Unified Data Foundation for AI & Analytics

1. Challenges from Data Silos

[Placeholder: Slide - Deriving insights from data can be challenging]

Organizations often hit blockers because data is split across isolated silos:

  • Data silos: data lakes and data warehouses run independently and are hard to blend.
  • People silos: data lives inside different functions with little collaboration.
  • Business silos: compliance, cost, and governance constraints slow sharing.

-> Insights get fragmented, innovation slows, and AI adoption stalls.

Data Lake + DW   ->   Data silo
Data Owners      ->   People silo
Compliance/Cost  ->   Business silo

2. Evolution of Data Requirements

[Placeholder: Slide - Ever evolving data requirements]

Traditional

  • Focused on structured and semi-structured data.
  • Driven by batch pipelines.
  • Stored in operational and analytical systems.
  • Governance centered on tables, files, and dashboards.

Modern

  • Augments structured data with unstructured sources (documents, media, knowledge).
  • Vector databases become core for AI workloads.
  • Self-service governance: policies extend to AI prompts and responses.
  • Stream-first pipelines replace legacy batch to power real-time AI and analytics.
Traditional -> Structured data, batch, centralized governance
Modern     -> Vector DB, mixed data, stream-first

-> Data now must be flexible, diverse, and AI-ready.

3. End-to-End Data Strategy

[Placeholder: Slide - End-to-end data strategy]

A complete end-to-end data strategy includes:

  • Producers: create and share data, choose the right tools, embrace self-service.
  • Foundations: standardize governance, auditing, discovery, and permissions.
  • Consumers: multi-persona, self-service, easy access across many use cases.

-> Shift the mindset from a single-purpose stack -> a unified shared platform.

[ Producers ] -> [ Foundations ] -> [ Consumers ]
Build/Share     Catalog/Govern     Use cases, BI, AI

4. Metadata at the Center

[Placeholder: Slide - Metadata centric pipeline]

When metadata sits at the heart of the data ecosystem:

  • Data sources (structured + unstructured) get standardized and enriched.

  • Metadata unlocks: find, share, understand, access, act, and monitor.

  • This powers analytics and AI such as:

    • GenAI / RAG.
    • Forecasting and classification.
    • BI and decision intelligence.
    • Graph analytics and anomaly detection.
Data Sources -> Prep/Enrich -> Metadata -> Analytics/AI

-> Metadata is no longer just a catalog; it becomes the orchestration layer for AI and analytics.

5. Relational & Purpose-built Databases

[Placeholder: Slide - Complete set of relational and purpose-built databases]

AWS offers purpose-built databases for every workload:

  • Relational: RDS, Aurora (traditional OLTP and OLAP).
  • Key-Value: DynamoDB.
  • Document: DocumentDB.
  • Graph: Neptune.
  • Caching: ElastiCache.
  • Time-series: Timestream.
  • Ledger: QLDB.
  • Wide column: Keyspaces.
  • Memory: MemoryDB for Redis.

-> Embrace polyglot persistence instead of one-size-fits-all.

6. Why one-size-fits-all does not work

[Placeholder: Slide - Developers want the right datastores]

A single relational database cannot cover every demand:

  • Performance issues as datasets grow.
  • Lack of scalability for surging workloads.
  • Lack of flexibility: developers are boxed in.
  • Cannot integrate every data type (images, vectors, graphs, etc.).

-> Developers need datastores matched to the unique traits of each application.

Performance low   Scalability low
Flexibility low   Integration low

[Placeholder: Slide - Enabling vector search across services]

AWS has woven Vector Search across multiple services:

  • OpenSearch Service, OpenSearch Serverless.
  • Aurora PostgreSQL, RDS for PostgreSQL.
  • DocumentDB, DynamoDB (zero-ETL).
  • MemoryDB, Neptune.

-> Vector search is the AI-native infrastructure layer that powers GenAI and RAG.

OpenSearch + AuroraPG + RDS -> Vector Search
DynamoDB + MemoryDB + Neptune -> Vector Search

8. AWS Analytics Stack

[Placeholder: Slide - Comprehensive set of analytics services]

The AWS end-to-end analytics toolkit:

  • Amazon Athena: interactive, serverless querying.
  • Amazon EMR: large-scale data processing.
  • Amazon Kinesis / MSK / Flink: real-time analytics.
  • Amazon Redshift: data warehousing with serverless options.
  • AWS Glue: data integration and ETL.
  • Amazon QuickSight: business intelligence.
  • Amazon OpenSearch: operational analytics.

-> Covers the journey from ingest -> transform -> analyze -> visualize.

9. Key Insights for the Notion materials

  • Data silos are the top blocker for AI adoption -> build a unified data foundation.
  • Metadata plus vector databases unlock GenAI and RAG experiences.
  • AWS supplies the full stack: diverse databases + analytics services + governance.
  • Organizations should practice polyglot persistence to match datastore to workload.

-> This is how AWS delivers modern data infrastructure for AI and analytics workloads.