🚀 Master Modern Data Engineering

Data Engineering Training

Learn Apache Spark, Kafka, AWS, and modern data stack. Build real-time pipelines, data warehouses, and ETL systems handling petabytes of data daily.

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Data Engineering Training

✓ Master Apache Spark for processing 100GB+ datasets daily
✓ Build real-time streaming pipelines with Kafka handling 10,000+ events/second
✓ Design scalable data warehouses using Snowflake & BigQuery
✓ Deploy to AWS with S3, Glue, Athena, and Lambda services
✓ 3-4 months training with 300+ hands-on coding hours
✓ 100% placement with 15+ LPA average salary at top tech companies

300+

Training Hours

15+ LPA

Average Salary

100%

Placement Rate

6+

Real Projects

Tech Stack: Python • Apache Spark • Kafka • AWS • SQL • Docker • dbt • Airflow • Snowflake • Great Expectations

Comprehensive Curriculum

Phase 1: Data Fundamentals & SQL

Duration: 2 weeks

🏗️

Data Pipeline Architecture

ETL/ELT design patterns

📊

Advanced SQL

CTEs, Window functions, Joins

🗄️

Database Design

Schema design & optimization

🔍

Data Modeling

Star & Snowflake schemas

✓

ACID Transactions

Consistency & data integrity

📈

Performance Tuning

Query optimization

Phase 2: Python & Big Data Processing

Duration: 3 weeks

🐍

Python for Data

Pandas, NumPy, Polars

📁

File Handling

CSV, Parquet, Avro, JSON

⚡

Apache Spark Core

RDDs & DataFrames

🔌

Spark SQL

SQL queries on big data

🚀

Performance Optimization

Caching & partitioning

🎯

Spark Streaming

Real-time processing

Phase 3: Real-Time Streaming & Cloud

Duration: 2.5 weeks

📨

Apache Kafka

Producers, consumers, topics

⚙️

Kafka Streams

Real-time stream processing

☁️

AWS Services

S3, Glue, Athena, EMR

🏭

Serverless Data

Lambda & Athena

📊

Data Warehouse

Snowflake & BigQuery

📈

Monitoring & Alerting

Production systems

Phase 4: Modern Data Stack & DevOps

Duration: 2 weeks

🔄

dbt Transformations

Modern SQL workflows

🌊

Airflow Orchestration

Workflow automation

✓

Data Quality

Testing & validation

🔐

Data Governance

Compliance & lineage

📦

Docker & Containers

Containerize pipelines

🚀

CI/CD Pipelines

Automated deployments

Data Engineering Technologies Stack

⚡ Big Data Processing

• Apache Spark: Distributed processing, RDDs, DataFrames, SQL, MLlib
• Hadoop: HDFS, MapReduce, ecosystem tools (Hive, Pig)
• Kafka: Real-time streaming, producers, consumers, partitioning
• Flink: Stream processing, stateful computations, windowing
• Airflow: Workflow orchestration, DAG management, scheduling

🗄️ Data Storage & Warehousing

• Snowflake: Cloud data warehouse, scalability, multi-tenant
• BigQuery: Google's serverless data warehouse, SQL queries
• Redshift: AWS data warehouse, columnar storage, compression
• dbt: Data transformation, version control, testing, documentation
• Delta Lake: ACID transactions, schema enforcement, time travel

☁️ Cloud & Infrastructure

• AWS Services: S3, Glue, EMR, Lambda, RDS, DynamoDB
• GCP Services: BigQuery, Dataflow, Pub/Sub, Cloud Storage
• Docker & Kubernetes: Containerization, orchestration, scaling
• Terraform: Infrastructure as Code, cloud resource provisioning
• CI/CD Pipelines: GitHub Actions, Jenkins, GitLab CI

📊 Data Quality & Governance

• Great Expectations: Data validation, quality assurance framework
• Data Lineage: Apache Atlas, Collibra for data governance
• Monitoring: Prometheus, Grafana, DataDog for pipeline health
• Metadata Management: Hudi, Iceberg for table formats
• Data Security: Encryption, access control, data masking

Data Engineering Best Practices & Patterns

🏗️

Data Pipeline Architecture Patterns

Lambda Architecture

Batch + Real-time layers for both accuracy and speed

Medallion Architecture

Bronze-Silver-Gold layers for data quality progression

Kappa Architecture

Stream-first approach for real-time processing

Data Lake Architecture

Centralized data repository with schema-on-read

⚡

Optimization & Performance Tuning

✓ Partitioning: Data partitioning by date, region, category for faster queries
✓ Bucketing: Hash-based distribution for join optimization
✓ Indexing: B-tree, hash indexes for query acceleration
✓ Compression: Snappy, Gzip, Parquet for storage optimization
✓ Spark Optimization: Broadcast joins, shuffle tuning, memory management
✓ Query Optimization: Explain plans, cost-based optimization, statistics

🔄

Scalability & High Availability

Horizontal Scaling

Add more nodes to cluster for processing power

Auto-Scaling

Dynamic resource allocation based on load

Failover & Replication

Multi-node redundancy for fault tolerance

Load Balancing

Distribute traffic across multiple servers

Data Replication

Backup across multiple locations & zones

SLA Compliance

99.9% uptime, RTO/RPO objectives

Real-World Projects

📊

Real-Time E-Commerce Analytics

Kafka → Spark Streaming → S3 → Athena/Snowflake with dashboards

💳

Financial Data Warehouse

Multi-source ETL with medallion architecture & dbt transformations

📡

IoT Sensor Processing

Handle millions of sensor readings with real-time anomaly detection

☁️

Data Lake Migration

Migrate legacy systems to modern cloud-native data platforms

🎬

Recommendation Engine

Real-time personalization using Kafka & Spark MLlib

🔗

Multi-Source Integration

Sync data from APIs, databases, CSV files with validation

Why Choose Our Program

🎯

6 Real-World Projects

E-commerce analytics, financial data warehouse, IoT processing

💰

15+ LPA Salaries

Average placement with tier-1 companies & startups

📝

20+ Mock Interviews

Practice with senior data engineers

👨‍🏫

Expert Mentors

Learn from professionals at FAANG companies

📊

Job Guarantee

100% placement support & assistance

📚

Up-to-Date Stack

Latest tools & technologies (2024-2025)

💻

Hands-On Labs

300+ hours of practical coding

🚀

Career Growth

Clear path to Staff Engineer roles

Success Stories

A

Arjun Kumar

Amazon

Senior Data Engineer

B.Tech CSE - 2023

22 LPA

P

Priya Sharma

Goldman Sachs

Data Engineer

B.E ECE - 2023

18 LPA

R

Rajesh Patel

Flipkart

Analytics Engineer

B.Tech IT - 2024

16 LPA

N

Neha Verma

Stripe

Data Engineer

M.Tech - 2023

20 LPA

Frequently Asked Questions

What is the course duration? ▼

3-4 months (300+ hours). Regular batches run Mon-Fri (90-120 days), weekend batches run Sat-Sun (4-5 months).

Do I need prior experience? ▼

Basic programming & SQL knowledge helps. We cover Python from basics, so beginners can also join.

What placement support is provided? ▼

100% placement support with resume building, mock interviews, and direct referrals to 50+ companies. Average salary is 15+ LPA.

Will I get certifications? ▼

Yes! Course completion certificate + preparation for AWS Certified Data Engineer. Top performers get exam vouchers.

Are class recordings available? ▼

Absolutely! All class recordings provided daily for self-paced review.

Can I work on real projects? ▼

Yes! 6+ real-world projects from E-commerce, finance, IoT, and cloud domains.

Ready to Master Data Engineering?

Join top tech companies with 15+ LPA salary. Start your journey today!

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Visit our training centers across Chennai, Salem, and Trichy. Expert trainers ready to guide your IT journey.

Chennai

Greens Technologies OMR

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NO.1050, 2ND FLOOR, TIRUVOTTIYUR HIGH ROAD THANGAL, above KFC, near KALADIPET, Rajakadai, METRO, Chennai, Tamil Nadu 600019

Contact Us

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contact@greenstechnologies.com

Data Engineering Training