AWS Data Analytics Services

Turning Data into Decisions

Every organization generates data. Few organizations extract meaningful value from it. The gap is not a lack of data — it is a lack of infrastructure to collect, process, and analyze that data efficiently.

AWS provides a comprehensive suite of analytics services, but choosing the right architecture and assembling these services into a coherent platform requires experience. A poorly designed data pipeline is expensive to run, difficult to maintain, and slow to deliver insights. A well-designed one becomes a competitive advantage.

At FactualMinds, we design and build modern data analytics platforms on AWS that deliver the right data to the right people at the right time. As an AWS Select Tier Consulting Partner, we bring hands-on experience with the full AWS analytics stack.

AWS Data Analytics Architecture

A modern data platform on AWS typically follows a layered architecture:

Data Sources → Ingestion → Storage (Data Lake) → Processing (ETL) → Analytics → Visualization

Data Sources

Data comes from everywhere:

• Application databases — RDS, Aurora, DynamoDB transactional data
• SaaS applications — Salesforce, HubSpot, Stripe, Shopify
• Clickstream and events — Web analytics, mobile app events, IoT telemetry
• Logs — Application logs, infrastructure logs, access logs
• External data — Third-party APIs, market data, public datasets

Ingestion Layer

Getting data into your analytics platform reliably:

Storage Layer: The Data Lake

Amazon S3 is the foundation of every modern data platform on AWS. We implement data lakes with a structured approach:

Raw zone — Landing area for data in its original format. Data arrives here exactly as produced by the source system. This zone serves as your system of record.

Processed zone — Cleaned, validated, and transformed data in optimized formats (Parquet or ORC) with partitioning for query performance. This is where most analytical queries run.

Curated zone — Business-ready datasets aggregated, joined, and enriched for specific use cases — dashboards, reports, ML training data.

Archive zone — Historical data moved to S3 Glacier or Glacier Deep Archive with lifecycle policies to minimize storage costs.

Each zone has defined access controls using AWS Lake Formation, encryption using KMS, and lifecycle policies for cost management.

Processing Layer: ETL Pipelines

AWS Glue is the backbone of most ETL workloads:

• Glue Crawlers — Automatically discover schemas and populate the Glue Data Catalog
• Glue ETL Jobs — Spark-based transformations that clean, validate, and transform data at scale
• Glue Data Quality — Built-in data quality rules that validate data at every pipeline stage
• Glue Studio — Visual ETL design for analysts who prefer a low-code approach

AWS Step Functions orchestrate complex pipelines:

• Multi-step workflows with conditional branching and error handling
• Parallel processing for independent data sources
• Retry logic with exponential backoff for transient failures
• Integration with Glue, Lambda, Athena, Redshift, and other services

For simpler transformations, Lambda functions process individual records or small batches with serverless compute — no infrastructure to manage.

Analytics Layer

Amazon Athena — Serverless SQL

Athena lets you query data directly in S3 using standard SQL. No infrastructure to provision, no clusters to manage — you pay per terabyte scanned.

Optimization strategies we implement:

• Columnar formats — Convert data to Parquet or ORC to reduce scan costs by 90%+
• Partitioning — Partition data by date, region, or other high-cardinality columns to limit scan scope
• Bucketing — Hash-distribute data within partitions for join-heavy queries
• Compression — Snappy or ZSTD compression to reduce storage and scan costs
• Workgroups — Separate workgroups with per-query and monthly spending limits

With proper optimization, Athena queries that would cost $5 scanning raw JSON can be reduced to $0.05 scanning partitioned, compressed Parquet.

Amazon Redshift — Data Warehouse

For workloads that need fast, repeatable queries across structured datasets — dashboards refreshed every 15 minutes, complex joins across millions of rows, sub-second response times — Redshift delivers:

• Redshift Serverless — Auto-scaling compute with pay-per-use pricing. Ideal for variable or unpredictable query workloads.
• Provisioned clusters — Dedicated compute for steady-state, high-frequency analytics. Ra3 instances separate compute from managed storage.
• Redshift Spectrum — Query data in S3 directly from Redshift, combining data warehouse and data lake queries in a single SQL statement.
• Materialized views — Pre-computed aggregations that accelerate dashboard queries.

Amazon OpenSearch — Search and Log Analytics

For full-text search, log analytics, and observability:

• Centralized log analytics across application and infrastructure logs
• Full-text search over document collections
• Real-time dashboards with OpenSearch Dashboards (Kibana-compatible)

Visualization Layer

Amazon QuickSight

QuickSight provides serverless business intelligence with:

• Interactive dashboards — Drag-and-drop dashboard builder connected to Athena, Redshift, RDS, or S3. See our QuickSight dashboards guide for patterns.
• Embedded analytics — Embed dashboards into your SaaS product for customer-facing analytics
• QuickSight Q — Natural language queries powered by Amazon Q for QuickSight let business users ask questions in plain English
• SPICE engine — In-memory caching for fast dashboard rendering
• Pay-per-session pricing — Readers pay only when they view dashboards, making it cost-effective for large organizations

Common Data Analytics Patterns

Pattern 1: Batch Analytics Platform

For organizations that need daily or hourly reporting:

RDS/DynamoDB → DMS → S3 (raw) → Glue ETL → S3 (processed, Parquet) → Athena/Redshift → QuickSight

Orchestration: Step Functions trigger Glue jobs on a schedule or in response to data arrival events.

Pattern 2: Real-Time Analytics

For live dashboards, fraud detection, or clickstream analytics:

Application Events → Kinesis Data Streams → Kinesis Data Analytics (Flink) → DynamoDB/OpenSearch → Dashboard
                                          → Kinesis Firehose → S3 (archive)

Use cases: Real-time revenue dashboards, fraud scoring, live recommendation engines.

Pattern 3: Data Lake with Self-Service Analytics

For organizations that want analysts to explore data independently:

Multiple Sources → Glue ETL → S3 Data Lake → Lake Formation (access control) → Athena (SQL) + SageMaker (ML)
                                              → Glue Data Catalog (schema registry)

Key feature: Lake Formation provides fine-grained access control so analysts see only the data they are authorized to access.

Pattern 4: Hybrid Data Warehouse + Data Lake

For organizations that need both ad-hoc exploration and high-performance dashboards:

S3 Data Lake → Redshift Spectrum (ad-hoc) + Redshift (curated warehouse) → QuickSight

Redshift Spectrum queries data in S3 for exploration, while critical reporting datasets are loaded into Redshift for fast, repeatable queries.

Data Governance and Security

AWS Lake Formation

Lake Formation provides centralized access control for your data lake:

• Table and column-level permissions — Grant access to specific tables or even specific columns
• Row-level filtering — Different users see different rows based on their attributes
• Tag-based access control — Define access policies based on data classification tags
• Cross-account sharing — Securely share data between AWS accounts without copying

Data Catalog

The Glue Data Catalog serves as your metadata repository:

• Automatic schema discovery with Glue Crawlers
• Schema versioning to track changes over time
• Business metadata (descriptions, data owners, classifications)
• Integration with Athena, Redshift Spectrum, and EMR

Encryption and Compliance

• All data encrypted at rest using KMS (S3 SSE-KMS, Redshift encryption, Glue job encryption)
• All data encrypted in transit with TLS 1.2+
• CloudTrail logging for all API calls and data access
• S3 access logging for data lake audit trails
• Compliance with HIPAA, SOC 2, PCI DSS, and GDPR through proper configuration of AWS security controls

Cost Optimization for Data Platforms

Data platforms can become expensive without cost discipline:

• S3 storage tiers — Move processed data to Infrequent Access after 30 days, archive to Glacier after 90 days
• Athena query optimization — Columnar formats + partitioning can reduce query costs by 95%
• Redshift Serverless — Pay only for compute when queries run, versus always-on provisioned clusters
• Glue job optimization — Right-size DPU allocation, use Glue auto-scaling, and implement job bookmarks to avoid reprocessing
• Reserved capacity — Redshift reserved nodes for steady-state workloads (up to 75% discount)

For comprehensive AWS cost optimization across your data platform and other workloads, talk to our cloud economics team.

Getting Started

For caching strategies that complement analytics workloads, see our ElastiCache Redis guide. For event-driven data pipelines, read our EventBridge patterns guide.

Whether you are building a data platform from scratch, modernizing a legacy data warehouse, or optimizing an existing analytics environment, our team brings the architectural expertise and hands-on implementation experience to deliver results.

Contact us to discuss your data analytics needs →