Description
Amazon DEA-C01 Exam Syllabus & Study Guide
At Certs4Success, we provide the most accurate and up-to-date preparation materials for the Amazon DEA-C01 Exam. Our content is professionally designed to help you master data engineering concepts on AWS and pass the Amazon DEA-C01 Exam with confidence.
If you are planning to clear the Amazon AWS Certified Data Engineer – Associate (DEA-C01) Exam, this detailed syllabus will guide you through all the essential domains you need to focus on.
All Exam Topics of Amazon DEA-C01 Exam
Topic 1: Data Ingestion & Collection
Data Sources: Understanding structured, semi-structured, and unstructured data.
Ingestion Methods: Batch and real-time data ingestion techniques for the Amazon DEA-C01 Exam.
AWS Services: Using services like Kinesis and Data Pipeline.
Topic 2: Data Storage Solutions
Amazon S3: Scalable object storage for data lakes.
Data Warehousing: Using Amazon Redshift for analytics.
Database Services: RDS, DynamoDB, and Aurora.
Topic 3: Data Processing & Transformation
ETL Pipelines: Extract, transform, and load processes.
AWS Glue: Serverless data integration service.
Big Data Processing: Using EMR and Spark frameworks.
Topic 4: Data Modeling & Design
Schema Design: Designing efficient and scalable data models.
Partitioning: Improving query performance.
Data Formats: Working with Parquet, ORC, and JSON.
Topic 5: Data Security & Governance
Encryption: Securing data at rest and in transit.
Access Control: Managing permissions with IAM roles.
Compliance: Meeting regulatory requirements.
Topic 6: Data Analysis & Visualization
Querying Data: Using Amazon Athena for analysis.
Visualization Tools: Amazon QuickSight dashboards.
Insights Generation: Turning raw data into actionable insights.
Topic 7: Monitoring & Troubleshooting
Amazon CloudWatch: Monitoring pipelines and resources.
Logging: Tracking system performance and errors.
Troubleshooting: Identifying and resolving data workflow issues.
Topic 8: Streaming Data Solutions
Amazon Kinesis: Real-time data streaming.
Stream Processing: Handling continuous data flows.
Use Cases: Real-time analytics and event processing.
Topic 9: Cost Optimization & Performance
Cost Management: Reducing data processing costs.
Performance Tuning: Optimizing queries and pipelines.
Resource Management: Efficient use of AWS services.
Topic 10: Best Practices & Architecture
Data Lake Architecture: Designing modern data platforms.
Scalability: Building highly scalable systems.
Best Practices: Following AWS-recommended strategies for the Amazon DEA-C01 Exam.
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