GCP Data Engineering Course in Hyderabad
Build batch and streaming data pipelines on Google Cloud with BigQuery and Dataflow
The GCP Data Engineering course teaches you to design, build and operate data platforms on Google Cloud. You start with SQL, Python and data modelling, then work through Cloud Storage as a data lake, BigQuery for warehousing and analytics, Dataflow and Apache Beam for batch and streaming processing, Pub/Sub for event ingestion, Dataproc for Spark and Hadoop workloads, and Cloud Composer, which is managed Apache Airflow, for orchestration. You also learn Dataform for SQL transformations, Dataplex for governance, Bigtable and Spanner use cases, BigQuery ML basics, IAM and data security, CI/CD with Cloud Build, and monitoring and cost optimisation. The topics are relevant to the Google Cloud Professional Data Engineer certification, which is an advanced exam that Google recommends after practical experience. The course suits graduates and working professionals with basic SQL; Python is taught from the basics. You complete labs on a real Google Cloud project and a capstone that processes batch and streaming data into a BigQuery warehouse with a Looker Studio report. Classes run in Ameerpet, Hyderabad, and online.
Syllabus
Data Engineering & Google Cloud Foundations
- •Data engineer responsibilities and data platform architecture
- •Google Cloud projects, IAM, billing and the gcloud CLI
- •Batch versus streaming; ETL versus ELT
- •Lab: Create a project and budget for a data platform
- •Outcome: Prepare a secure, cost-aware workspace
SQL & Python for Data Pipelines
- •SQL joins, aggregations, CTEs and window functions
- •Python and pandas for data wrangling
- •File formats: CSV, JSON, Avro and Parquet
- •Lab: Clean and transform a dataset with SQL and Python
- •Outcome: Write the core logic used in pipelines
Cloud Storage as a Data Lake
- •Cloud Storage buckets, storage classes and access control
- •Lake zones: raw, curated and serving
- •Transfer Service, lifecycle policies and cost control
- •Lab: Build a lake structure and load sample data
- •Outcome: Organise storage for analytics
BigQuery Fundamentals
- •BigQuery architecture, datasets and tables
- •Loading and external tables; schema design including nested and repeated fields
- •Partitioning, clustering and query cost control
- •Lab: Load a large dataset and optimise a query
- •Outcome: Query and model data efficiently in BigQuery
BigQuery Advanced: Optimisation, Security & ML
- •Views, materialised views, routines and scheduled queries
- •Row-level and column-level security; authorised views
- •Slots, reservations and performance tuning
- •BigQuery ML for simple models
- •Lab: Build a secure reporting layer and a basic forecast model
- •Outcome: Operate BigQuery in a governed, cost-aware way
Streaming Ingestion with Pub/Sub
- •Pub/Sub topics, subscriptions and delivery semantics
- •Message ordering, dead-letter topics and schemas
- •Pub/Sub to BigQuery subscriptions
- •Lab: Publish events and stream them into BigQuery
- •Outcome: Ingest events reliably
Dataflow & Apache Beam
- •Apache Beam model: pipelines, PCollections and transforms
- •Windowing, triggers and late data in streaming pipelines
- •Dataflow templates, autoscaling and monitoring
- •Lab: Build a streaming Beam pipeline with windowed aggregates
- •Outcome: Process batch and streaming data at scale
Dataproc & Spark on Google Cloud
- •Dataproc clusters, ephemeral clusters and Dataproc Serverless
- •PySpark transformations and BigQuery connectors
- •When to choose Dataproc versus Dataflow versus BigQuery
- •Lab: Run a PySpark job on Dataproc against lake data
- •Outcome: Use Spark where it fits best
Orchestration with Cloud Composer
- •Apache Airflow concepts: DAGs, tasks, operators and sensors
- •Cloud Composer environments and best practices
- •Scheduling, retries, alerts and backfills
- •Lab: Orchestrate a daily load and transform pipeline
- •Outcome: Run dependable, scheduled pipelines
SQL Transformation & Modelling with Dataform
- •Dataform projects, SQLX and dependencies
- •Assertions, incremental tables and documentation
- •Dimensional modelling: facts, dimensions and slowly changing data
- •Lab: Build a tested transformation layer
- •Outcome: Produce tested, documented warehouse models
NoSQL & Operational Stores
- •Bigtable design: row keys and time series patterns
- •Spanner and AlloyDB use cases; Firestore overview
- •Choosing between analytical and operational stores
- •Lab: Design a Bigtable schema for time-series readings
- •Outcome: Pick the right store for each access pattern
Governance, Security & Quality
- •Dataplex, lineage and data quality scans
- •Sensitive Data Protection (Cloud DLP), CMEK and VPC Service Controls overview
- •IAM design for data teams
- •Lab: Run a data quality scan and mask sensitive columns
- •Outcome: Build governed, auditable pipelines
CI/CD, Monitoring & Capstone
- •Cloud Build, Git workflows and Terraform for data infrastructure
- •Cloud Monitoring and logging for pipelines; cost management
- •Capstone: batch and streaming pipeline to a BigQuery warehouse with a Looker Studio report
- •Interview practice for data engineering roles
- •Outcome: A portfolio-ready data platform project
Tools Covered
Projects Included
- Retail Analytics Warehouse: Load sales data to BigQuery with partitioning, clustering and a star schema; deliverable is a documented warehouse and cost report.
- Streaming Clickstream Pipeline: Publish events to Pub/Sub, process in Dataflow and land aggregates in BigQuery; deliverable is a working pipeline and dashboard.
- Orchestrated Daily ELT: Use Cloud Composer to load, transform with Dataform and test; deliverable is a DAG repository and run logs.
- Spark Batch Job on Dataproc: Process lake data with PySpark and write to BigQuery; deliverable is a job with a performance comparison against BigQuery SQL.
- Capstone Data Platform: Combine batch, streaming, governance and CI/CD with a Looker Studio report; deliverable is a GitHub repository and architecture diagram.
Start Your Journey Today
Interested in GCP Data Engineering? Fill in your details and we'll get back to you.
Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the GCP Data Engineering course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on GCP Data Engineering.
Certificate of Completion
GCP Data Engineering
This certifies that
[Your Name]
has successfully completed the
GCP Data Engineering
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured GCP Data Engineering course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
What does the GCP Data Engineering course syllabus cover?
Is GCP Data Engineering suitable for beginners?
Does the course prepare me for the Google Cloud Professional Data Engineer exam?
What is the difference between Dataflow and Dataproc?
Do I need to know Python?
Will I work with real-time data?
How is this different from the general Data Engineering course?
Can I use free credits?
Which jobs use GCP data skills?
How long is the GCP Data Engineering course at GloryTecks, and how many hours a week?
Is the GCP Data Engineering course available as live online training?
What is the fee for the GCP Data Engineering course in Hyderabad?
Will I get a certificate after the GCP Data Engineering course?
What placement support do GCP Data Engineering students get?
Best GCP Data Engineering Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include GCP Data Engineer, Data Engineer, BigQuery Developer, Analytics Engineer; support is guidance with the job search, not a guarantee of a job.