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Big Data Architecture Masterclass - Complete Course - 2026

Name: Big Data Architecture Masterclass - Complete Course - 2026
Rating: 4.2 (14 reviews)

Big Data architecture: Design Scalable Lambda, Kappa, Data Mesh Systems Using Kafka, Spark, Airflow & Hadoop Ecosystem

Created byBig Data Landscape

Last updated 3/2026

English

English [Auto],

What you'll learn

Understand the core components of any robust big data architecture for efficient data processing and analytics.
Learn to design scalable and secure data pipelines using popular big data tools and technologies.
Gain expertise in integrating diverse data sources into a unified and well-structured big data ecosystem.
Understand Big Data Reference Architecture Components from Data Sources to Data Consumers
Become a successful Big Data Architect and Data Engineer
Learn how to design a performant, scalable distributed system that handles big data.
Practice Big Data software engineering fundamentals; Data Ingestion, Data Loading & preprocessing, ETL, Batch & Stream processing, Analytic Engine, Visualizion
Architect and create a big data or distributed system based on ultimate reference architecture
How Big data architectures can be set up in a generic and repetitive way
Work out a blueprint for a Big data architecture for any project in any industry landscapse
Familiarity with big data storage systems: Data Lake, Warehouse, NoSQL & Relational Databases, data mesh and how to best apply it
Master the Big Data conceptual model with views and activities performed by the functional components
Big Data architecture High-level and Low-level overview
Ability to design, implement, and maintain any big data architecture that meets the organization’s requirements
Understanding of Big Data Security and Privacy
Understand the role of System Workload Orchestrator and Data Life Cycle management for the Big Data Software Application pipeline
Master Data Architecture

Course content

7 sections • 54 lectures • 3h 31m total length

Introduction3:54
Master a universal big data reference architecture that works across industries and clouds. Design end-to-end data systems with framework-first thinking across ingestion, processing, storage, analytics, visualization, and orchestration.
The Problematic of Big Data Architecture Complexity1:38
Big Data History _ The 4 Vs of Big Data Overview5:49
Explore the four v's of big data—volume, velocity, variety, and variability—and how scalable architectures unlock actionable insights from unstructured and structured data.
Unleashing the Power of Big Data: Exploring its Characteristics1:23
Importance of well-designed Big Data Architecture3:37
Discover how a well-designed big data architecture delivers scalability, reliability, and performance through redundancy and distributed processing, while preventing data silos and security vulnerabilities and enabling efficient data integration.
Big Data Reference Architecture Overview4:00
Explore the big data reference architecture, from data sources and system workload orchestrator to it infrastructure. Study data ingestion, processing, analytics, visualization, data lifecycle management, and security for data consumers.
NOTE0:25

A Reference Architecture for All Big Data Projects - NOTE0:26
Big Data Sources - Data Provider5:28
Explore big data sources as diverse data feeds from sensors, logs, social, and mobile data, and learn to separate signal from noise for unified analytics.
Big Data Software Application - Processing Pipeline - High-Level Overview7:44
Explore the big data software application and its processing pipeline, from data ingestion and loading to preprocessing, analytics engines, and visualization, with a focus on data lifecycle management.
Unveiling the Stages of Big Data Software Application2:31
Quiz
Data Ingestion - Data Collection3:12
Mastering Data Ingestion: Extracting and Preparing Raw Data1:55
Real-World Ingestion Case Study — IoT Sensor Pipeline12:59
Maps a real IoT sensor pipeline to the big data reference architecture. Leverages Kafka ingestion, Flink real-time anomaly detection, and Spark batch for predictive maintenance.
Quiz - Data Ingestion
Big Data Loading & Preprocessing - Processing - ETL4:23
Learn how data loading, pre-processing, and processing transform raw and structured data into analyzable information using ETL and ELT, with validation, optimization, NLP techniques, and storage in big data pipelines.
Data Preparation and Processing - Recap Article2:11
Quiz - Data Processing
Analytics Engines6:08
Analytics Engine for Extracting Insights from Big Data - Article1:16
explores how the analytics engine empowers organizations to extract valuable insights from processed data. It covers data analysis levels (descriptive, predictive, prescriptive), gain knowledge to drive data-driven decisions and gain a competitive edge in the dynamic data landscape.
Feature Stores & Vector Databases — Bridging Big Data and ML13:13
Quiz - Analytics Engines
Data Visualisation4:01
Explain how data visualization communicates insights from big data, enabling static and interactive visualizations, dashboards, and reports, while serving data storage delivers pre-aggregated and real-time data for interaction.
Static and Interactive Visualizations - Article1:31
Quiz - Data visualization
Big Data System Workload Orchestrator1:58
Coordinate data flows across the big data pipeline, with the system workload orchestrator acting as the conductor to manage ingestion, storage, processing, analysis, and disposal.
Mastering the System Workload Orchestrator in Big Data Processing - Article0:46
Note - Unlocking Excellence1:03

Big Data Architecture Low Level Overview - Lamda - Kappa - Microservices7:00
Explore big data architectures, including lambda, kappa, and microservices, and how data sources, ingestion, storage, processing, and visualization form integrated layers for batch and real-time analytics.
Lambda vs. Kappa vs. Data Mesh Decision Framework7:22
Data Ingestion - Unstructured Data types - Apache Kafka - Flume - Amazon Kinesis7:07
Explore the data source layer of big data architecture, detailing structured, semi-structured, and unstructured data, and real-time ingestion with kafka, flume, and kinesis from social media, mobile, and IoT.
Storage - Data Lake, Data Warehouse, NoSQL & SQL Databases - Hadoop, S3 , Hive +3:18
Explore big data storage: data lakes, data warehouses, and SQL and NoSQL databases, comparing formats, scalability, and use cases with Hadoop, S3, and Hive in modern architectures.
The Data Lakehouse — Delta Lake, Iceberg & Databricks6:40
Discover how the data lakehouse unifies lake storage with warehouse reliability via a transaction log layer on Delta Lake or Iceberg, enabling ACID transactions, time travel, and schema evolution.
Big Data Distributed vs Traditional Processing - Batch vs Stream Processing2:39
Understand the significance of security and privacy in Big Data projects.
Identify the various security and privacy threats posed to Big Data assets.
Explore the techniques and strategies to address security and privacy challenges in Big Data architecture.
Gain insights into key security and privacy principles applicable to Big Data projects.
Recognize the importance of data security auditing in maintaining a secure Big Data system.
Comprehend the significance of data privacy policies in protecting sensitive information.
Learn about the specific security challenges related to heterogeneous components, streamed data, and multiple data sources in Big Data projects.
Understand the security implications of Internet of Things (IoT) devices in Big Data implementations.
Recognize the complexities introduced by new types of data, such as geospatial and video imaging data, in terms of security and privacy.
Explore the challenges posed by veracity, context, provenance, and jurisdiction of data in maintaining its security and privacy in Big Data projects.
Understand the impact of data volatility on security and privacy in Big Data architecture.
Acquire practical tips for implementing a security-first approach and involving security and privacy experts in Big Data projects.
Learn about the importance of a layered security approach and regularly updating security and privacy policies.
Educate employees about security and privacy risks associated with Big Data projects and how to protect data effectively.
Big Data Processing Technonologies - MapReduce - Apache Spark, Flink and Storm2:43
Fintech Fraud Detection Pipeline Case Study6:21
Explore a real-world fintech fraud detection pipeline built on a Lambda architecture, with real-time Flink, Kafka, Redis feature store, and batch Spark processing for model retraining and regulatory compliance.
Big Data Query - Data Science - Data Preparation - Apache Hive - PIG - SparkSQL2:50
Explore the big data query layer that unifies querying across distributed storage with tools like Hive, Pig, Spark SQL, and Presto, enabling data preparation and secure access.
System Orchestrator - Worflow Management & Scheduling - Apache Airflow, Oozie +1:26
Learn orchestrators manage big data workflow management and scheduling to automate, optimize, and ensure repeatable data processing with Airflow, Oozie, Azkaban, and Luigi in right order at the right time.
Data Vizualization - Static & Dynamic Real Time Dashboard - Kibana - Superset +1:52

Mapping the Reference Architecture to AWS4:00
Map the big data reference architecture to AWS managed services, aligning ingestion on Kinesis, batch on EMR, stream processing with Flink, data lake on S3, and data warehouse on Redshift.
Mapping the Reference Architecture to Azure3:53
Map the big data reference architecture to azure managed services, aligning ingestion to event hubs, batch to databricks, stream to stream analytics, and data lake to adls gen 2.
Mapping the Reference Architecture to GCP4:14
E-Commerce Architecture Design Exercise5:36
Lead architecture exercise guides you to justify Lambda architecture on AWS with Kinesis data streams, S3 Delta Lake, and Redis feature store for 200 ms recommendations, tying decisions to constraints.
Big Data Architecture Interview Prep — Top 10 Questions10:58

Requirements

Basic Knowledge of Big Data and data Architecture terminology
Desire to master Big Data , Data Architecture and Become Big Data Architect Expert
Basic knowledge of data concepts and computer systems will be helpful
This course welcomes learners from diverse backgrounds.

Description

Are you designing Big Data systems but feeling lost in a sea of tools, frameworks, and conflicting architectures?

Most courses teach you tools. This course teaches you how to think like a Big Data Architect.

Whether you're a data engineer trying to move up, a software architect expanding into data, or a student building toward a career in Big Data — this is the structured, framework-first course the industry has been missing.

Why this course is different:

Unlike bootcamps that throw Spark, Kafka, and Hadoop at you and call it architecture, this course gives you a universal reference model — a standardized Big Data blueprint that works across industries, deployment environments, and technology stacks. Healthcare, finance, e-commerce, IoT — one framework to rule them all.

You won't just learn what the components are. You'll learn why each exists, when to use which architecture pattern, and how to make strategic trade-offs like a senior architect.

What You Will Be Able to Do After This Course:

Design a complete, scalable Big Data system from scratch — adapting it to any industry or business model
Choose confidently between Lambda, Kappa, and Microservices architectures based on real project requirements
Architect robust data pipelines covering ingestion, ETL/ELT, batch and stream processing, analytics, and visualization
Select the right storage solution — Data Lake, Data Warehouse, Data Lakehouse, NoSQL, or SQL — for each use case
Understand and apply Data Mesh principles for decentralized, scalable data governance
Make infrastructure decisions around scalability, reliability, security, and performance
Communicate architecture decisions clearly to technical and non-technical stakeholders

What Makes This Architecture Framework Universal:

This course is built around a standardized Big Data Reference Architecture — a logical model inspired by NIST principles that maps every component of a Big Data system into a coherent, reusable blueprint. This is the foundation used by Big Data teams in enterprise environments across the world.

Every technology you encounter in the real world — Kafka, Spark, Airflow, Hive, Flink — has a place in this model. Once you understand the architecture, the tools become obvious choices rather than confusing options.

This course contains the use of artificial intelligence.

Course Content Overview:

High-Level Architecture (Conceptual Layer) The 4 Vs of Big Data (Volume, Velocity, Variety, Variability + Value) — Data Sources & Providers — Ingestion (Batch & Stream) — ETL/ELT & Preprocessing — Distributed Processing — Analytics Engines (Descriptive, Predictive, Prescriptive, ML/AI) — Visualization — Workload Orchestration — Data Lifecycle Management

Big Data IT Infrastructure Storage, Compute & Networking — Horizontal vs. Vertical Scaling — System Resource Management

Security & Governance Big Data Security Principles — Privacy Frameworks — Data Governance Best Practices

Low-Level Technical Architecture Lambda Architecture — Kappa Architecture — Microservices — Apache Kafka, Flume & Amazon Kinesis — Data Lakes, Warehouses & Lakehouses (Hadoop, S3, Hive) — Batch & Stream Processing: MapReduce, Apache Spark, Flink, Storm — Querying: Apache Hive, PIG, Presto, SparkSQL — Orchestration: Apache Airflow, Oozie, Luigi — Visualization: Kibana, Superset, Apache Zeppelin

Modern & Emerging Topics Data Mesh Architecture — Cloud Big Data Infrastructure — Edge Computing — Distributed Computing Deep Dive — Vector Databases & Feature Stores (context for ML integration)

Why Enroll Now?

Framework-first approach: Learn the architecture, then map any tool to it — future-proof your skills
Industry-agnostic: The same blueprint works for healthcare, fintech, retail, IoT, and more
Career-oriented: Designed to build the mindset of architects, not just practitioners
Constantly updated: Content reflects the 2024–2025 Big Data landscape including Data Mesh, cloud-native patterns, and modern orchestration

Stop learning isolated tools. Start thinking in systems. Enroll now and build the architectural foundation that will define your entire data career.

Who this course is for:

Big Data Architect
Big Data Scientists
Enterprise IT Architects
Big Data Engineers
Data Architects or Software Engineers who want to master Big Data Architecture
Anyone interested in Data Architecture or Big data Systems Design
Data Scientists
Data Engineers
Data Developpers
Aspiring Big Data Architects looking to design scalable and adaptable solutions across various industries.
Data Engineers and IT professionals aiming to elevate their skill set to architect-level in Big Data projects.
Business strategists and decision-makers interested in understanding the foundations of Big Data architecture
Software Engineers and Data Scientists seeking to broaden their expertise in Big Data solution design.

Big Data Architecture Masterclass - Complete Course - 2026

What you'll learn

Explore related topics

Course content

Introduction7 lectures • 21min

Big Data System : Software Architecture Perspective17 lectures • 1hr 11min

Big Data IT Infrastructure & Frameworks5 lectures • 17min

Big Data Security and Privacy2 lectures • 12min

Big Data Architecture - Low Level Overview - Technical Architecture11 lectures • 49min

Big Data in the Real World — Cloud & Career5 lectures • 29min

More learnings - Additional content7 lectures • 14min

Requirements

Description

Who this course is for: