Building a Lakehouse Architecture
Customers can build multi-engine applications atop a single source of truth within TCLake's unified storage. This robust foundation supports diverse workloads, including Spark-driven batch processing, Flink-powered real-time pipelines, TCHouse-backed high-performance analytics, and SparkML-based machine learning. This unified approach effectively eliminates the data silos inherent in traditional architectures that separate offline, real-time, and interactive analytics. Furthermore, by consolidating Lakehouse assets via a unified metadata layer and delivering intelligent optimization and acceleration services, TCLake significantly boosts data maintenance and utilization efficiency.
Multimodal Data Lake
Enterprises can seamlessly ingest both structured and unstructured data into the lake. Through TCLake's unified data catalog, organizations can integrate disparate multimodal data from heterogeneous systems with native TCLake assets. This centralized management delivers a globally visible asset governance console for administrators. For upper-layer applications, it provides standardized pan-domain data access, Unified Access Control, and full-lifecycle governance. Ultimately, this architecture shatters data silos, minimizes data movement, and drastically improves overall data management efficiency.
Big Data & Machine Learning Integration
Leveraging TCLake's multimodal data capabilities and open engine ecosystem, customers can rapidly construct integrated Data+AI pipelines. Training datasets preprocessed by upstream big data engines (e.g., Apache Spark) can be directly registered into the unified metadata layer. Downstream AI training frameworks, such as PyTorch and TensorFlow, can then natively read this data. Once training is complete, the resulting model artifacts can be registered back into TCLake for unified lifecycle management, dramatically accelerating the end-to-end development and governance of AI applications.