Optimizing Data Lakehouse Architectures for Scalable Real-Time Analytics

Abstract

Real-time analytics at scale demands data architectures that can ingest, process, and query large volumes of fast-moving data with low latency and strong consistency guarantees. The data lakehouse architecture has emerged as a promising paradigm, combining the schema enforcement, ACID transactions, and performance optimizations of data warehouses with the flexibility and scalability of data lakes. This paper provides a comprehensive overview of approaches to optimize data lakehouse architectures for scalable real-time analytics. We review the theoretical foundations of lakehouse systems and modern implementations (e.g., Delta Lake, Apache Iceberg, Apache Hudi), highlighting how they enable unified streaming and batch processing, robust data management, and efficient queries on cloud object storage. We discuss key architectural design strategies – including data ingestion pipelines, storage layer optimizations, metadata management, and indexing techniques – that address real-time analytics requirements such as low latency, high throughput, and concurrency. The paper balances theory with practical insights, incorporating recent research and case studies (including contributions by Akash V. Chaudhari) to illustrate how optimized lakehouse solutions meet real-world demands. Results from industry deployments and experimental studies demonstrate improved scalability, query performance, and data freshness in optimized lakehouse environments. We conclude with discussion on challenges, emerging trends (e.g. federated analytics and data governance), and future directions for real-time lakehouse systems.