MrSuiChuan/data-warehouse-learning
Data Warehouse Learning
Data warehouse learning is a reference implementation of a real-time stream processing system and open-source data lakehouse architecture. It combines stream processing engines, open lakehouse formats, and analytical data warehouses into a complete e-commerce data warehouse system built for both offline and real-time analytics pipelines.
The project implements hybrid data warehouse architectures utilizing multi-layer storage models and stream-batch processing pipelines. It features change data capture pipelines that stream database transaction logs into messaging systems, progressive data transformation scripts that clean, enrich, and aggregate information across operational and dimensional layers, and directed acyclic graph scheduling to orchestrate dependent data tasks across distributed clusters.
Additional capabilities cover multi-source data ingestion, open table format integration, and semantic metric layers connected to visualization dashboards for real-time reporting.
Features
- Streaming Data Lakehouses - Combines open table formats like Paimon, Hudi, and Iceberg with cloud storage for batch and streaming workloads.
- Data Pipelines and ETL - Synchronizes operational databases and external feeds into unified staging environments using integration tools.
- Unified Batch and Stream Processing Engines - Combines continuous event streaming engines with scheduled batch jobs to power low-latency and historical analytics.
- Stream Processing Systems - Ingests operational data and CDC logs into distributed lake storage tables via a stream-batch processing platform.
- SQL Data Loading and Transformation - Processes multi-stage data models through structured SQL scripts that clean, enrich, and aggregate warehouse information.
- Data Warehousing - Builds low-latency analytics systems using Apache Flink and Apache Doris to process streaming e-commerce events.
- Lakehouse Table Formats - Combines immutable file formats with distributed object stores to support ACID transactions and analytical queries.
- Apache Flink Connectors - Implements an e-commerce data warehouse system built with Flink, Paimon, and Doris for real-time analytics.
- Change Data Capture - Streams database transaction logs into messaging systems to propagate real-time row-level updates.
- Directed Acyclic Graph Pipelines - Orchestrates dependent data transformation tasks and monitors pipeline execution order across distributed clusters.
- BI Semantic Layers - Exposes pre-aggregated business dimensions and measures directly to visualization dashboards for real-time reporting.
- Big Data Workflow Management - Schedules and monitors complex multi-stage data transformation dependencies across distributed computing clusters.
- Data Storage Architectures - Utilizes multi-layer storage models and stream-batch processing pipelines for dual-architecture e-commerce analytics.
- Data Layer Architects - Progressively transforms raw ingestion data through operational, detailed, and dimensional layers.
- Multi-Source Ingestion - Ingests raw event streams and operational records from multiple disparate sources into initial staging layers.
- Stream Transformations - Applies progressive transformations across staging, dimensional, and summary layers using stream processing queries.
- Warehouse Reporting - Integrates dashboard and reporting tools connected to aggregated service layers to display business insights.
- Change Data Capture Streams - Captures change logs and message streams from various sources into distributed lake storage tables.
