Choosing an AWS analytics service

Data sources are diverse and come from various systems, applications, devices, and external platforms.

Data sources have unique structure, format, and frequency of data updates. Analyzing these sources helps in identifying suitable data collection methods and technologies.

Analyzing data types, such as structured, semi-structured, and unstructured data determines the appropriate data processing and storage approaches.

Analyzing data sources and types of facilitates data quality assessment, helps you anticipate potential data quality issues—missing values, inconsistencies, or inaccuracies.

Data transformation: Determine the specific transformations needed to make the raw data suitable for analysis. This involves tasks like data aggregation, normalization, filtering, and enrichment.

Data cleansing: Assess data quality and define processes to handle missing, inaccurate, or inconsistent data. Implement data cleansing techniques to ensure high-quality data for reliable insights.

Processing frequency: Determine whether real-time, near real-time, or batch processing is required based on the analytical needs. Real-time processing enables immediate insights, while batch processing may be sufficient for periodic analyses.

Scalability and throughput: Evaluate the scalability requirements for handling data volumes, processing speed, and the number of concurrent data requests. Ensure that the chosen processing approach can accommodate future growth.

Latency: Consider the acceptable latency for data processing and the time it takes from data ingestion to analysis results. This is particularly important for real-time or time-sensitive analytics.

Data volume: Assess the amount of data being generated and collected, and estimate future data growth to plan for sufficient storage capacity.

Data retention: Define the duration for which data should be retained for historical analysis or compliance purposes. Determine the appropriate data retention policies.

Data access patterns: Understand how data will be accessed and queried to choose the most suitable storage solution. Consider read and write operations, data access frequency, and data locality.

Data security: Prioritize data security by evaluating encryption options, access controls, and data protection mechanisms to safeguard sensitive information.

Cost optimization: Optimize storage costs by selecting the most cost-effective storage solutions based on data access patterns and usage.

Integration with analytics services: Ensure seamless integration between the chosen storage solution and the data processing and analytics tools in the pipeline.

Transactional data: Includes information about individual interactions or transactions, such as customer purchases, financial transactions, online orders, and user activity logs.

File-based data: Refers to structured or unstructured data that is stored in files, such as log files, spreadsheets, documents, images, audio files, and video files. Analytics services should support the ingestion of different file formats/

Event data: Captures significant occurrences or incidents, such as user actions, system events, machine events, or business events. Events can include any data that is arriving in high velocity that is captured for on-stream or down-stream processing.

Infrastructure abstraction: Serverless services abstract infrastructure management, relieving users from provisioning, scaling, and maintenance tasks. AWS handles these operational aspects, reducing management overhead.

Auto-Scaling and performance: Serverless services automatically scale resources based on workload demands, ensuring optimal performance without manual intervention.

High availability and disaster recovery: AWS provides high availability for serverless services. AWS manages data redundancy, replication, and disaster recovery to enhance data availability and reliability.

Security and compliance: AWS manages security measures, data encryption, and compliance for serverless services, adhering to industry standards and best practices.

Monitoring and logging: AWS offers built-in monitoring, logging, and alerting capabilities for serverless services. Users can access detailed metrics and logs through Amazon CloudWatch.

Data volume and frequency: Batch processing is suitable for large volumes of data with periodic updates.

Data latency: Batch processing might introduce some delay in delivering insights compared to real-time processing.

Data query complexity: Interactive analysis requires low-latency responses for quick feedback.

Data visualization: Evaluate the need for interactive data visualization tools to enable business users to explore data visually.

Data velocity and volume: Streaming workloads require real-time processing to handle high-velocity data.

Data windowing: Define data windowing and time-based aggregations for streaming data to extract relevant insights.

Descriptive analytics is ideal for gaining a historical overview

Diagnostic analytics helps understand the reasons behind past events

Predictive analytics forecasts future outcomes

Prescriptive analytics provides recommendations for optimal actions

Data availability and quality: Descriptive and diagnostic analytics rely on historical data, while predictive and prescriptive analytics require sufficient historical data and high-quality data to build accurate models.

Data volume and complexity: Predictive and prescriptive analytics require substantial data processing and computational resources. Ensure that your infrastructure and tools can handle the data volume and complexity.

Decision complexity: If decisions involve multiple variables, constraints, and objectives, prescriptive analytics may be more suitable to guide optimal actions.

Risk tolerance: Prescriptive analytics may provide recommendations, but come with associated uncertainties. Ensure that decision-makers understand the risks associated with the analytics outputs.

Data volume and growth: Assess the current data volume and anticipate future growth.

Data velocity and real-time requirements: Determine if the data needs to be processed and analyzed in real-time or near real-time.

Data processing complexity: Analyze the complexity of your data processing and analysis tasks. For computationally intensive tasks, services such as Amazon EMR provide a scalable and managed environment for big data processing.

Concurrency and user load: Consider the number of concurrent users and the level of user load on the system.

Auto-scaling capabilities: Consider services that offer auto-scaling capabilities, allowing resources to automatically scale up or down based on demand. This ensures efficient resource utilization and cost optimization.

Geographic distribution: Consider services with global replication and low-latency data access if your data architecture needs to be distributed across multiple regions or locations.

Cost-performance trade-off: Balance the performance needs with cost considerations. Services with high performance may come at a higher cost.

Service-level agreements (SLAs): Check the SLAs provided by AWS services to ensure they meet your scalability and performance expectations.

Data retention policies: Define data retention policies based on regulatory requirements and business needs and establish processes for secure data disposal when it is no longer needed.

Audit trail and logging: Decide on the logging and auditing mechanisms to monitor data access and usage. Implement comprehensive audit trails to track data changes, access attempts, and user activities for compliance and security monitoring.

Compliance requirements: Understand the industry-specific and geographic data compliance regulations that apply to your organization. Ensure that the data architecture aligns with these regulations and guidelines.

Data classification: Classify data based on its sensitivity and define appropriate security controls for each data class.

Disaster recovery and business continuity: Plan for disaster recovery and business continuity to ensure data availability and resilience in case of unexpected events or system failures.

Third-party data sharing: If sharing data with third-party entities, implement secure data sharing protocols and agreements to protect data confidentiality and prevent data misuse.

Access control and authorization: Implement robust authentication and authorization protocols to ensure that only authorized users can access specific data resources.

Data encryption: Choose appropriate encryption methods for data stored in databases, data lakes, and during data movement between different components of the architecture.

Data masking and anonymization: Consider the need for data masking or anonymization to protect sensitive data, such as PII or sensitive business data, while allowing certain analytical processes to continue.

Secure data integration: Establish secure data integration practices to ensure that data flows securely between different components of the architecture, avoiding data leaks or unauthorized access during data movement.

Network isolation: Consider services that support Amazon VPC Endpoints to avoid exposing resources to the public internet.

Data source integration: Identify the data sources from which data will be collected, such as databases, applications, files, or external APIs. Decide on the data ingestion methods (batch, real-time, event-based) to bring data into the pipeline efficiently and with minimal latency.

Data transformation: Determine the transformations required to prepare data for analysis. Decide on the tools and processes to clean, aggregate, normalize, or enrich the data as it moves through the pipeline.

Data movement architecture: Choose the appropriate architecture for data movement between pipeline components. Consider batch processing, stream processing, or a combination of both based on the real-time requirements and data volume.

Data replication and sync: Decide on data replication and synchronization mechanisms to keep data up-to-date across all components. Consider real-time replication solutions or periodic data syncs depending on data freshness requirements.

Data quality and validation: Implement data quality checks and validation steps to ensure the integrity of data as it moves through the pipeline. Decide on the actions to be taken when data fails validation, such as alerting or error handling.

Data security and encryption: Determine how data will be secured during transit and at rest. Decide on the encryption methods to protect sensitive data throughout the pipeline, considering the level of security required based on data sensitivity.

Scalability and resilience: Ensure that the data flow design allows for horizontal scalability and can handle increased data volumes and traffic.

Resource sizing and selection: Right-size your resources based on actual workload requirements. Choose AWS services and instance types that match the workloads performance needs while avoiding overprovisioning.

Auto-scaling: Implement auto-scaling for services that experience varying workloads. Auto-scaling dynamically adjusts the number of instances based on demand, reducing costs during low-traffic periods.

Spot Instances: Use Amazon EC2 Spot Instances for non-critical and fault-tolerant workloads. Spot Instances can significantly reduce costs compared to on-demand instances.

Reserved instances: Consider purchasing AWS Reserved Instances to achieve significant cost savings over on-demand pricing for stable workloads with predictable usage.

Data storage tiering: Optimize data storage costs by using different storage classes based on data access frequency.

Data lifecycle policies: Establish data lifecycle policies to automatically move or delete data based on its age and usage patterns. This helps manage storage costs and keeps data storage aligned with its value.