Domain 1: Collection

• Determine the operational characteristics of the collection system
• Evaluate that the data loss is within tolerance limits in the event of failures
• Evaluate costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (e.g., networking, bandwidth, ETL/data migration costs)
• Assess the failure scenarios that the collection system may undergo, and take remediation actions based on impact
• Determine data persistence at various points of data capture
• Identify the latency characteristics of the collection system
• Select a collection system that handles the frequency, volume, and the source of data
• Describe and characterize the volume and flow characteristics of incoming data (streaming, transactional, batch)
• Match flow characteristics of data to potential solutions
• Assess the tradeoffs between various ingestion services taking into account scalability, cost, fault tolerance, latency, etc.
• Explain the throughput capability of a variety of different types of data collection and identify bottlenecks
• Choose a collection solution that satisfies connectivity constraints of the source data system
• Select a collection system that addresses the key properties of data, such as order, format, and compression
• Describe how to capture data changes at the source
• Discuss data structure and format, compression applied, and encryption requirements
• Distinguish the impact of out-of-order delivery of data, duplicate delivery of data, and the tradeoffs between at-most-once, exactly-once, and at-least-once processing
• Describe how to transform and filter data during the collection process

Domain 2: Storage and Data Management

• Determine the operational characteristics of the storage solution for analytics
• Determine the appropriate storage service(s) on the basis of cost vs. performance
• Understand the durability, reliability, and latency characteristics of the storage solution based on requirements
• Determine the requirements of a system for strong vs. eventual consistency of the storage system
• Determine the appropriate storage solution to address data freshness requirements
• Determine data access and retrieval patterns
• Determine the appropriate storage solution based on update patterns (e.g., bulk, transactional, micro batching)
• Determine the appropriate storage solution based on access patterns (e.g., sequential vs. random access, continuous usage vs.ad hoc)
• Determine the appropriate storage solution to address change characteristics of data (appendonly changes vs. updates)
• Determine the appropriate storage solution for long-term storage vs. transient storage
• Determine the appropriate storage solution for structured vs. semi-structured data
• Determine the appropriate storage solution to address query latency requirements
• Select appropriate data layout, schema, structure, and format
• Determine appropriate mechanisms to address schema evolution requirements
• Select the storage format for the task
• Select the compression/encoding strategies for the chosen storage format
• Select the data sorting and distribution strategies and the storage layout for efficient data access
• Explain the cost and performance implications of different data distributions, layouts, and formats (e.g., size and number of files)
• Implement data formatting and partitioning schemes for data-optimized analysis
• Define data lifecycle based on usage patterns and business requirements
• Determine the strategy to address data lifecycle requirements
• Apply the lifecycle and data retention policies to different storage solutions
• Determine the appropriate system for cataloging data and managing metadata
• Evaluate mechanisms for discovery of new and updated data sources
• Evaluate mechanisms for creating and updating data catalogs and metadata
• Explain mechanisms for searching and retrieving data catalogs and metadata
• Explain mechanisms for tagging and classifying data

Domain 3: Processing

• Determine appropriate data processing solution requirements
• Understand data preparation and usage requirements
• Understand different types of data sources and targets
• Evaluate performance and orchestration needs
• Evaluate appropriate services for cost, scalability, and availability
• Design a solution for transforming and preparing data for analysis
• Apply appropriate ETL/ELT techniques for batch and real-time workloads
• Implement failover, scaling, and replication mechanisms
• Implement techniques to address concurrency needs
• Implement techniques to improve cost-optimization efficiencies
• Apply orchestration workflows
• Aggregate and enrich data for downstream consumption
• Automate and operationalize data processing solutions
• Implement automated techniques for repeatable workflows
• Apply methods to identify and recover from processing failures
• Deploy logging and monitoring solutions to enable auditing and traceability

Domain 4: Analysis and Visualization

• Determine the operational characteristics of the analysis and visualization solution
• Determine costs associated with analysis and visualization
• Determine scalability associated with analysis
• Determine failover recovery and fault tolerance within the RPO/RTO
• Determine the availability characteristics of an analysis tool
• Evaluate dynamic, interactive, and static presentations of data
• Translate performance requirements to an appropriate visualization approach (pre-compute and consume static data vs. consume dynamic data)
• Select the appropriate data analysis solution for a given scenario
• Evaluate and compare analysis solutions
• Select the right type of analysis based on the customer use case (streaming, interactive, collaborative, operational)
• Select the appropriate data visualization solution for a given scenario
• Evaluate output capabilities for a given analysis solution (metrics, KPIs, tabular, API)
• Choose the appropriate method for data delivery (e.g., web, mobile, email, collaborative notebooks)
• Choose and define the appropriate data refresh schedule
• Choose appropriate tools for different data freshness requirements (e.g., Amazon Elasticsearch
• Service vs. Amazon QuickSight vs. Amazon EMR notebooks)
• Understand the capabilities of visualization tools for interactive use cases (e.g., drill down, drill through and pivot)
• Implement the appropriate data access mechanism (e.g., in memory vs. direct access)
• Implement an integrated solution from multiple heterogeneous data sources

Domain 5: Security

• Select appropriate authentication and authorization mechanisms
• Implement appropriate authentication methods (e.g., federated access, SSO, IAM)
• Implement appropriate authorization methods (e.g., policies, ACL, table/column level permissions)
• Implement appropriate access control mechanisms (e.g., security groups, role-based control)
• Apply data protection and encryption techniques
• Determine data encryption and masking needs
• Apply different encryption approaches (server-side encryption, client-side encryption, AWS
• KMS, AWS CloudHSM)
• Implement at-rest and in-transit encryption mechanisms
• Implement data obfuscation and masking techniques
• Apply basic principles of key rotation and secrets management
• Apply data governance and compliance controls
• Determine data governance and compliance requirements
• Understand and configure access and audit logging across data analytics services
• Implement appropriate controls to meet compliance requirements