AWS Big Data Practice Exam
About the AWS Big Data Exam
The AWS Big Data exam evaluates candidates' proficiency in designing, implementing, and managing big data solutions on the Amazon Web Services (AWS) platform. It covers various aspects of big data technologies, including data storage, processing, analysis, and visualization using AWS services such as Amazon S3, Amazon EMR, Amazon Redshift, Amazon Kinesis, and AWS Glue. The exam assesses candidates' ability to architect scalable, secure, and cost-effective big data solutions to meet business requirements and derive insights from large datasets.
Skills Required:
- Data Storage and Management: Knowledge of data storage technologies, including Amazon S3, Amazon Glacier, and Amazon EBS, and their features for storing and managing large datasets.
- Data Processing and Analysis: Proficiency in big data processing frameworks such as Apache Hadoop, Apache Spark, and Apache Flink, and their integration with AWS services for data processing and analysis.
- Data Streaming: Understanding of real-time data streaming concepts and AWS services such as Amazon Kinesis for ingesting, processing, and analyzing streaming data.
- Data Warehousing: Familiarity with data warehousing concepts, Amazon Redshift, and columnar storage for performing complex queries and analytics on structured data.
- Data Lake: Knowledge of building data lakes on AWS using Amazon S3, AWS Glue, and other services for storing and analyzing diverse datasets at scale.
- Data Governance and Security: Understanding of data governance principles, compliance requirements, encryption methods, and access controls for securing big data solutions on AWS.
- Data Integration and ETL: Skills in data integration, extraction, transformation, and loading (ETL) processes using AWS Glue, AWS Data Pipeline, and other tools for preparing data for analysis.
- Data Visualization: Ability to visualize and communicate insights from big data using tools such as Amazon QuickSight, Tableau, or other visualization platforms.
- Performance Optimization: Optimization techniques for improving the performance, scalability, and cost-effectiveness of big data solutions on AWS.
- Monitoring and Logging: Monitoring, logging, and alerting mechanisms for monitoring the health, performance, and security of big data environments on AWS.
- High Availability and Disaster Recovery: Architecting high availability and disaster recovery solutions for big data applications on AWS to ensure business continuity and data integrity.
Who should take the Exam?
The AWS Big Data exam is suitable for data engineers, data architects, big data developers, solutions architects, and IT professionals involved in designing, implementing, and managing big data solutions on the AWS platform. It is beneficial for individuals seeking to validate their skills and expertise in leveraging AWS services for big data analytics, data warehousing, data lakes, and real-time data processing.
Detailed Course Outline:
The AWS Big Data Exam covers the following topics -
Module 1: Introduction to Big Data on AWS
- Overview of big data concepts, challenges, and opportunities. Introduction to AWS big data services and solutions.
Module 2: Data Storage and Management
- Amazon S3, Amazon Glacier, Amazon EBS. Storage classes, object lifecycle management, versioning, and encryption.
Module 3: Data Processing and Analysis
- Apache Hadoop, Apache Spark, Apache Flink. Amazon EMR, AWS Lambda, Amazon Athena, Amazon Elasticsearch Service.
Module 4: Data Streaming
- Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics. Real-time data ingestion, processing, and analysis.
Module 5: Data Warehousing
- Amazon Redshift, Redshift Spectrum, Amazon Redshift Spectrum. Columnar storage, data distribution, query optimization.
Module 6: Data Lake
- Building data lakes on AWS. Amazon S3, AWS Glue, AWS Lake Formation. Data cataloging, data ingestion, metadata management.
Module 7: Data Governance and Security
- Data governance principles, compliance requirements. Encryption, access controls, audit logging.
Module 8: Data Integration and ETL
- AWS Glue, AWS Data Pipeline, AWS Batch. Data integration, ETL processes, data transformation, and loading.
Module 9: Data Visualization
- Amazon QuickSight, Tableau, other visualization platforms. Creating dashboards, reports, and visualizations.
Module 10: Performance Optimization
- Optimizing performance, scalability, and cost-effectiveness of big data solutions. Resource optimization, query optimization.
Module 11: Monitoring and Logging
- AWS CloudWatch, AWS CloudTrail. Monitoring, logging, and alerting mechanisms for big data environments.
Module 12: High Availability and Disaster Recovery
- Architecting high availability and disaster recovery solutions for big data applications on AWS. Multi-region deployments, backup and restore strategies.