Big Data Engineer Study Guide (2026)

Big Data Engineer Study Guide 2026 – Pass on Your First Attempt

This Big Data Engineer study guide covers all exam domains, key concepts, and real exam-style scenarios to help you pass on your first attempt. Learn what topics matter most, avoid common mistakes, and follow a structured plan based on the official exam blueprint.

Edureify AI helps you identify your strengths and weak areas using real exam-style questions, detailed explanations, and domain-level analysis. Get a personalized study plan, track your progress, and focus only on what will improve your Big Data Engineer exam score.

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What should you study for the Big Data Engineer exam?

To pass the Big Data Engineer certification exam, you should focus on:

  • Big Data Architecture and Design: Understanding the design and architecture of big data systems, including Hadoop, Spark, and cloud technologies.
  • Data Pipeline and Integration: Designing and managing data pipelines and integrating big data sources into scalable systems.
  • Big Data Storage and Management: Managing data storage solutions for large datasets, including NoSQL and data warehousing.
  • Big Data Security and Governance: Ensuring the security and governance of big data systems, including privacy and compliance aspects.
  • Data Processing and Analytics: Analyzing large-scale datasets using big data tools and processing frameworks.

The exam tests your ability to apply concepts in real scenarios, not just memorize definitions.

Big Data Engineer Exam Syllabus and Topics

The Big Data Engineer exam is divided into 5 domains. Each domain tests specific skills and contributes to your overall score.

Big Data Architecture and Design

Understanding the design and architecture of big data systems, including Hadoop, Spark, and cloud technologies.

25%
Weight
33
Questions
165
Marks

Hadoop Ecosystem and Architecture

  • HDFS (Hadoop Distributed File System)
  • MapReduce Framework
  • YARN (Yet Another Resource Negotiator)

Apache Spark Architecture

  • Spark RDDs
  • Spark SQL and DataFrames
  • Spark Streaming

Cloud Platforms for Big Data

  • AWS (Amazon Web Services) for Big Data
  • Google Cloud Big Data Tools
  • Azure Data Services

Data Pipeline and Integration

Designing and managing data pipelines and integrating big data sources into scalable systems.

20%
Weight
26
Questions
130
Marks

Data Ingestion and Collection

  • Batch vs. Stream Processing
  • Kafka and Data Streaming
  • Apache NiFi

Data Transformation and Processing

  • ETL (Extract, Transform, Load) Processes
  • Apache Flink and Beam
  • Data Quality and Validation

Data Integration Tools

  • Apache Sqoop
  • Talend
  • Airflow for Workflow Automation

Big Data Storage and Management

Managing data storage solutions for large datasets, including NoSQL and data warehousing.

15%
Weight
19
Questions
95
Marks

Types of NoSQL Databases

  • Document Databases (MongoDB, CouchDB)
  • Key-Value Stores (Redis, DynamoDB)
  • Column-Family Stores (Cassandra, HBase)

Data Modeling in NoSQL

  • Schema Design for NoSQL
  • Data Consistency and Sharding

Data Warehouse Architecture

  • OLAP vs OLTP
  • Data Warehouse Design Principles

Big Data Storage Solutions

  • Columnar Storage (Parquet, ORC)
  • Data Lakes and Lakehouses

Big Data Security and Governance

Ensuring the security and governance of big data systems, including privacy and compliance aspects.

15%
Weight
19
Questions
95
Marks

Data Encryption and Privacy

  • Data Masking and Tokenization
  • GDPR and Data Compliance
  • Encryption at Rest and in Transit

Access Control and Authentication

  • Kerberos Authentication
  • OAuth and OpenID
  • Apache Ranger and Sentry

Data Governance Principles

  • Metadata Management
  • Data Stewardship
  • Data Lineage

Regulatory Compliance

  • Data Privacy Regulations
  • Audit Logs and Monitoring

Data Processing and Analytics

Analyzing large-scale datasets using big data tools and processing frameworks.

25%
Weight
33
Questions
165
Marks

Batch and Stream Processing

  • Batch Processing with Hadoop
  • Stream Processing with Apache Kafka and Spark

Data Processing with Spark

  • Spark RDD and DataFrames
  • Spark MLlib and ML Algorithms

Machine Learning with Big Data

  • Clustering and Classification
  • Big Data Machine Learning Frameworks

Data Visualization

  • Visualization with Hadoop and Spark
  • Using Tools like Tableau and Power BI
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Learning Progress

Big Data Architecture and Design 85%
Data Pipeline and Integration 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

Frequently Asked Questions