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Data Engineering Course in Chennai

Learn Data Engineering in Chennai with Global Certification and Placement Assurance.

Master Data Engineering with industry-relevant tools, practical projects and cloud technologies. Learn to build, automate and manage scalable data pipelines using Python, SQL, PySpark, ETL/ELT, Apache Airflow, Snowflake DBT, Power BI, AWS and Microsoft Azure.

  • 500+ Hiring Partners
  • Lifetime Placement Support
  • Bilingual Training in Tamil & English
  • Global Certification Preparation
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₹70+ LPA
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Why Choose Greens Technologies for Data Engineering Training in Chennai?

Greens Technologies provides IT training for students and working professionals seeking to develop practical, job-relevant technical skills.

Practical Learning

Industry-Relevant Curriculum

Experienced IT Trainers

Flexible Training Modes

Personalized Learning Support

Real-World Project Exposure

Placement Assurance

Flexible Batch Options

Meet Our Founder

A message from the leadership behind Greens Technologies.

Founder & Managing Director

Greens Technologies Leadership

Building Chennai's leading software training & placement institute

"Our mission is simple: make quality software education accessible, practical and 100% job-oriented."

What began as a single training center in Adyar has grown to 25 branches across Chennai, Trichy and Salem, offering training, consultancy and development services, with 1,00,000+ students placed through 500+ hiring partners.

10+ YearsIn IT Industry
1,00,000+Students Placed
25Branches

Meet Our Certified Senior Instructors

Learn under the direct mentorship of Greens Technologies leadership and battle-tested industry practitioners. Direct 1-on-1 practical lab guidance with senior practitioners from top product and service companies.

KR

Karthik Raghavan

Lead Data Engineering Faculty
14+ Years Experience
2,000+ Students Placed
Specialisation: AWS Data Platforms, PySpark & Big Data Pipelines
Certifications: AWS Certified Data Engineer - Associate, Databricks Certified Data Engineer Associate
Previously: Amazon, Cognizant

Karthik has built and led production data platforms for retail and banking clients. His classes run on live AWS labs from day one: students don't just watch pipelines, they build, break and fix them. Known for his "explain it like an interviewer" style, he personally reviews every capstone project so students walk into interviews with answers, not just code.

DV

Deva Venkataraman

Senior Faculty - SQL, Python & Analytics Engineering
11+ Years Experience
1,000+ Students Placed
Specialisation: SQL, Python for Data Engineering, Airflow Orchestration
Previously: Infosys, HCLTech

Deva handles the foundations that decide whether a student finishes job-ready: SQL, Python, Linux and Git. A former enterprise data lead, he drills students through hundreds of hands-on queries and scripts, and his weekend doubt-clearing sessions are a student favourite. Many of his learners started with no IT background at all.

DK

Deepa Krishnan

Senior Faculty - Cloud Data Warehousing
12+ Years Experience
2,800+ Students Mentored
Specialisation: Snowflake, dbt & Modern Data Stack
Certifications: Snowflake SnowPro Core
Previously: TCS, Zoho Corporation

Deepa spent a decade migrating enterprises from legacy databases to cloud warehouses. She teaches Snowflake and dbt the way data teams actually use them, with real business datasets and cost-aware design. Her students consistently credit her step-by-step approach for making a "scary" stack feel simple, even with zero coding background.

SR

Shankar Rajasekaran

Faculty - BI & Data Modelling
10+ Years Experience
1,500+ Students Mentored
Specialisation: Power BI, Dimensional Modelling, PL-300 Preparation
Certifications: Microsoft Power BI Data Analyst (PL-300)
Previously: Wipro, Freshworks

Shankar turns pipelines into stories businesses understand. She teaches Power BI, star schema design and dashboarding, and leads the capstone's reporting track. Her interview prep sessions, built on real questions her placed students faced, are one of the most-reviewed parts of the course.

Want to talk directly to our Trainer before enrolling?

Schedule a 15-minute 1-on-1 technical counselling session to discuss your background, syllabus questions, and custom career transition roadmap.

Real Students. Real Career Transformations.

Get Industry Recognized Certification

Greens Technologies' Data Engineering certification is recognised by 500+ hiring partners across product and service companies. It showcases your hands-on skills in SQL, Python, PySpark, AWS, Snowflake, DBT, Airflow and Power BI, backed by real-time project execution.

Benefits of Data Engineering Certification

  • To demonstrate your proficiency in designing and building production data pipelines,
  • To add weightage to your professional resume & LinkedIn profile,
  • To prepare for global AWS, Snowflake, Microsoft & Databricks certification exams,
  • To fast-track interview shortlisting across top data & MNC hiring partners.
Sample Greens Technologies certificate of completion

Global Certification Pathways - One Stack, Four Paths

Click a certification to see how the course prepares you. Provider exams are optional and their fees are separate.

Master AWS data services, data ingestion, transformation, storage, Lake Formation, Glue pipelines, and end-to-end cloud pipeline operations.

Master Snowflake cloud data architecture, data loading with Snowpipe, virtual warehouses, caching, security, and cloud platform fundamentals.

Validate skills in enterprise data preparation, star schema data modelling, advanced DAX expressions, automated refreshes, and reporting.

Further study in Spark, big data engineering, Delta Lake, and modern lakehouse architecture, building directly upon our PySpark module.

Course Curriculum 2026 - One Course. A Clear Sequence.

Master the Complete Data Stack: The training is designed around practical, real-time scenarios that match what data teams actually run in production. We build your skills from first query to production deployment.

The vocabulary and architecture every AWS data engineer uses, mapped to real AWS services.

Lab Whiteboard an AWS retail pipeline: sources, raw-silver-gold zones on S3, catalog, warehouse and dashboard.

The Data Engineer Role

  • Role of a data engineer
  • Data lifecycle
  • Volume, velocity and variety
  • Distributed computing basics
  • Data structures: graphs and trees

Medallion on AWS

  • Data catalog: Glue Data Catalog, SageMaker Catalog
  • Data transformation and the silver layer
  • Aggregation and the gold layer
  • Data warehouse and reporting
  • Medallion architecture

Streaming Overview

  • Apache Kafka
  • Kinesis Data Streams
  • Amazon Data Firehose
  • Amazon MSK
  • Managed Service for Apache Flink
  • Lambda and Kappa architectures

Data Systems

  • OLTP vs OLAP
  • Row vs columnar storage
  • Structured, semi-structured, unstructured data
  • Data warehouse vs data lake vs lakehouse
  • Schema-on-read vs schema-on-write

Operating Pipelines

  • Data quality
  • Monitoring
  • Orchestration
  • Data lineage
  • Data governance

Processing Patterns

  • ETL vs ELT
  • Batch processing
  • Full load and incremental processing
  • Change data capture (CDC)
  • Batch vs streaming
  • Stateful vs stateless processing

GenAI for Data Engineers

  • Vectors and embeddings
  • Amazon Bedrock
  • LLMs in pipelines

Sources & Ingestion

  • Source systems: databases, DynamoDB, OpenSearch
  • APIs and files
  • Data ingestion and the raw layer
  • Data APIs for other systems
  • Replayability of ingestion

Full Python for data engineering, from first variable to tested, AWS-connected scripts.

Lab Pull a paginated REST API, clean it with pandas, write Parquet with pyarrow and upload to S3 with Boto3, with logs and tests.

Python Basics

  • Variables and data types
  • List, tuple, set, dictionary
  • Operators
  • Input and output
  • Type conversion

Strings

  • f-strings
  • Slicing
  • String methods
  • Regular expressions

Comprehensions & Modules

  • List comprehension
  • Dictionary comprehension
  • Modules and packages
  • Imports

Functions

  • Functions and return values
  • Lambda functions
  • *args and **kwargs
  • Variable scope
  • Type hints

Errors, Files & Logging

  • Exception handling
  • File handling: JSON
  • File handling: CSV
  • Logging

Control Flow

  • Conditions: if, elif, else
  • for and while loops
  • break, continue, pass
  • match-case

Every SQL command category through warehouse-grade patterns, practised on a banking dataset.

Lab Build an SCD Type 2 customer dimension with MERGE, then write window-function reports for running balances and top-N per region.

SQL Basics

  • DDL, DML, DQL
  • DCL and TCL
  • Constraints
  • Normalisation

Subqueries & CTEs

  • Subqueries
  • Correlated subqueries
  • CTEs
  • Recursive CTEs

Data Modelling

  • SCD Type 1
  • SCD Type 2
  • SCD Type 3
  • Star and snowflake schemas
  • Facts and dimensions

Querying

  • SELECT and WHERE
  • DISTINCT and LIMIT
  • LIKE, IN, BETWEEN
  • Aliases
  • ORDER BY
  • CASE

Window Functions

  • ROW_NUMBER, RANK, DENSE_RANK
  • LEAD and LAG
  • FIRST_VALUE, LAST_VALUE
  • NTILE
  • Window frames: ROWS BETWEEN
  • Running totals

Functions

  • NULL handling: COALESCE, NULLIF
  • CAST
  • String functions
  • Date functions
  • PIVOT and UNPIVOT
  • JSON in SQL

Aggregation

  • GROUP BY and HAVING
  • Aggregate functions
  • Grouping, rolling averages, pivoting

Database Objects

  • Views
  • Materialized views
  • Stored procedures
  • Temporary tables

Performance & Transactions

  • Indexes
  • EXPLAIN plans
  • Query optimization
  • Transactions
  • Isolation levels
  • Locks and blocking

Joins & Sets

  • Inner, left, right, full joins
  • Cross and self joins
  • UNION
  • INTERSECT and EXCEPT

Loading Patterns

  • MERGE
  • Upsert
  • Deduplication
  • Incremental SQL

Distributed processing with the DataFrame API, ready for Glue, EMR and Databricks.

Lab Ingest nested JSON with an explicit schema, explode and clean it, mask card numbers and write partitioned Parquet to S3.

Spark Basics & Architecture

  • Driver and executors
  • SparkSession
  • Jobs, stages, tasks and the DAG
  • Cluster managers

Cleaning & Types

  • Null handling
  • Data type conversion
  • when / otherwise

Reading & Writing

  • CSV, JSON, Parquet
  • Explicit schemas (StructType)
  • Read/write options
  • Write modes
  • Spark SQL and temp views

Advanced Functions

  • Window functions
  • UDFs and pandas UDFs
  • map
  • String and date functions
  • pivot

Core Concepts

  • DataFrame and RDD
  • Transformations vs actions
  • Lazy evaluation

Beyond the Basics

  • Broadcast variables
  • Accumulators
  • Structured Streaming
  • Testing Spark code

DataFrame Operations

  • select, filter, withColumn
  • groupBy and agg
  • orderBy / sort
  • Joins and union
  • Deduplication

JSON Processing

  • explode
  • Array functions
  • Struct functions

Spark Platforms

  • Spark on Glue
  • Spark on Amazon EMR
  • Databricks: Delta Lake
  • Databricks notebooks and workflows

Production load patterns and the AWS services that move data in.

Lab Run a DMS full-load-plus-CDC task from Aurora to S3, with idempotent merges and a quarantine path for bad rows.

Patterns

  • ETL vs ELT: extract, transform, load
  • Batch ETL
  • Full load vs incremental load
  • CDC
  • Watermarks and high-water marks

Data Correctness

  • Data validation
  • Reconciliation
  • Audit columns
  • Schema evolution
  • Late-arriving data
  • SCD loads
  • Lineage

AWS Ingestion Services

  • AWS DMS and DMS Schema Conversion
  • Amazon AppFlow
  • AWS DataSync
  • AWS Transfer Family
  • Snow Family overview
  • Zero-ETL integrations

Reliability

  • Idempotency
  • Retry and reprocessing
  • Backfills
  • Error handling and quarantine
  • Dead-letter handling

Streaming & Ops

  • Streaming ETL
  • Logging
  • Alerting
  • IP allowlists for sources
  • Throttling and rate limits

Identity and access for data platforms: least privilege, roles and cross-account access.

Lab Build least-privilege roles for Glue, Lambda and Redshift, assume a cross-account role and fix an AccessDenied with Access Analyzer.

Identities

  • IAM users, groups and roles
  • Root user protection and MFA
  • IAM Identity Center (SSO)
  • Access keys best practice and rotation

Roles & Credentials

  • Service roles
  • Role assumption
  • Cross-account access
  • STS temporary credentials
  • Instance profiles

Data Service Access

  • S3 IAM access
  • Glue IAM access
  • Lambda IAM access
  • Redshift IAM access

Policies

  • Trust policy vs permissions policy
  • Policy JSON: Effect, Action, Resource, Condition
  • Managed vs inline policies
  • Resource-based policies
  • Least privilege
  • Custom policies

Troubleshooting

  • AccessDenied troubleshooting
  • IAM Access Analyzer
  • Policy Simulator

Advanced Access

  • ABAC with tags
  • Permission boundaries
  • SCPs and AWS Organizations
  • Managed vs unmanaged services

Data Security Services

  • KMS
  • Secrets Manager
  • Macie
  • VPC endpoints

The data lake foundation: storage, security, formats and cost on Amazon S3.

Lab Build a raw/silver/gold lake with SSE-KMS, lifecycle rules, an event notification to Lambda and an S3 Tables (Iceberg) table.

S3 Basics

  • Buckets, objects, prefixes
  • Multipart upload
  • Consistency model

Security

  • SSE-S3, SSE-KMS, SSE-C
  • Block Public Access
  • Object Ownership (ACLs disabled by default)
  • ACLs vs bucket policies
  • Bucket policy vs IAM policy

Formats & Performance

  • CSV, JSON, Parquet, ORC, Avro
  • Compression
  • Small file problem
  • S3 performance

Storage Classes & Lifecycle

  • Storage classes
  • Glacier tiers
  • Intelligent-Tiering
  • Lifecycle policies
  • Expiring data by age

Access & Events

  • Presigned URLs
  • Access Points
  • VPC endpoints
  • Event notifications to Lambda, SQS, SNS, EventBridge

Data Lake on S3

  • Raw, silver and gold layers
  • Apache Iceberg
  • Delta Lake
  • S3 Tables
  • S3 partitioning

Data Protection

  • Versioning
  • Cross- and same-Region replication
  • Object Lock
  • Deleting data for legal requirements

Cost & Visibility

  • S3 cost optimization
  • S3 Inventory
  • Storage Lens

Choosing and loading the right store for each access pattern, a core exam domain.

Lab Model an orders table in DynamoDB with TTL and Streams, and compare it with the same workload on Aurora.

Relational

  • Amazon RDS
  • Amazon Aurora
  • Read replicas and Multi-AZ
  • pgvector and HNSW on Aurora PostgreSQL
  • JDBC and ODBC connections

Amazon DynamoDB

  • Partition and sort keys
  • GSIs and LSIs
  • Capacity modes and throttling
  • TTL
  • DynamoDB Streams
  • Export to S3

Other Stores

  • DocumentDB
  • Neptune (graphs)
  • Keyspaces
  • MemoryDB for key/value
  • OpenSearch Service

Choosing a Store

  • Access patterns
  • Cost vs performance
  • Locks and concurrency
  • Provisioned vs serverless trade-offs

Serverless ETL on AWS: jobs, catalog, quality and cost control.

Lab Build an incremental Glue job with bookmarks, DQDL rules and an Iceberg MERGE into the silver layer.

Glue Overview

  • Glue overview and architecture
  • Glue Data Catalog
  • Databases and tables
  • Glue connections

DynamicFrames

  • DynamicFrame vs DataFrame
  • ApplyMapping
  • ResolveChoice
  • Relationalize

Scaling & Cost

  • Worker types
  • Auto scaling
  • Flex execution
  • Glue cost optimisation

Lake Formation Basics

  • Data lake governance
  • Lake Formation permissions

Incremental Processing

  • Job parameters
  • Job bookmarks
  • Incremental processing
  • Pushdown predicates

Quality & Monitoring

  • Glue monitoring
  • Error handling
  • Glue Data Quality (DQDL)
  • Glue DataBrew
  • Spark UI for Glue

Glue Jobs

  • Spark ETL jobs
  • Python shell jobs
  • Ray jobs (closed to new customers Apr 2026)
  • Streaming ETL

Orchestration

  • Glue triggers
  • Glue workflows

Open Table Formats

  • Glue with Apache Iceberg
  • Iceberg MERGE and compaction

Development

  • Glue Studio visual ETL
  • Interactive sessions
  • Notebooks

Managed big-data clusters for Spark workloads that outgrow Glue.

Lab Run the same PySpark job on EMR Serverless and on an EMR cluster with Spot, and compare cost and runtime.

EMR Options

  • EMR on EC2
  • EMR Serverless
  • EMR on EKS
  • Choosing EMR vs Glue vs Lambda

Running Jobs

  • Steps and spark-submit
  • EMR Studio notebooks
  • Hive and Presto/Trino overview
  • Glue Data Catalog as metastore

Clusters

  • Primary, core and task nodes
  • Instance fleets and Spot
  • Managed scaling
  • Bootstrap actions

Operations

  • Logging to S3
  • Troubleshooting failed steps
  • Cost optimisation
  • Containers: ECR, ECS and EKS basics

Event-driven serverless processing for light transformations and glue code.

Lab Deploy with SAM a Lambda that validates S3 uploads, routes failures to a DLQ and triggers a Glue job.

Lambda Basics

  • Functions, handlers, events
  • Runtimes
  • Environment variables
  • IAM role
  • Layers

Concurrency

  • Cold starts
  • Reserved concurrency
  • Provisioned concurrency

Reliability

  • Error handling and retry
  • Dead-letter queues and destinations
  • Idempotency

Triggers & Patterns

  • S3 to Lambda triggers
  • Lambda to S3 and Lambda to Glue
  • Kinesis, SQS, EventBridge, DynamoDB Streams triggers
  • Fan-in and fan-out

Packaging

  • Packaging dependencies
  • Container images
  • Lambda in a VPC
  • EFS mounts
  • AWS SAM

Limits

  • Timeout
  • Memory and ephemeral storage
  • 15-minute limit and other quotas

Invocation

  • Sync vs async invocation
  • Versions and aliases

Operations

  • Powertools logging
  • Lambda with Step Functions

Real-time ingestion and processing, the largest gap in most AWS syllabi.

Lab Stream clickstream events to Kinesis, aggregate in Flink windows and land them in S3 (Iceberg) via Firehose.

Kinesis Data Streams

  • Shards and partition keys
  • On-demand vs provisioned
  • Retention and replay
  • Enhanced fan-out
  • Throttling and hot shards
  • KCL and producers

Amazon Data Firehose

  • Delivery to S3, Redshift, OpenSearch
  • Buffering
  • Lambda transforms
  • Format conversion to Parquet
  • Dynamic partitioning
  • Delivery to Iceberg tables

Managed Service for Apache Flink

  • Flink concepts
  • Windows: tumbling, sliding, session
  • Stateful processing
  • Exactly-once semantics

Streaming Patterns

  • Calling Lambda from Kinesis
  • DynamoDB Streams
  • Redshift streaming ingestion
  • Spark Structured Streaming on Glue
  • Lambda vs Kappa in practice

Amazon MSK

  • Kafka topics and partitions
  • MSK Provisioned vs Serverless
  • MSK Connect
  • Consumer groups

Workflow orchestration with Airflow 3 on Amazon MWAA.

Lab Orchestrate S3 → Glue → Redshift on MWAA with asset-driven scheduling, retries, branching and alerting.

Architecture

  • Scheduler, workers, executor
  • API server and metadata DB
  • What changed in Airflow 3

Data Passing

  • XCom
  • Variables
  • Connections

Advanced

  • Jinja templating and macros
  • Dynamic task mapping
  • Deferrable operators
  • Monitoring
  • Testing DAGs

DAGs

  • DAGs, DAG runs and tasks
  • DAG structure and default_args
  • TaskFlow API and Task SDK
  • DAG versioning

Control Flow

  • Sensors
  • Branching
  • Task groups
  • Trigger rules
  • Pools and priority

Amazon MWAA

  • Managed Airflow on AWS
  • Deploying DAGs to S3/MWAA
  • Environment sizing

Operators

  • PythonOperator
  • BashOperator
  • AWS and Glue operators
  • Hooks

Reliability

  • Retries
  • Failure handling
  • Backfill and catchup
  • Deadline alerts (replace Airflow 2 SLAs)

Alternatives

  • AWS Step Functions
  • Amazon EventBridge

DataOps

  • CI/CD with Jenkins and GitHub Actions
  • Infrastructure as Code with Terraform
  • Docker basics
  • Kubernetes basics

Scheduling

  • Dependencies
  • Scheduling and cron
  • Data-aware scheduling: assets (formerly datasets)
  • Event-driven scheduling with Asset Watchers

The AWS cloud data warehouse, from architecture to tuning and security.

Lab Load a star schema with COPY, tune dist and sort keys, add RLS and masking, and share it to another namespace.

Architecture

  • Clusters
  • Leader and compute nodes
  • Slices
  • RA3 and managed storage

Table Design

  • Distribution styles
  • Distribution keys
  • Sort keys
  • Compression encoding

Integration

  • Redshift Spectrum
  • Federated queries
  • Zero-ETL
  • Streaming ingestion
  • Data API

Redshift Serverless

  • Workgroups
  • Namespaces
  • RPUs
  • Provisioned vs serverless

Maintenance

  • VACUUM
  • ANALYZE
  • Locks and concurrency

Sharing & Security

  • Data sharing
  • RBAC
  • Column-level security
  • Row-level security
  • Dynamic data masking

Query Optimization

  • Concurrency scaling
  • WLM queues
  • Short query acceleration
  • EXPLAIN
  • System tables

Objects

  • Databases, schemas, tables
  • Materialized views
  • SUPER type
  • Stored procedures

Operations

  • Snapshots
  • Resize

Loading & Unloading

  • COPY from S3 with an IAM role
  • Manifest files
  • Auto-copy
  • UNLOAD

Serverless SQL on the lake, and how to keep it fast and cheap.

Lab Query the lake with partition projection, convert JSON to Parquet with CTAS and MERGE into an Iceberg table.

Athena Overview

  • Serverless SQL on S3
  • Glue Data Catalog integration
  • External tables
  • Athena vs Redshift

Writing Data

  • CTAS
  • INSERT INTO
  • UNLOAD
  • Views

Iceberg in Athena

  • MERGE
  • Time travel
  • OPTIMIZE and VACUUM

Advanced

  • Federated queries
  • Lake Formation permissions
  • JSON SerDes
  • Athena Spark notebooks
  • Analysing logs with Athena

Performance

  • Partitioning
  • Partition projection
  • Parquet and columnar formats
  • Query optimization
  • Cost optimization

Administration

  • Workgroups
  • Query result location
  • Encryption
  • Query result reuse

Snowflake running on AWS, loaded straight from S3.

Lab Auto-ingest S3 files with Snowpipe, build streams and tasks for incremental MERGE and secure them with masking.

Architecture

  • Databases and schemas
  • Virtual warehouses
  • Editions and credits

Loading from S3

  • Stages and external stages
  • S3 storage integration with an IAM role
  • COPY INTO
  • File formats
  • Unloading to S3
  • Snowpipe through SQS

Incremental Loading

  • Streams and tasks
  • MERGE
  • Dynamic tables

Warehouses

  • Warehouse sizing
  • Multi-cluster warehouses
  • Auto-suspend
  • Resource monitors

Semi-Structured & Code

  • VARIANT
  • Snowpark
  • Iceberg tables

Storage

  • Micro-partitioning
  • Clustering
  • Time Travel
  • Fail-safe
  • Zero-copy cloning

Performance & Security

  • Query profile
  • Result cache
  • RBAC
  • Masking policies
  • Row access policies
  • Data sharing

The transformation layer of the modern data stack, on Redshift and Snowflake.

Lab Build staging and mart models on Redshift with incremental models, tests, snapshots and published docs.

dbt Overview

  • Transformation layer in ELT
  • dbt Core vs dbt Cloud
  • Project structure: dbt_project.yml
  • Profiles and connections

Materializations

  • View
  • Table
  • Incremental
  • Ephemeral

Reuse

  • Macros and Jinja
  • dbt packages
  • Seeds
  • Exposures

Models

  • SQL models
  • Python models
  • Staging and marts layers
  • Sources and ref
  • The dbt DAG

Quality & Docs

  • Generic tests
  • Singular tests
  • Documentation
  • Lineage graphs

History & Deployment

  • Snapshots for SCD Type 2
  • Jobs and environments
  • dbt on Snowflake
  • dbt on Redshift

Turning AWS and Snowflake data into governed, fast reports.

Lab Build a sales report on Redshift with a star model, DAX time intelligence, RLS and scheduled refresh.

Overview

  • Desktop, Service and Mobile
  • PL-300 certification alignment

Data Modelling

  • Star schema
  • Relationships
  • Cardinality

Security & Sharing

  • Row-level security (RLS)
  • Workspaces
  • Publishing and sharing
  • Deployment pipelines

Power Query

  • Connecting to sources
  • Data cleaning with M

DAX

  • Calculated columns
  • Measures
  • Time intelligence

Refresh

  • Scheduled refresh
  • On-premises data gateways

AWS Sources

  • Snowflake
  • Redshift
  • Athena
  • S3 data lakes

Reports

  • Visuals and report design
  • Dashboards
  • Apps

Performance

  • Query folding
  • Aggregations
  • Incremental refresh
  • Performance Analyzer

Preparing data for AI and using LLMs inside pipelines, new in the DEA-C01 v1.1 outline.

Lab Chunk and embed policy PDFs from S3 into a Bedrock knowledge base and call it from a Lambda-based pipeline step.

Foundations

  • LLMs, tokens, embeddings
  • Amazon Bedrock models
  • Prompting for data tasks
  • Responsible AI

Vectors

  • Vectorisation concepts
  • Vector index types: HNSW vs IVF
  • pgvector on Aurora
  • OpenSearch vector search
  • S3 Vectors overview

Bedrock Knowledge Bases

  • Chunking strategies
  • Sync from S3
  • RAG retrieval
  • Metadata filtering
Download AWS Brochure (PDF)

Build a Real Data Engineering Portfolio

Capstone projects that connect every tool in the stack to a real business scenario. The project mix can vary by batch to reflect the latest industry demands.

Capstone Project 01

1. Sales Data Lake on AWS

Platform: AWS S3, Glue, PySpark, Athena & Power BI

Ingest raw multi-region transactional sales files into S3 landing zones. Transform, cleanse, and deduplicate them using AWS Glue and PySpark into a partitioned data lake. Run serverless ad-hoc queries with Athena and deliver interactive executive dashboards in Power BI.

AWS S3 Glue PySpark Athena Power BI
Capstone Project 02

2. Customer 360 Warehouse

Platform: Snowflake & DBT

Consolidate omni-channel customer interactions, support tickets, and purchase transactions. Build an enterprise star schema in Snowflake cloud data warehouse, execute modular data modeling and testing with DBT, and automate data quality alerting.

Snowflake DBT Star Schema Data Quality
Capstone Project 03

3. Real-Time Clickstream Pipeline

Platform: AWS Kinesis, PySpark & Redshift

Stream live user clickstream and mobile interaction events at scale through AWS Kinesis. Process micro-batches and sliding time windows using PySpark Structured Streaming, landing real-time operational aggregates directly into Amazon Redshift.

Kinesis Streaming PySpark Redshift
Capstone Project 04

4. Marketing Spend Dashboard

Platform: Python, DBT & Power BI

Combine multi-channel ad campaign data (Google Ads, Meta Ads) via REST APIs using Python scripts. Harmonize and model attribution with DBT transformations, tracking customer acquisition cost (CAC) and marketing ROI on a live Power BI dashboard.

Python APIs DBT Power BI

Your portfolio tells the story: In technical interviews, show the pipeline design, architecture diagrams, clean code, data quality checks, and executive dashboards. Clearly explain the business question and your technical choices to stand out from other candidates.

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Upcoming Data Engineering Training Batches

New batch every Monday & Friday Classroom & Online Live Tamil & English Instruction
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Choose the learning structure that matches your current schedule, location, and career goals.

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Live instructor-led, India & abroad

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Hands-on labs at 25 branches

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Custom pace & scope

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Who Should Take This Data Engineering Course?

Designed for both technical and non-technical backgrounds with zero prior coding prerequisites.

Course Prerequisites: None Required

Learners can take this course regardless of previous technical or programming knowledge. We start from foundational SQL, Python and Linux basics, making it equally suitable for complete beginners, freshers, and experienced professionals.

Complimentary SQL and Python Foundation Course Included Beginner Friendly Live Mentor Guidance

Freshers & College Students

Non-IT Professionals

Software Developers & Testers

Database & Support Teams

Data & BI Analysts

Cloud & DevOps Professionals

Career Switchers

IT Managers & Tech Leads

Placement Assurance - Learn. Prepare. Get Placed.

Practical training, project preparation and career services in one course. Our dedicated placement cell bridges the gap between your training and your first data engineering role.

Placements Guaranteed Build your skills and prepare for the next step with Greens Technologies

Resume Preparation

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Our Students Work at Global Tech Leaders

Greens Technologies graduates are hired across top product companies and multinational IT consultancies.

Zoho
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TCS
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500+ Active Hiring Partners
100% Placement Assurance
₹10 - ₹70+ LPA Salary Packages
1,00,000+ Careers Launched

Data Engineering Top Interview Questions & Answers

Master 150+ real-world technical interview questions covering SQL, Python, PySpark, Snowflake, DBT, Airflow and AWS to crack your technical rounds on the very first attempt.

Where Data Engineering Creates Business Value

Modern enterprises run on data pipelines. Our training focuses on the exact use cases Chennai and pan-India employers hire for:

Analytics & Reporting Pipelines

Customer 360 Platforms

Real-Time Data Processing

Marketing & Revenue Intelligence

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Docker containerization, Kubernetes orchestration, Infrastructure as Code (Terraform), AWS, and continuous CI/CD pipelines.

Course FAQs

Everything you need to know about the Data Engineering programme, certification, placements and how we handle your data.

Data Engineering is one of the fastest-growing, highest-paying domains in technology, with demand across Chennai's IT corridor and pan-India product and service companies. This course prepares you to build production-grade data pipelines, gain hands-on expertise across the modern data stack, and qualify for Data Engineer, Analytics Engineer and BI Engineer roles with salaries from Rs 10L to 45+L per year.
Data Engineering builds the pipelines, warehouses and platforms that move and prepare data. Data Science analyses that data to build models and predictions. Companies hire for both, and data engineering roles are typically more numerous - every data science team depends on the pipelines data engineers build.
The complete modern data stack: SQL and Python foundations, AWS (S3, Glue, Lambda, Athena, Kinesis, RDS, Redshift), PySpark for big data processing, Snowflake for cloud warehousing, dbt for tested transformations, Airflow and Step Functions for orchestration, and Power BI for dashboards - connected in one end-to-end capstone project.
You will receive an industry-recognized Greens Technologies Data Engineering Course Completion Certificate, plus structured preparation guidance for global credentials: AWS Certified Data Engineer - Associate, Snowflake SnowPro Core, Microsoft Power BI Data Analyst (PL-300) and Databricks Certified Data Engineer Associate. Provider exams are optional and their fees are separate.
Our dedicated placement cell provides end-to-end placement assurance: resume preparation with trainer review, technical and behavioral mock interviews, Naukri and LinkedIn profile building, interview scheduling support and job opening updates, with continued guidance until you are placed.
Yes. We offer live instructor-led online sessions across India with flexible timings (weekday evening and dedicated weekend batches) and international time-zone support for working professionals, alongside classroom batches at our 25 branches.
No prior coding experience is required. The curriculum begins with data engineering foundations, Linux, SQL and Python taught from scratch before progressively advancing to cloud pipelines, big data and warehousing. Freshers from IT and non-IT backgrounds both succeed in this course.
Absolutely. We offer free interactive demo sessions where you can experience our trainer's practical teaching approach, review real-time lab demos, and discuss your learning and placement plan with a counsellor.

25 Greens Technologies Branches in Chennai, Bengaluru, Coimbatore, Salem and Trichy

Visit your nearest branch for walk-in counseling, lab tours, and live classroom batches. Also present in Salem, Trichy, Bengaluru and Coimbatore. Online live batches serve learners across all of India.

Adyar

11 First Street, Padmanabha Nagar
Adyar, Chennai
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OMR (Thoraipakkam)

19, Balamurugan Garden, Rajiv Gandhi Salai
Thoraipakkam, Chennai - 600097

Landmark: Opp. to Geetham Restaurant

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Tambaram Sanatorium

No. 1, Appa Rao Colony
Tambaram Sanatorium, Chennai - 600047

Landmark: Opp. Sri Rama Anjaneyar Temple, near HP Petrol Bunk

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Navalur

No. 12, Rajiv Gandhi Salai (OMR), Egattur Village
Navalur (just before Navalur Tollgate), Chennai - 600035
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Porur

149, 1C/1D, 1st Floor, Opp. to DLF IT Park
Ramapuram, Chennai - 600089
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Perumbakkam

1st Floor, Plot No. 19 & 20, American Advent Christian Layout
Sholinganallur to Medavakkam Main Road, Perumbakkam, Chennai - 600100

Landmark: Next to OMR Food Street

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Anna Nagar

Ground Floor, New No. W125, Old No. W42, 3rd Avenue, W Block
Anna Nagar, Chennai - 600040

Landmark: Near Parambriym Hotel, opp. HP Petrol Bunk

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West Tambaram

No. 05, Bakthavachalam Street
West Tambaram, Chennai - 600045
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Velachery

51-A, 2nd Floor, Velachery Road, Dhadeswaram Nagar
Velachery, Chennai - 600042
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Vadapalani

79, 100 Feet Road, Thiru Nagar Colony, NGO Colony
Vadapalani, Chennai - 600026
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Avadi

New No. 398, Old No. 577, CTH Road, Avadi Checkpost
Avadi, Chennai - 600054
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Mugalivakkam

No. 01, Adithi Colony, Mugalivakkam Main Road
Mugalivakkam, Porur, Chennai - 600116
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Mount Road

162, Second Floor, Anna Salai, Express Estate
Triplicane, Chennai - 600002
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Kolathur

No. 7, 2nd Floor, Perambur Paper Mills Road
Kolathur, Chennai - 600099

Landmark: Near Everwin Vidhyasram School, above Axis Bank

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T. Nagar

160, North Usman Road
T. Nagar, Chennai - 600017
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Ambattur

3rd Floor, No. 27/A, North Park Street, Secretariat Colony, Venkatapuram
Ambattur, Chennai - 600053
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Guduvancheri

162, Grand Southern Trunk Road, near Guduvancheri Railway Station
Nandivaram, Guduvancheri - 603202
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Trichy

75/E-3, Sri Krishna Enclave, 2nd Floor, Salai Road
Thillai Nagar, Tiruchirappalli - 620018
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Salem

2nd Floor, Sri Sai Kamatchi Complex, Vincent
Salem - 636007

Landmark: Opp. to Government Arts College

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Gowriwakkam

No. 280 K, Saranya Complex, Velachery Road, Shanthi Nagar
New Kunagkurichi, Gowriwakkam, Sembakkam, Chennai - 600073
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Tiruvottiyur

No. 1050, 2nd Floor, Tiruvottiyur High Road, Thangal
Near Kaladipet, Rajakadai Metro, Chennai - 600019

Landmark: Above KFC

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Poonamallee

Om Sakthi Plaza, First Floor, Old No. 1, New No. 126, Avadi Road
Karayanchavadi, Poonamallee, Chennai - 600056
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