apache Beam model — PCollections, PTransforms
- ▸Batch and streaming Dataflow pipelines
- ▸Dataflow Flex Templates — reusable pipelines
- ▸Dataflow → BigQuery streaming inserts
- ▸Auto-scaling and windowing strategies
9 Topics
→
⚡
Cloud Dataproc — Managed Spark
Managed Apache Spark and Hadoop on GCP — PySpark jobs, Dataproc Serverless, Hive Metastore and BigQuery connector for Spark workloads.
- ▸Dataproc cluster setup — master, worker nodes
- ▸PySpark jobs on Dataproc
- ▸Dataproc Serverless — no cluster management
- ▸Dataproc Metastore — Hive-compatible catalog
- ▸BigQuery connector for Dataproc
8 Topics
→
📨
Cloud Pub/Sub & Streaming
Fully managed messaging service — topics, subscriptions, push/pull delivery, dead-letter topics and real-time CDC to BigQuery.
- ▸Pub/Sub topics, subscriptions, push vs pull
- ▸Pub/Sub to Dataflow streaming pipelines
- ▸Pub/Sub → BigQuery direct subscription
- ▸Dead-letter topics and retry policies
- ▸Eventarc — event-driven pipelines
7 Topics
→
🔄
Cloud Composer — Orchestration
Managed Apache Airflow on GCP — DAG deployment, GCP operators for BigQuery/GCS/Dataflow/Dataproc and Cloud Monitoring integration.
- ▸Cloud Composer = managed Apache Airflow on GCP
- ▸DAG deployment to Cloud Composer
- ▸GCP operators — BigQuery, GCS, Dataflow, Dataproc
- ▸Composer 2 — auto-scaling, workload identity
- ▸Monitoring with Cloud Monitoring
6 Topics
→
🔍
Data Catalog & Governance
Enterprise data governance on GCP — Data Catalog tagging, BigLake unified access, Dataplex data mesh and VPC Service Controls.
- ▸Data Catalog — search, tag, lineage
- ▸BigLake — unified access control
- ▸Column-level security in BigQuery
- ▸VPC Service Controls — data perimeter
- ▸Dataplex — data mesh on GCP
7 Topics
→
Student Success Stories
GCP Data Engineers Placed at Top Companies
★★★★★
"The BigQuery optimization and Dataflow streaming modules were excellent. Real GCP project access made learning very practical. Got placed at Wipro!"
SB
Suresh Babu
Analyst → GCP Data Engineer
✅ Wipro · ₹18 LPA
★★★★★
"Cleared GCP Professional Data Engineer exam on first attempt thanks to Module 9's prep material! Now working as a data architect at Deloitte."
AR
Ananya Reddy
BI Dev → Cloud Architect
✅ Deloitte · ₹26 LPA
★★★★★
"Cloud Composer and Dataproc Serverless modules were exactly what companies want. Trainer Venu's teaching style is clear and practical!"
VM
Vijay Mohan
Fresher → Data Engineer
✅ TCS · ₹10 LPA
FAQs
Frequently Asked Questions
Does this course cover the GCP Professional Data Engineer certification? ▾
Yes — Module 9 has dedicated exam prep with 100+ practice questions for the Google Cloud Professional Data Engineer exam. Many students have passed on their first attempt.
Do I need a GCP account for labs? ▾
Yes — you'll need a GCP account (Google offers $300 free credits for new accounts). We guide you through setup and ensure all labs stay within free tier limits.
Is BigQuery the focus of this course? ▾
BigQuery gets the most coverage (Module 2, 10 hours) as it's the core GCP analytics tool. But we also deeply cover Dataflow, Dataproc, Pub/Sub and Composer.
What is Apache Beam and do I need to learn it for Dataflow? ▾
Yes — Dataflow is Google's managed Apache Beam service. Module 4 teaches you the Beam programming model from scratch — no prior Beam knowledge needed.
What companies hire GCP data engineers in India? ▾
Google, Wipro, TCS, Deloitte, Infosys, HCL, Cognizant, Accenture and many startups. GCP skills are growing faster than any other cloud platform.