Cert Notes/ Commute Study Notes
Roadmap
KOEN
CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
  • Week 1
    • 1.What Is Data Engineering
    • 2.Batch vs Streaming
    • 3.A Bird's-Eye View of AWS Data Services
    • 4.Data Formats and Modeling
    • 5.Week 1 Comprehensive Review
  • Week 2
    • 1.Batch Ingestion: S3 Upload, DataSync, Transfer Family, Snow
    • 2.Kinesis Data Streams: Shards, Partition Keys, and Throughput
    • 3.Kinesis Data Firehose: Delivery Streams and Loading
    • 4.Amazon MSK (Kafka): Topics, Partitions, and When to Use What
    • 5.Week 2 Synthesis: Data Ingestion Part 1 Review
  • Week 3
    • 1.Streaming Processing: Managed Service for Apache Flink and Window Aggregation
    • 2.Ingestion Reliability: Idempotency, Ordering, Retries, Deduplication, DLQ
    • 3.CDC and Data Replication: Database Migration Service (DMS)
    • 4.Ingestion Architecture Patterns: Lambda Architecture and Event-Driven Ingestion
    • 5.Week 3 Synthesis: Data Ingestion Part 2 Review
  • Week 4
    • 1.Glue Data Catalog and Crawlers: Adding Metadata to Data
    • 2.Glue ETL Job: Transform Data on Spark
    • 3.Glue Studio and DataBrew: Transform Without Code
    • 4.Schema Management and Data Quality: Tolerate Evolution, Guarantee Trust
    • 5.Week 4 Synthesis: The Big Picture of AWS Glue Transformation
  • Week 5
    • 1.Amazon EMR: Spark, Hive and Cluster Operations, Plus EMR Serverless
    • 2.Lambda Transformation and Lightweight Processing: Event-Driven ETL's Limits and Fit
    • 3.Orchestration: Step Functions, MWAA, and Glue Workflows Selection Criteria
    • 4.Performance and Cost Optimization: File Format, Compression, Partitioning, and Small File Problem
    • 5.Week 5 Synthesis: Data Transformation 2 — Engines, Orchestration, Optimization Integration Review
  • Week 6
    • 1.S3 Data Lake Layout and Partitioning Strategy
    • 2.AWS Lake Formation Central Permission Management
    • 3.Open Table Formats: Iceberg, Hudi, Delta Lake
    • 4.S3 Storage Management and Cost Optimization
    • 5.Week 6 Comprehensive Review: Data Lake Recap
  • Week 7
    • 1.Amazon Redshift: Distribution and Sort Keys and Workload Optimization
    • 2.Amazon Athena: Serverless Queries and Cost Optimization
    • 3.DynamoDB (Analytics Perspective): Key Design and Stream-Based Pipelines
    • 4.RDS/Aurora and Store Selection: OLTP, Zero-ETL, Workload-to-Store Decision
    • 5.Week 7 Comprehensive Review: Analytics Stores Recap
  • Week 8
    • 1.Pipeline Monitoring: CloudWatch Metrics, Logs, and Alarms
    • 2.Data Quality and Validation: Glue Data Quality and Quality Gates
    • 3.Logging, Audit, and Troubleshooting: CloudTrail and Failure Recovery
    • 4.Cost and Performance Operations: Monitoring, Sizing, Auto Scaling
    • 5.Week 8 Comprehensive Review: Data Operations and Support Recap
  • Week 9
    • 1.Access Control: IAM and Lake Formation Permissions
    • 2.Encryption: KMS and Service-Specific Encryption
    • 3.Sensitive Data Protection: Macie and Masking
    • 4.Data Governance: Catalog, Lineage, Sharing, Auditing
    • 5.Week 9 Synthesis: Security and Governance Review
  • Week 10
    • 1.Integrated Review of Domains 1 & 2: Ingestion, Transformation & Storage Management
    • 2.Integrated Review of Domains 3 & 4: Operations & Support, Security & Governance
    • 3.Full-Length Practice Exam Pace: 8 Integrated Scenarios
    • 4.Common Traps & Keywords: "Requirement → Service" Translation Guide
    • 5.Final D-Day Prep: Exam Structure, Time Management & Scenario Breakdown Strategy
MLS-C01 · SpecialtyMachine Learning - Specialty
← DEA-C01/Week 7/Day 2
DEA-C01· AssociateWeek 7 · Day 2~8 min read

Day 2 - Amazon Athena: Serverless Queries and Cost Optimization

Amazon Athena is a serverless analytics service querying data on S3 with standard SQL. No infrastructure to provision; billed on scanned data volume. Today we cover partition and format optimization, CTAS, federated queries, and cost savings.

Athena Operating Model

Athena runs on Presto/Trino engine (Athena Engine v3) and uses Glue Data Catalog as metastore. Tables are just metadata pointing to S3 locations; data is not separately loaded (schema-on-read).

-- External table definition pointing to S3 path
CREATE EXTERNAL TABLE orders (
  order_id BIGINT,
  amount   DOUBLE,
  status   STRING
)
PARTITIONED BY (dt STRING)
STORED AS PARQUET
LOCATION 's3://datalake-clean/orders/';

The rest is Pro only

Week 1 is free for everyone. Week 2 onwards — plus mock exams and unlimited review — is included in the Pro plan.

See ProLogin
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