100% PASS AWS Certified Data Engineer Associate Exam Study Guide + CHEAT SHEET (Downloadable) скачать в хорошем качестве

100% PASS AWS Certified Data Engineer Associate Exam Study Guide + CHEAT SHEET (Downloadable)

CHEAT SHEET (DOWNLOAD LINK) - https://certcloudprojects.com/home/ex... Donation URLS : India payment link - https://razorpay.me/@sthithapragna Rest of the world payment link - https://paypal.me/sthithapragnaaws?co... Get exam-ready with a clear, exam-aligned overview & domain-by-domain walkthrough of the AWS Certified Data Engineer - Associate (DEA-C01) topics. What you’ll learn AWS Data Engineering Deep Dive: DEA-C01 Exam Prep Guide This video breaks down the four key domains you need to master for the AWS Certified Data Engineer – Associate (DEA-C01) exam. We cover everything from building scalable ETL pipelines to securing and governing your data on AWS. 1. Data Ingestion & Transformation Learn the core skills for managing data flow on AWS: Ingestion: Master Batch (S3, EMR, DMS) and Streaming (Kinesis, MSK, DynamoDB Streams) patterns, including managing throughput, latency, and replayability. Transformation (ETL): Build robust ETL pipelines using AWS Glue, Amazon EMR, and Lambda. Focus on Spark processing, data staging, optimizing container usage, and transforming data between formats (e.g., CSV to Parquet). Orchestration: Configure and manage complex workflows using services like AWS Step Functions, Amazon MWAA (Apache Airflow), and EventBridge. Programming: Apply programming and SQL skills, including using IaC (CDK/CloudFormation), optimizing code, and deploying serverless applications with AWS SAM. 2. Data Store Management The backbone of your data architecture: Data Store Selection: Choose the right service—Amazon Redshift, DynamoDB, Amazon RDS, or Amazon EMR—based on cost, performance, and access patterns. Data Cataloging: Build and reference a centralized data catalog using the AWS Glue Data Catalog to discover schemas and partition data. Data Lifecycle: Implement policies for hot/cold data storage, cost optimization, and data retention using S3 Lifecycle Policies and DynamoDB TTL. Data Modeling: Design schemas for various data stores, understand schema evolution, and establish data lineage (e.g., using AWS SCT and SageMaker ML Lineage Tracking). 3. Data Operations & Support Keep your pipelines running smoothly and reliably: Automation & Analysis: Use Lambda, Amazon MWAA, and Step Functions for automated processing. Analyze data with Amazon Athena, QuickSight, and AWS Glue DataBrew. Monitoring & Maintenance: Implement robust logging (CloudWatch Logs, CloudTrail) for audits and traceability. Troubleshoot performance and maintain pipelines (Glue, EMR). Data Quality: Implement data validation, run quality checks (e.g., checking for empty fields), and define data quality rules with tools like AWS Glue DataBrew. 4. Data Security & Governance Secure and govern your data environment: Security Mechanisms: Apply Authentication (IAM roles/policies, Secrets Manager) and Authorization (Least Privilege) concepts. Secure VPCs and manage access through Lake Formation. Encryption: Implement client-side and server-side encryption using AWS KMS and configure encryption in transit. Understand data anonymization and masking. Audit Readiness: Prepare logs for auditing using CloudTrail, CloudWatch Logs, and CloudTrail Lake. Privacy: Implement PII identification (Amazon Macie), data sharing permissions (Redshift Data Sharing), and data sovereignty strategies.