Start from a real architecture instead of a blank canvas. Every template opens as an editable diagram — real nodes, groups and edges, not a picture.
Template architecture for a modern data platform featuring diverse data sources, batch/stream ingestion, multi-zone data lake (landing, raw, processed, curated), transformation/orchestration, governance, and analytics & ML consumption.
A left-to-right production lakehouse flow from Azure and SaaS sources through ingestion, medallion layers, and Databricks SQL consumption.
End-to-end CDC-driven ETL architecture using Debezium to capture changes from PostgreSQL, MySQL, and MongoDB, streaming via Kafka into Snowflake with both real-time micro-batch ingestion and curated batch processing paths.
A multi-cloud data platform spanning AWS, Azure, and GCP with centralized orchestration, unified governance, and a conformed dimensional warehouse for analytics and downstream consumption.
Event-driven streaming architecture from operational producers through Kafka and Flink into real-time serving and analytics.
A shared Feast feature store supports point-in-time-correct training and low-latency fraud inference.
End-to-end telemetry flow from factory equipment through AWS IoT ingestion to storage, real-time anomaly detection, and operations analytics.
Quality checks read the existing warehouse pipeline, persist results, and drive operational response and impact analysis.
Customer data flows from channel touchpoints through identity resolution into unified profiles and activation destinations.
Domain-oriented data mesh with 4 domain data products and a central data marketplace.
Managed extraction lands operational and marketing data in Snowflake RAW, dbt builds governed staging and mart models, and BI, reverse ETL, and notebooks consume the marts.
A star-schema entity model centered on retail sales and inventory facts, with shared dimensions and three BI consumers.