Choosing optimal data stores, understanding data cataloging systems, managing data lifecycles, and designing data models with schema evolution on AWS.
This domain focuses on selecting and managing the right data storage solutions on AWS. It covers choosing storage services based on cost, performance, and access pattern requirements across Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, and Amazon Kinesis. Candidates must understand data cataloging systems including the AWS Glue Data Catalog, Glue crawlers, schema discovery, partition synchronization, and business data catalogs with Amazon SageMaker Catalog. The domain also covers managing the lifecycle of data through S3 Lifecycle policies, storage tiering, data versioning, DynamoDB TTL, and data deletion for compliance. Additionally, it tests designing data models and schema evolution including schema design for Redshift and DynamoDB, schema conversion with AWS SCT and DMS, data lineage tracking, open table formats like Apache Iceberg, vector database concepts (HNSW, IVF), and optimization techniques such as indexing, partitioning, and compression. (26% of exam)
5 minutes
5 Questions
Data Store Management is a critical domain in the AWS Certified Data Engineer - Associate certification that focuses on designing, implementing, and maintaining various data storage solutions on AWS. It encompasses selecting appropriate data stores based on requirements such as performance, cost, scalability, and data access patterns.
Key areas include:
**Amazon S3 (Simple Storage Service):** Understanding storage classes (Standard, Intelligent-Tiering, Glacier), lifecycle policies, versioning, encryption, and partitioning strategies for data lakes. S3 serves as the foundation for most AWS data architectures.
**Amazon Redshift:** Managing data warehousing solutions, including distribution styles (KEY, EVEN, ALL), sort keys, compression encoding, vacuum operations, and workload management (WLM) to optimize query performance.
**Amazon DynamoDB:** Handling NoSQL data stores with proper partition key design, capacity modes (on-demand vs. provisioned), secondary indexes (GSI/LSI), TTL settings, and DynamoDB Streams for change data capture.
**Amazon RDS and Aurora:** Managing relational databases, including read replicas, Multi-AZ deployments, automated backups, and performance tuning for transactional workloads.
**Data Catalog and Schema Management:** Leveraging AWS Glue Data Catalog for metadata management, schema discovery, crawlers, and maintaining data governance through classification and tagging.
**Data Lifecycle Management:** Implementing strategies for data retention, archival, and deletion using S3 lifecycle policies, automated snapshots, and compliance-driven data management practices.
**Security and Access Control:** Applying encryption at rest and in transit, IAM policies, bucket policies, VPC endpoints, and Lake Formation permissions to secure data stores.
**Performance Optimization:** Understanding caching with ElastiCache or DAX, data partitioning strategies, file format selection (Parquet, ORC, Avro), and compression techniques to improve query efficiency and reduce costs.
Data engineers must evaluate trade-offs between consistency, availability, durability, and cost when choosing and managing data stores, ensuring the architecture meets both current and future business requirements while maintaining data quality and governance standards.Data Store Management is a critical domain in the AWS Certified Data Engineer - Associate certification that focuses on designing, implementing, and maintaining various data storage solutions on AWS. It encompasses selecting appropriate data stores based on requirements such as performance, cost, s…
