Data Analytics
We design and build modern data platforms on AWS that turn raw data into actionable business intelligence — from data lakes to real-time analytics dashboards.
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AWS data analytics services — scalable data warehouse, ETL/ELT pipelines, real-time analytics, and business intelligence.
A data lake stores raw, unprocessed data in its native format (JSON, CSV, Parquet, logs) on Amazon S3 — schema is applied when you query. A data warehouse like Amazon Redshift stores structured, pre-processed data optimized for fast analytical queries. Most modern data platforms use both: a data lake for raw storage and flexible exploration, with a data warehouse for high-performance reporting on curated datasets.
Costs vary widely based on data volume and query patterns. A small data lake (under 1 TB) with Glue ETL and Athena queries can run for $50-200/month. Mid-size platforms (1-10 TB) with regular ETL and QuickSight dashboards typically cost $500-2,000/month. Enterprise platforms with Redshift, real-time streaming, and ML pipelines range from $5,000-20,000+/month. We design for cost efficiency at every tier.
Use Athena for ad-hoc queries, exploration, and workloads where query frequency is low to moderate — you pay per query with no infrastructure to manage. Use Redshift for high-frequency dashboards, complex joins across large datasets, and workloads that need sub-second query response times. Many clients use both: Athena for exploration and Redshift Serverless or provisioned clusters for production dashboards.
Yes. We migrate data warehouses from on-premises systems (Oracle, SQL Server, Teradata) and other cloud platforms to Amazon Redshift or a modern data lake architecture. Migrations include schema conversion, ETL pipeline rebuilding, report migration, and parallel validation to ensure data accuracy.
We implement data quality checks at every pipeline stage using AWS Glue Data Quality rules, custom validation in Step Functions, and data catalog management with AWS Glue Data Catalog. For governance, we implement Lake Formation for fine-grained access control — which in 2026 extends to both read and write operations with row- and cell-level security on Iceberg tables — data classification tagging, and audit logging of all data access. S3 Tables auto-populate the Glue Data Catalog when you create your first table bucket, so analytical workloads picked up by Athena, Redshift, QuickSight, and the AWS-optimized Spark runtime stay in sync without manual registration.
Yes. We build real-time analytics pipelines using Amazon Kinesis Data Streams for ingestion, Kinesis Data Analytics (Apache Flink) for stream processing, and DynamoDB or OpenSearch for real-time serving. Common use cases include live dashboards, fraud detection, clickstream analytics, and IoT telemetry.
AWS data analytics is a stack of managed services for ingesting, storing, processing, and visualizing data at any scale on Amazon Web Services. Core building blocks include Amazon S3 for data lakes, AWS Glue for ETL, Amazon Athena for ad-hoc SQL, Amazon Redshift for warehousing, Amazon Kinesis for streaming, and Amazon QuickSight for BI — all governed through AWS Lake Formation and the Glue Data Catalog.
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. This includes data warehouse modernization — migrating legacy on-premises data warehouses (Oracle, SQL Server, Teradata) to Amazon Redshift or a modern data lake architecture on S3 and Athena. As an AWS Select Tier Consulting Partner, we bring hands-on experience with the full AWS analytics stack.
For organizations looking to layer AI on top of their analytics platform, our AWS Bedrock and AWS SageMaker services build on the data foundations we create here — enabling natural language queries, predictive analytics, and ML-powered business intelligence.
A modern data platform on AWS typically follows a layered architecture:
Data Sources → Ingestion → Storage (Data Lake) → Processing (ETL) → Analytics → VisualizationData comes from everywhere:
Getting data into your analytics platform reliably:
| Method | AWS Service | Best For |
|---|---|---|
| Batch ingestion | AWS Glue, DMS, Step Functions | Database replication, file processing |
| Real-time streaming | Kinesis Data Streams, Kinesis Firehose | Clickstream, IoT, event-driven data |
| Change data capture | DMS with CDC, DynamoDB Streams | Real-time database replication |
| API ingestion | Lambda + EventBridge | SaaS application data |
| File transfer | Transfer Family, S3 Transfer Acceleration | Partner data, large file uploads |
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.
AWS Glue is the backbone of most ETL workloads:
AWS Step Functions orchestrate complex pipelines:
For simpler transformations, Lambda functions process individual records or small batches with serverless compute — no infrastructure to manage.
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:
With proper optimization, Athena queries that would cost $5 scanning raw JSON can be reduced to $0.05 scanning partitioned, compressed Parquet.
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:
For full-text search, log analytics, and observability:
QuickSight provides serverless business intelligence with:
For organizations that need daily or hourly reporting:
RDS/DynamoDB → DMS → S3 (raw) → Glue ETL → S3 (processed, Parquet) → Athena/Redshift → QuickSightOrchestration: Step Functions trigger Glue jobs on a schedule or in response to data arrival events.
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.
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.
For organizations that need both ad-hoc exploration and high-performance dashboards:
S3 Data Lake → Redshift Spectrum (ad-hoc) + Redshift (curated warehouse) → QuickSightRedshift Spectrum queries data in S3 for exploration, while critical reporting datasets are loaded into Redshift for fast, repeatable queries.
Lake Formation provides centralized access control for your data lake:
The Glue Data Catalog serves as your metadata repository:
Data platforms can become expensive without cost discipline:
For comprehensive AWS cost optimization across your data platform and other workloads, talk to our cloud economics team.
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.
Scalable data lakes on S3 plus fully managed Apache Iceberg tables on Amazon S3 Tables — automatic compaction, materialized views (2026), and a built-in AWS Glue Data Catalog integration that auto-registers tables in your account. Lake Formation now extends fine-grained access control to both read and write operations (row- and cell-level).
Automated batch and streaming pipelines using AWS Glue 5.0 (Iceberg-native, S3 Tables support), Step Functions, and EventBridge. AWS Glue Zero-ETL moves data from Aurora, DynamoDB, Salesforce, and SAP into Amazon Redshift or S3 Tables without manual pipelines — CDC, schema discovery, and evolution are all managed.
Query your data lake directly with standard SQL using Amazon Athena — no infrastructure to manage, pay per query.
Amazon Redshift for structured analytics workloads that require fast joins, aggregations, and complex queries across terabytes of data.
Interactive dashboards and reports with Amazon QuickSight, embedded analytics, and AI-powered insights.
Kinesis Data Streams and Firehose for real-time data ingestion, processing, and analytics on streaming data.
From data ingestion to visualization — one team that covers the entire data pipeline, not just one layer.
We design data platforms that deliver insights without runaway costs — right-sized compute, efficient storage tiers, and pay-per-query where appropriate.
Architectures validated across industries — SaaS, eCommerce, healthcare, and financial services.
Deep expertise across the full AWS analytics stack with hands-on deployment experience.
Verticalized engagements aligned to industry threat models, compliance, and reference architectures.
We build analytics platforms for retail and e-commerce companies on AWS that turn transaction data into actionable insights — customer segmentation, demand forecasting, and real-time personalization.
We build HIPAA-compliant analytics platforms on AWS that transform clinical and operational data into insights — population health analysis, outcomes research, and operational efficiency.
We build analytics platforms for real estate companies that turn property data into competitive advantages — market analysis, automated valuations, and portfolio performance tracking.
We build industrial analytics platforms on AWS that connect factory floor telemetry to executive dashboards — predictive maintenance that reduces unplanned downtime, OEE monitoring, and supply chain analytics for manufacturers.
Implementation guides for this service from our team of AWS experts.
AWS Glue automates ETL (Extract, Transform, Load) workflows while Athena provides serverless SQL queries. This guide covers building a complete data pipeline: ingesting raw data, transforming it, and querying at scale without managing servers.
S3 + Glue + Athena is the canonical AWS data-lake stack. It's also the one teams over-engineer the fastest. A reference architecture with the partitioning scheme, the Glue crawler config, and the Athena cost guardrails that keep query bills predictable as the lake grows.
AWS Glue 5.1 brings Apache Iceberg 1.10.0, Spark 3.5.6, and Delta Lake 3.3.2. Here is how to use these together to build a production lakehouse on AWS — with time travel, ACID transactions, and schema evolution.
AWS Glue and dbt solve different transformation problems. Glue runs Spark for large-scale ETL across any data source. dbt runs SQL transforms inside your data warehouse. Here is how to choose — and when to use both.
Kinesis Data Streams and Amazon MSK both handle real-time streaming on AWS, but they serve different architectures. Here is how to choose between them for your workload.
Athena charges per TB of data scanned. The right partitioning, compression, and table format can cut your Athena bill by 90%. Here is exactly how to do it.
Redshift Serverless removes cluster management but is not always cheaper. Here is exactly when to choose Serverless, when to stay Provisioned, and how to calculate the cost difference.
Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is the fully managed way to run stateful stream processing on AWS. Here is everything you need to know to use it in production.
Copying curated Parquet into every consumer account is how data platforms drown in storage cost and permission sprawl. On Feb 11, 2026 AWS shipped Lake Formation cross-account version 5 — wildcard RAM shares for hundreds of thousands of tables. A composite 12-account platform cut duplicate curated copies from 3 to 0 and dropped cross-account access tickets from ~11/month to ~3 by standardizing LF-Tags + resource links.
For a mid-market 3PL (~2.1M shipments/yr, OTIF 82%), layering AWS Supply Chain on ERP ingest plus Location Services fleet tracking moved ETA accuracy from 71% to 87% in 90 days — without replacing Manhattan WMS.
On a multi-domain retailer (~4,200 Glue tables, 11 AWS accounts), publishing a stewardship RACI plus SageMaker Catalog subscriptions cut mean time-to-data-access from 19 days to 4 days — without replacing Lake Formation enforcement.
For a specialty retailer (~380 stores, 2.4M loyalty members), S3 Tables Iceberg silver plus Kinesis cart events cut dashboard refresh from 26 hours to 22 minutes — conversion reporting error dropped from 11% to 2%.
For discrete manufacturing (~850 OPC-UA tags, 12 lines), Greengrass + IoT SiteWise anomaly detection moved OEE from 61% to 74% in 90 days — unplanned downtime −18% without replacing MES.
For a multi-clinic digital health platform (~2.4M FHIR resources, 180 TB imaging), HealthLake + HealthImaging cut CCDA-to-FHIR onboarding from 14 weeks to 9 days — query latency p95 820 ms → 190 ms on clinical dashboards.
For a regional PropTech platform (~420k listings, 38 MLS connectors), DynamoDB canonical store + OpenSearch geo cut median search latency from 340 ms to 48 ms — image CDN cost −62% with Intelligent-Tiering.
For an enterprise lake (~38 TB Iceberg, 8.4k small files), S3 Tables managed compaction cut Athena scan spend −35% — compaction OPEX −90% vs pre–July 2025 pricing on the same workload.
For a retailer DW exit (~42 TB logical, 18 nightly Glue jobs), Aurora→Redshift zero-ETL retired 11 jobs and cut dashboard freshness from 6h → ~15 min — concurrency scaling on zero-ETL (March 2026) absorbed Monday open spikes.
Third-party tools we frequently wire into AWS as part of this engagement — production-tested integration guides for each.
Snowflake + AWS in 2026: Cortex Analyst, Iceberg Tables on S3, Hybrid Tables, Snowpark, Polaris Catalog — vs Redshift, Athena, SageMaker Lakehouse.
MongoDB Atlas on AWS in 2026: MongoDB 8.0, Vector Search GA, Stream Processing, Queryable Encryption, Edge Server — vs DynamoDB, OpenSearch, pgvector.
Architecture patterns, decision trees, and glossary terms that map to this engagement.
Production lakehouse reference architecture on AWS — S3 Tables (managed Apache Iceberg), Glue Data Catalog, Athena, Redshift Spectrum, Lake Formation, and Managed Service for Apache Flink for streaming ingest. The AWS-native default for unified analytics in 2026.
Fully managed cloud data warehouse for running fast SQL analytics on petabyte-scale datasets.
Amazon Simple Storage Service — scalable object storage for any amount of data, used for backups, data lakes, static websites, and application assets.
Real engagements where FactualMinds implemented this service for clients.
Configured Amazon QuickSight with SPICE in-memory engine to deliver near real-time campaign analytics, eliminating reporting lag and reducing Aurora database overhead.
Read case studyConnected 280 production assets to AWS IoT SiteWise, deployed native anomaly detection for predictive maintenance, and reduced unplanned downtime by 40% — from sensor to alert in under 8 seconds.
Read case studyIn-depth comparisons to help you choose the right approach before engaging.
Technical comparison of Amazon DynamoDB vs RDS. Schema flexibility, query patterns, scaling, and when to choose each.
Technical comparison of Amazon RDS vs Aurora — architecture, I/O economics, HA, plus PostgreSQL migration paths (logical replication and LSN pitfalls).
Technical comparison of Aurora Serverless v2 vs Provisioned. ACU pricing, cold start behavior, scaling, and production readiness.
Talk to our AWS experts about how we can help transform your business.
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