Generate AWS Data Platform Diagrams from Text with AI
Describe your S3 data lake zones, Glue ETL pipelines, Redshift clusters, and Kinesis streams in plain English. Get a valid Draw.io diagram with official AWS icons.
This AWS data platform diagram generator converts plain-text descriptions of your analytics infrastructure into Draw.io diagrams with correct data flow paths, zone boundaries, and service connections. Describe a setup like 'S3 data lake with raw, curated, and analytics zones. Kinesis Data Streams ingesting clickstream events at 10,000 records/second, Kinesis Data Firehose delivering to S3 raw zone in Parquet format with Snappy compression. AWS Glue crawlers cataloging raw zone tables, Glue ETL jobs transforming to curated zone, Redshift Spectrum querying curated data.' The AI maps each service to its official icon, draws data flow arrows with format annotations, and groups resources by lake zone. Architecture warnings flag missing data encryption (WARN-04) and single-AZ Redshift clusters (WARN-01). Every element snaps to a 10px grid. Output is native .drawio XML.
What Is an AWS Data Platform Diagram?
An AWS data platform diagram maps the end-to-end flow of data from ingestion through transformation to consumption. It shows how raw data lands in S3, gets cataloged by AWS Glue Data Catalog, transforms through ETL jobs or EMR Spark clusters, and reaches analysts through Redshift, Athena, or QuickSight. Drawing this manually means placing dozens of services, routing data flow arrows between them, annotating data formats at each stage, and showing the permission boundaries that Lake Formation enforces. Diagrams.so automates that entire workflow. Describe your pipeline in plain English and the AI identifies ingestion services (Kinesis Data Streams, Kinesis Data Firehose, AWS Database Migration Service), storage zones (S3 raw, curated, analytics buckets), transformation engines (Glue ETL, EMR, Glue DataBrew), orchestration layers (MWAA running Apache Airflow, Step Functions), query engines (Athena, Redshift, Redshift Spectrum), and consumption tools (QuickSight dashboards, SageMaker notebooks). RULE-02 enforces official AWS icons for every service. RULE-06 groups related components: Kinesis Data Streams with its Firehose delivery stream, Glue crawlers with the Data Catalog they populate, Redshift clusters with their Spectrum layer. Opinionated mode enforces left-to-right data flow from sources through transformation to consumption, following the medallion architecture pattern. Architecture warning WARN-01 flags single-AZ Redshift clusters without Multi-AZ or RA3 node failover. WARN-04 catches S3 buckets without server-side encryption or Lake Formation permission grants missing column-level security. WARN-03 identifies Redshift clusters without automated snapshots or cross-region snapshot copy. VLM visual validation detects overlapping data flow arrows on complex pipelines with many stages.
Key components
- S3 data lake with clearly labeled zones: raw (landing), curated (transformed), and analytics (aggregated) buckets with lifecycle policies
- Kinesis Data Streams for real-time ingestion with shard count annotations and Kinesis Data Firehose delivery to S3 with Parquet conversion
- AWS Glue Data Catalog as the central metadata store with crawlers scanning each zone and table versioning enabled
- AWS Glue ETL jobs with PySpark or Spark SQL transformations moving data between zones, annotated with job bookmarks for incremental processing
- Amazon Redshift cluster (RA3 nodes) with Spectrum external schema pointing to curated S3 zone for federated queries
- Amazon Athena for ad-hoc SQL queries against Glue Data Catalog tables with workgroup-level query cost controls
- Lake Formation permission grants showing database-level and column-level access control replacing raw S3 bucket policies
- MWAA (Managed Airflow) or Step Functions orchestrating the end-to-end pipeline with DAG or state machine annotations
How to generate with AI
- 1
Describe your data platform architecture
Write your data pipeline in plain English, specifying services and data flow direction. For example: 'Clickstream events from web application to Kinesis Data Streams (4 shards). Kinesis Data Firehose consumes from the stream, converts to Parquet with Snappy compression, and delivers to S3 raw zone (s3://datalake-raw/) partitioned by year/month/day. AWS Glue crawler runs hourly on raw zone, populates Data Catalog. Glue ETL job reads raw tables, deduplicates, applies schema validation, writes to S3 curated zone (s3://datalake-curated/) in Parquet. Redshift Spectrum external schema points to curated zone. QuickSight connects to Redshift for dashboards. Lake Formation grants analytics team read access to curated zone only. MWAA orchestrates the full pipeline with a daily DAG.'
- 2
Select data pipeline type and AWS provider
Choose 'Data Pipeline' as the diagram type and 'AWS' as the cloud provider. Diagrams.so loads the official AWS icon set with icons for Kinesis, Glue, Redshift, Athena, Lake Formation, EMR, QuickSight, and S3. Enable opinionated mode to enforce left-to-right data flow from ingestion sources on the left through transformation in the center to consumption on the right.
- 3
Generate and validate
Click generate. The AI produces .drawio XML with S3 zone boundaries, service icons connected by data flow arrows labeled with formats (Parquet, JSON, CSV) and throughput annotations. Architecture warnings flag single-AZ Redshift (WARN-01), unencrypted S3 buckets (WARN-04), and Redshift clusters without snapshot policies (WARN-03). VLM visual validation catches overlapping arrows on complex multi-stage pipelines. Download as .drawio for editing, or export to PNG or SVG for data architecture reviews.
Example prompt
AWS data platform with medallion architecture. Sources: PostgreSQL RDS (transactional data via AWS DMS full load + CDC to S3 raw zone), mobile app events via Kinesis Data Streams (8 shards, 7-day retention) consumed by Kinesis Data Firehose delivering to S3 raw zone in Parquet with Snappy compression partitioned by event_type/year/month/day, third-party CSV files uploaded to S3 raw zone via Transfer Family SFTP endpoint. Storage: S3 data lake with three buckets: datalake-raw (raw zone, SSE-KMS encryption, 90-day lifecycle to Glacier), datalake-curated (curated zone, SSE-KMS, versioning enabled), datalake-analytics (analytics zone, SSE-KMS). Catalog: AWS Glue Data Catalog with crawlers on raw and curated zones running on schedule. Transform: Glue ETL jobs (PySpark) reading raw zone, deduplicating, validating schemas, writing to curated zone with job bookmarks for incremental processing. EMR Spark cluster (r5.2xlarge, 1 primary + 4 core nodes) for complex ML feature engineering writing to analytics zone. Orchestration: MWAA (Apache Airflow 2.7) running daily DAG that triggers DMS tasks, waits for Firehose delivery, runs Glue crawlers, executes Glue ETL, triggers EMR step, and runs data quality checks. Query: Athena workgroup for ad-hoc queries with 10GB scan limit per query. Redshift RA3.xlplus cluster (2 nodes) with Spectrum external schema on curated zone. Consume: QuickSight Enterprise connected to Redshift with row-level security. SageMaker notebook accessing analytics zone via Lake Formation grants. Governance: Lake Formation with database-level grants for engineering team, column-level grants restricting PII columns from analytics team.
Example diagrams from the gallery
AWS Redshift vs Azure Synapse vs GCP BigQuery — Data Platform Architecture
Each cloud provider takes a different approach to the analytical data platform. AWS builds on S3 as the storage layer with Redshift, Glue, and Athena as separate services you compose together. Azure packages compute and storage into Synapse Analytics. GCP offers BigQuery as a serverless warehouse with built-in storage. The composition model affects how you diagram each platform.
| Feature | AWS (Redshift + S3) | Azure (Synapse) | GCP (BigQuery) |
|---|---|---|---|
| Storage layer | S3 as the data lake with zone-based bucket organization; Redshift Managed Storage for warehouse data; Spectrum bridges the two for federated queries | Azure Data Lake Storage Gen2 (ADLS) with hierarchical namespace; Synapse dedicated SQL pools store warehouse data; Synapse serverless queries ADLS directly | BigQuery native storage with automatic compression and encryption; Cloud Storage (GCS) for external tables; BigLake unifies access across both |
| ETL and transformation | AWS Glue ETL (PySpark/Spark SQL) with Data Catalog, Glue DataBrew for visual transforms, EMR for heavy Spark workloads, Glue job bookmarks for incremental loads | Synapse Pipelines (forked from Data Factory) with mapping data flows; Synapse Spark pools for notebook-based transforms; native integration with Synapse workspace | Dataflow (Apache Beam) for streaming and batch ETL; Dataproc for managed Spark; BigQuery SQL-based transforms with scheduled queries and dbt integration |
| Real-time ingestion | Kinesis Data Streams (configurable shards) to Kinesis Data Firehose with Parquet conversion and S3 delivery; sub-minute delivery latency | Event Hubs (Kafka-compatible) with capture to ADLS in Avro; Stream Analytics for windowed SQL processing before landing | Pub/Sub for event ingestion; BigQuery Storage Write API for direct streaming inserts; Dataflow for stream processing before loading |
| Governance and access control | Lake Formation for database-level and column-level grants; replaces complex S3 bucket policies and IAM; tag-based access control for cross-account sharing | Microsoft Purview for data cataloging and classification; Synapse workspace-level RBAC; Azure AD integration for authentication | Dataplex for data governance across zones; BigQuery column-level security and row-level access policies; Data Catalog for metadata |
| Ad-hoc query engine | Athena (serverless Trino/Presto) queries S3 data via Glue Data Catalog; pay per TB scanned; workgroup-level cost controls | Synapse serverless SQL pool queries ADLS directly; pay per TB processed; views can abstract file formats | BigQuery on-demand queries with per-TB pricing or flat-rate reservations; materialized views reduce repeated scan costs |
| Diagram layout pattern | Left-to-right flow: sources > Kinesis/DMS > S3 raw > Glue ETL > S3 curated > Redshift/Athena > QuickSight; zone boundaries around S3 buckets | Left-to-right flow: sources > Event Hubs/Data Factory > ADLS raw > Synapse Spark > ADLS curated > Synapse SQL > Power BI; Synapse workspace as container | Left-to-right flow: sources > Pub/Sub/Dataflow > GCS raw > Dataproc > GCS curated > BigQuery > Looker; fewer service boundaries due to BigQuery consolidation |
When to use this pattern
Use an AWS data platform diagram when designing or documenting your analytics infrastructure. It's the right choice for data lake architecture reviews, ETL pipeline design sessions, and data governance audits. The diagram shows stakeholders how data flows from source systems through ingestion, transformation, and storage zones to reach analysts and ML engineers. If you only need to document a single Glue job or Kinesis stream, an architecture diagram covers that. If your focus is on the real-time event flow without the full lake architecture, a data flow diagram fits better. Data platform diagrams deliver the most value when you're building a medallion architecture with multiple ingestion sources, shared transformation logic, and distinct consumption patterns for BI, ad-hoc SQL, and machine learning workloads.
Frequently asked questions
What does the AWS data platform diagram generator include?
This AWS data platform diagram generator produces S3 data lake zone boundaries, Kinesis ingestion streams, Glue ETL pipelines with Data Catalog integration, Redshift clusters with Spectrum, Athena query layers, Lake Formation permission grants, and orchestration via MWAA or Step Functions. It uses official AWS icons from Diagrams.so's 30+ icon libraries.
How are data lake zones represented in the diagram?
Each S3 zone (raw, curated, analytics) renders as a labeled container with the bucket name and key configuration details like encryption type and lifecycle rules. Data flow arrows between zones show the transformation service (Glue ETL, EMR) and output format (Parquet, ORC). Zone boundaries make the medallion architecture visually explicit.
Can I show both real-time and batch ingestion paths?
Yes. Describe your real-time path (Kinesis Data Streams to Firehose to S3) and batch path (DMS, Transfer Family, or direct S3 upload) in the same prompt. The AI draws parallel ingestion lanes converging at the raw S3 zone. Each path gets distinct arrow labels showing throughput, format, and delivery cadence.
Does the diagram show Lake Formation permissions?
Yes. Describe which teams or roles get access to which databases, tables, or columns. The AI annotates Lake Formation grants as labels on the consumption services, showing database-level or column-level restrictions. WARN-04 flags zones where Lake Formation grants are missing and raw S3 bucket policies are still the access mechanism.
What architecture warnings apply to data platform diagrams?
WARN-01 flags single-AZ Redshift clusters without Multi-AZ or failover configuration. WARN-03 catches Redshift clusters missing automated snapshot schedules or cross-region snapshot copies. WARN-04 detects S3 buckets without server-side encryption enabled. WARN-05 flags ambiguous components like unspecified transformation engines. Warnings are non-blocking annotations.
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