How to Build a Cloud Data Analytics Platform on AWS (Architecture for Beginners-Intermediates)
Build a Cloud Data Analytics Platform on AWS (Step-by-Step Architecture for Beginners)
In this hands-on walkthrough, I build a real-world cloud data pipeline on AWS from scratch—perfect for beginner to intermediate data architects and data engineers. We cover ingestion from multiple sources, lake-zone organization (Bronze/Silver/Gold), transformations, orchestration, governance, and fast analytics with Redshift, Athena, and QuickSight.
What you’ll learn
How to ingest data from on-prem Oracle, on-prem CSVs, and vendor SFTP into Amazon S3
How to design Bronze / Silver / Gold zones for reliability and governance
How to transform and catalog data with AWS Glue
How to serve analytics using Amazon Redshift, Amazon Athena, and Amazon QuickSight
How to automate, monitor, and secure the entire stack
Chapters (timestamps)
00:00 Intro & Goals
00:58 Agenda
01:52 Scenario
03:15 Ingestion (Oracle → DMS → S3)
05:55 Ingestion (CSV → DataSync → S3)
07:08 Ingestion (Vendor → Transfer Family → S3)
09:01 Storage Zones (Bronze / Silver / Gold)
12:20 Transformations with AWS Glue
14:08 Serving Layer (Redshift, Athena, QuickSight)
16:10 Orchestration & Scheduling (Workflows, EventBridge, Lambda)
17:43 Monitoring & Ops (CloudWatch, SNS, CloudTrail)
23:04 Security & Governance (KMS, IAM, Lake Formation)
27:13 Wrap-Up & Next Steps
Services used (AWS)
Ingestion:
AWS Database Migration Service (DMS) – full load + CDC from Oracle
AWS DataSync – incremental file transfers from on-prem CSV shares
AWS Transfer Family (SFTP) – vendor SFTP landing to S3
Storage & Metadata:
Amazon S3 – data lake (Bronze/Silver/Gold)
AWS Glue Data Catalog – table/partition metadata
(Optional) Apache Iceberg on S3 for ACID/time travel
Transformations & Quality:
AWS Glue ETL (Spark) – clean, conform, curate
AWS Glue Crawlers – schema discovery & drift handling
Amazon CloudWatch – job metrics & logs
(Optional) Great Expectations for data quality
Serving / Analytics:
Amazon Redshift (+ Redshift Spectrum) – warehouse & S3 query
Amazon Athena – serverless SQL over S3
Amazon QuickSight – dashboards & SPICE
Orchestration & Events:
AWS Glue Workflows – pipeline orchestration
Amazon EventBridge – schedules & S3 events
AWS Lambda – COPY refreshes, post-processing
(Alternative) AWS Step Functions for complex branching
Monitoring & Ops:
Amazon CloudWatch – metrics, logs, dashboards
Amazon SNS – alerting
AWS CloudTrail – API audit
AWS Config – compliance & drift
Security & Governance:
AWS KMS – SSE-KMS encryption at rest
AWS IAM – least-privilege roles & policies
AWS Lake Formation – table/column/row-level permissions
AWS Secrets Manager – secure credentials
Amazon VPC + VPC Endpoints / PrivateLink – private networking
Replicable on other clouds
The same architecture pattern works on Azure (Data Factory, ADLS, Synapse, Purview) and GCP (Storage, Dataflow, BigQuery, Dataplex). Concepts are universal—only the tooling changes.
Contact
LinkedIn: https://www.linkedin.com/in/moe-b-aa88a6153/
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