Big Data Engineer Certification Exam Preparation With Edureify AI

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240 min
Duration
130
Questions
80
Passing Score

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Learning Progress

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

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Big Data Engineer Syllabus

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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