What is event-time processing?

Event-time processing uses the timestamp associated with an event to determine when the event actually occurred, which is important when events arrive late or out of order.

What is Change Data Capture?

Change Data Capture is a technique for identifying database changes and delivering those changes to downstream systems, often through streaming platforms.

What is the difference between batch and real-time processing?

Batch processing handles accumulated data at scheduled intervals, while real-time processing continuously handles events as they arrive.

Is Real-Time Data Engineering suitable for beginners?

Learners with basic programming and SQL knowledge can start learning Real-Time Data Engineering through structured training and hands-on practice.

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Real-Time Data Engineering

Data Engineering

Real-Time Data Engineering

Q: What is Real-Time Data Engineering?

Real-Time Data Engineering involves building systems that continuously ingest, process, transform, and deliver data as events are generated.

Q: What does a Real-Time Data Engineer do?

A Real-Time Data Engineer designs and maintains streaming pipelines that process events from applications, databases, APIs, IoT systems, and other sources.

Q: What is a streaming data pipeline?

A streaming data pipeline continuously moves and processes data as it arrives instead of waiting for data to accumulate for batch processing.

Q: What is Apache Kafka used for?

Apache Kafka is used to publish, store, distribute, and consume high-volume streams of events across distributed systems.

Q: What is Spark Structured Streaming?

Spark Structured Streaming is a Spark processing engine used to build scalable applications for continuously processing streaming data.

Q: What is Apache Flink?

Apache Flink is a distributed processing framework designed for stateful computation over bounded and unbounded data streams.

Build practical skills with a Real-Time Data Engineering Course covering Apache Kafka, Spark Structured Streaming, PySpark, Flink, and modern streaming pipelines. This Real-Time Data Engineering Training focuses on event-driven architecture, real-time processing, streaming analytics, and hands-on projects, with an online learning path for professionals.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

About

Training Plan

Course Curriculum

New Batch

Projects

Certificate

Testimonials

FAQ

Interview FAQ

Placement Client

About Real-Time Data Engineering

The Real-Time Data Engineering Course at TechPratham is designed to help learners build and manage scalable streaming data pipelines. The program combines practical Real-Time Data Engineering Training with Apache Kafka, Spark Structured Streaming, PySpark, Apache Flink, SQL, event-time processing, windowing, watermarking, CDC, and streaming data quality.

The Real-Time Data Engineering Course Online covers Kafka topics, partitions, producers, consumers, consumer groups, offsets, Kafka Connect, Schema Registry, and reliable event processing. Learners also explore Real-Time Data Engineering with Kafka and Spark, along with cloud streaming, monitoring, orchestration, and production-focused pipeline design.

This Real-Time Data Engineering Training Online includes hands-on projects and an end-to-end capstone. The program provides a structured foundation for Real-Time Data Engineering Certification and a Real-Time Data Engineering Certification Course, while supporting learners looking for the Best Real-Time Data Engineering Course and practical Real-Time Data Engineering Online Training.

Training Plan

About trainer

Working professional who is carrying more than 10 years of industry experience.

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

e-Book

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

Video Recording

Daily Session would be recorded and shared to the candidate.

Projects

Live projects will be provided for hands-on practice.

Resume Building

Expert-guided resume building with industry-focused content support.

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Real-Time Data Engineering Curriculum

Module 1: Real-Time Data Engineering Fundamentals

Understand the foundations of real-time data systems, streaming architectures, event-driven applications, and continuous data processing.

Introduction to Real-Time Data Engineering

Real-time vs batch processing

Streaming data characteristics

Event-driven architecture

Streaming pipeline architecture

Latency and throughput

Event sources

Real-time use cases

Streaming system components

Scalable streaming architectures

Module 2: Streaming Data Ingestion and Event Sources

Learn how real-time systems collect events from applications, databases, APIs, logs, and other continuous data sources.

Real-time data ingestion

Application events

API-based ingestion

Database events

Log and sensor data

IoT event sources

JSON event structures

Avro and serialization

Event validation

Ingestion architecture

Source-to-stream workflows

Module 3: Apache Kafka Fundamentals

Build strong Kafka fundamentals for creating scalable, fault-tolerant, and distributed event streaming systems.

Apache Kafka architecture

Kafka brokers

Topics

Partitions

Producers

Consumers

Consumer groups

Offsets

Message ordering

Retention

Replication

Kafka KRaft fundamentals

Kafka use cases

Module 4: Kafka Connect, Schema Registry and Reliability

Learn the tools and engineering practices required to build reliable Kafka-based streaming pipelines.

Kafka Connect fundamentals

Source connectors

Sink connectors

JDBC connectors

Schema Registry

Avro schemas

Schema evolution

Compatibility rules

Idempotent processing

Delivery semantics

Dead-letter queues

Consumer lag

Kafka security fundamentals

Module 5: Spark Structured Streaming

Use Apache Spark and PySpark to process continuous data streams and build scalable real-time processing applications.

Apache Spark architecture

Structured Streaming

Streaming DataFrames

SparkSession

Kafka and Spark integration

PySpark streaming

Transformations

Streaming aggregations

Output modes

Triggers

Checkpointing

Fault recovery

Streaming queries

Module 6: Event-Time Stream Processing

Learn how streaming systems handle event timing, windows, late data, state, and out-of-order events.

Event time

Processing time

Ingestion time

Tumbling windows

Sliding windows

Session windows

Watermarking

Late-arriving data

Out-of-order events

Stateful processing

Streaming joins

Deduplication

State management

Module 7: Apache Flink and Streaming SQL

Explore Apache Flink for stateful stream processing and understand how it compares with Spark Structured Streaming.

Apache Flink introduction

Flink architecture

DataStream API

Event-time processing

Flink windows

Stateful processing

Checkpoints

Fault tolerance

Flink SQL

Streaming transformations

Flink monitoring

Spark vs Flink

Choosing a stream processor

Module 8: CDC and Real-Time Data Synchronization

Learn how Change Data Capture enables database changes to flow continuously into streaming systems.

Change Data Capture fundamentals

Database change events

Log-based CDC

Debezium introduction

Database to Kafka pipelines

CDC event structures

Kafka-based synchronization

CDC transformations

Schema evolution

CDC reliability

Real-time replication

CDC use cases

Module 9: Real-Time Data Quality, Storage and Lakehouse

Build reliable streaming data systems with validation, deduplication, data quality controls, and modern lakehouse storage.

Streaming data quality

Data validation

Duplicate detection

Malformed events

Data quarantine

Dead-letter queues

Delta Lake

Parquet

Lakehouse concepts

Bronze, Silver and Gold layers

Analytics-ready datasets

Real-time storage patterns

Module 10: Orchestration, Monitoring and Observability

Learn how to manage, monitor, troubleshoot, and orchestrate real-time data workflows in production environments.

Apache Airflow fundamentals

Streaming workflow orchestration

Event-driven scheduling

Pipeline dependencies

Kafka monitoring

Consumer lag

Spark monitoring

Logs and metrics

Pipeline health

Alerts

Prometheus concepts

Grafana concepts

Troubleshooting

Recovery and backfills

Module 11: Cloud and Production Real-Time Architecture

Understand how real-time streaming systems are deployed, secured, scaled, and monitored in modern cloud environments.

Cloud streaming architecture

Managed Kafka concepts

AWS MSK

Amazon Kinesis concepts

Azure Event Hubs

Google Cloud Dataflow

Cloud storage integration

Docker

Docker Compose

Scalability

Performance optimization

Streaming security

Disaster recovery

Cost considerations

Module 12: End-to-End Real-Time Data Engineering Capstone

Apply the complete streaming technology stack to design and implement an end-to-end real-time data engineering solution.

Project requirement analysis

Streaming architecture design

Event ingestion

Kafka implementation

Stream processing

Event-time analytics

Windowing

Data quality

CDC integration

Real-time storage

Monitoring

Dashboard integration

Performance optimization

Production readiness

Final project presentation

Data Engineering Courses

No related courses found

Additional Program Highlights

Comprehensive study materials and resources

Professional resume building session

Master your interview skills

Real-world project demonstrations

Upcoming Batches

✓ Selected

Weekdays

📅 02 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekdays

📅 05 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekdays

📅 07 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekdays

📅 08 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Can't find a batch you were looking for?

Who Should Take Real-Time Data Engineering Course?

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Ops/Administrators/HR

Developers

BA/QA Engineers

Cloud / Infra

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Career Opportunities After Learning Real-Time Data Engineering

Real-Time Data Engineer

Streaming Data Engineer

Data Streaming Engineer

Kafka Engineer

Big Data Streaming Engineer

Data Pipeline Engineer

Stream Processing Engineer

Real-Time Analytics Engineer

Real-Time Data Engineer

Streaming Data Engineer

Data Streaming Engineer

Key Projects

Real-Time Data Engineering

WNS – Customer Event Pipeline

Scenario: Design a real-time event pipeline that processes customer interactions and identifies activity patterns as events arrive.

Live Work:

Create Kafka event streams
Process events with PySpark
Apply windowed stream analytics

Outcome: Develop streaming analytics skills

Deloitte – Enterprise Event Monitoring

Scenario: Create a simulated enterprise event pipeline to monitor operational events and generate analytics-ready streaming data.

Live Work:

Ingest events through Kafka
Apply event-time processing
Monitor streaming pipeline health

Outcome: Build production-style streaming skills

Accenture – CDC Streaming Pipeline

Scenario: Build a simulated CDC workflow that captures database changes and streams them through Kafka for downstream processing.

Live Work:

Capture database change events
Stream CDC events through Kafka
Process changes using Spark

Outcome: Apply real-time CDC concepts

Walmart – Retail Event Streaming

Scenario: Build a simulated streaming pipeline that processes retail orders and customer events for real-time business analytics.

Live Work:

Stream events through Apache Kafka
Process data using Spark
Create real-time analytics datasets

Outcome: Build a real-time retail pipeline

Placement Process

Live Class with Industry Use Cases

Interactive learning
Real-world examples
Expert guidance
Doubt resolution

Daily Assignment & Weekly Assessments

Hands-on practice
Concept reinforcement
Weekly evaluation
Timely feedback

Project Testing & Deployment

Project development
Testing practice
Deployment exposure
Portfolio creation

Interview Preparation (Mock, PD, Alumni Session)

Mock interviews
Personal development
Alumni guidance
Confidence building

Resume Building

Resume crafting
Industry-focused format
Skills highlighting
Professional presentation

Placement

Job assistance
Interview support
Career guidance
Opportunity access

Latest Hiring

No hiring posts

Recently Placed Candidates

No placements available

Latest Hiring

No hiring posts

Our Success Mantra

Commitment

Ensuring quality training every day

Fulfillment

Meeting learning goals with confidence

Accomplishment

Students achieving industry-ready expertise

Our Learner Voice

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Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What Makes TechPratham Training Different?

Feature

TechPratham Advantage

Industry Standard Institutes

Real-World Implementation

Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.

Restricted or simulation-based access that lacks real-world complexity.

Subject Matter Experts

Mentorship from certified professionals with 15+ years of global industry experience.

Generic instructors with no hands-on implementation background.

2026 Ready Curriculum

AI-driven curriculum with advanced platform integrations and real-world configurations.

Basic, outdated syllabus that misses key technical integrations.

Daily & Weekly assignments

Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.

Self-paced learning with no accountability or progress tracking.

End-to-End Lifecycle

Hands-on project testing and deployment with portfolio-ready real-world exposure.

Basic lab exercises without any deployment or testing practice.

Interview Readiness

Structured interview preparation including mock interviews, PD sessions, and alumni guidance.

Minimal support limited to a generic certificate of completion.

Placement Support

Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.

Basic career tips with no direct industry connections.

Training Support

24/7 technical doubt resolution and personalized mentoring support.

Limited mentor availability and no support after class hours.

Affordable Fees

Competitive fixed pricing with flexible payment options and transparent fee structure.

Inflated fees with hidden costs and low return on investment.

Real-World Implementation

Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.

Restricted or simulation-based access that lacks real-world complexity.

Subject Matter Experts

Mentorship from certified professionals with 15+ years of global industry experience.

Generic instructors with no hands-on implementation background.

2026 Ready Curriculum

AI-driven curriculum with advanced platform integrations and real-world configurations.

Basic, outdated syllabus that misses key technical integrations.

Daily & Weekly assignments

Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.

Self-paced learning with no accountability or progress tracking.

End-to-End Lifecycle

Hands-on project testing and deployment with portfolio-ready real-world exposure.

Basic lab exercises without any deployment or testing practice.

Interview Readiness

Structured interview preparation including mock interviews, PD sessions, and alumni guidance.

Minimal support limited to a generic certificate of completion.

Placement Support

Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.

Basic career tips with no direct industry connections.

Training Support

24/7 technical doubt resolution and personalized mentoring support.

Limited mentor availability and no support after class hours.

Affordable Fees

Competitive fixed pricing with flexible payment options and transparent fee structure.

Inflated fees with hidden costs and low return on investment.

What is Real-Time Data Engineering?

What does a Real-Time Data Engineer do?

What is a streaming data pipeline?

What is Apache Kafka used for?

What is Spark Structured Streaming?

What is Apache Flink?

Industry-Recognized Certification

Real-Time Data Engineering Certificate

News Highlights

TechPratham Introduces Hire-Train-Deploy Model to Transform HR & ERP Talent in the AI Era

TechPratham Empowering Future Professionals Through AI-Focused HR & ERP Training

Featured In

TechPratham Gains Recognition for Bridging the HR & ERP Skills Gap with Hire-Train-Deploy

TechPratham's Hire-Train-Deploy Approach Reshaping HR & ERP Careers in the AI-Driven Industry

About Us

TechPratham is an ISO 9001:2015 certified IT Training and Development, delivering industry-focused certification courses with expert guidance and placement support.

CIN:U62013UP2025PTC223378GST:09AALCT8794N1Z2

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Noida Office:B-24, Sector-1, Noida, Uttar Pradesh - 201301

Hyderabad Office:LVS Arcade, 71, Hitech, 6th floor, Madhapur Road, Jubilee Enclave, HITEC City, Hyderabad - 500081

+91-8882178896

+1 (343) 477-0926

info@techpratham.com

All Courses

Start typing to search courses...

Real-Time Data Engineering

Enquire This Course

Placement Client

About Real-Time Data Engineering

Training Plan

About trainer

Decks & Updated Content

e-Book

Assignments & MCQs

Video Recording

Projects

Resume Building

Interview Preparation

Real-Time Data Engineering Curriculum

Module 1: Real-Time Data Engineering Fundamentals

Module 2: Streaming Data Ingestion and Event Sources

Module 3: Apache Kafka Fundamentals

Module 4: Kafka Connect, Schema Registry and Reliability

Module 5: Spark Structured Streaming

Module 6: Event-Time Stream Processing

Module 7: Apache Flink and Streaming SQL

Module 8: CDC and Real-Time Data Synchronization

Module 9: Real-Time Data Quality, Storage and Lakehouse

Module 10: Orchestration, Monitoring and Observability

Module 11: Cloud and Production Real-Time Architecture

Module 12: End-to-End Real-Time Data Engineering Capstone

Data Engineering Courses

Additional Program Highlights

Upcoming Batches

Who Should Take Real-Time Data Engineering Course?

Career Opportunities After Learning Real-Time Data Engineering

Key Projects

Real-Time Data Engineering

WNS – Customer Event Pipeline

Live Work:

Deloitte – Enterprise Event Monitoring

Live Work:

Accenture – CDC Streaming Pipeline

Live Work:

Walmart – Retail Event Streaming

Live Work:

Placement Process

Live Class with Industry Use Cases

Daily Assignment & Weekly Assessments

Project Testing & Deployment

Interview Preparation (Mock, PD, Alumni Session)

Resume Building

Placement

Latest Hiring

Recently Placed Candidates

Latest Hiring

Our Success Mantra

Commitment

Fulfillment

Accomplishment

Our Learner Voice

Beyond Courses:

What Makes TechPratham Training Different?

What is Real-Time Data Engineering?

What does a Real-Time Data Engineer do?

What is a streaming data pipeline?

What is Apache Kafka used for?

What is Spark Structured Streaming?

What is Apache Flink?

What is the difference between Kafka and Spark Structured Streaming?

What is a Kafka partition?

What is a Kafka consumer group?

What is a Kafka offset?

What is watermarking in stream processing?

What is the difference between event time and processing time?

Real-Time Data Engineering Certification

Certification Details

Certification Coverage

Vendor Certification Pathways

TechPratham Course Completion Certificate

﻿

Industry-Recognized Certification

News Highlights