Comparative Analysis of Apache Hudi, Apache CarbonData, and Delta Lake for Data Lake Solutions

Background : Modern data lakes must handle mutable, time‑varying, and incremental data while supporting ACID transactions. Traditional HDFS and object storage lack built‑in transaction support, prompting the development of specialized storage layers that embed transactional semantics in file formats or metadata.

Apache Hudi : Developed by Uber, Hudi (Hadoop Upserts Deletes and Incrementals) focuses on upserts, deletes, and incremental processing. It organizes tables as partitioned directories, uses a HoodieKey for indexing, and offers three table types (copy‑on‑write and merge‑on‑read) with snapshot, incremental, and read‑optimized query modes. Key tools include DeltaStreamer, Spark Datasource API, HiveSyncTool, and HiveIncremental puller.

Apache CarbonData : Originating from Huawei, CarbonData emphasizes high‑performance analytics with columnar storage, multi‑level indexing, and advanced compression. It supports ACID operations without a primary‑key design, integrates tightly with Spark, Hive, Flink, TensorFlow, PyTorch, and Presto, and provides features such as materialized views, secondary indexes, and geo‑spatial queries.

Delta Lake : An open‑source project from Databricks, Delta Lake adds ACID transactions to data lakes via a transaction log, supports upserts/merges, and unifies batch and streaming workloads. All data is stored in Apache Parquet, enabling efficient compression. It offers schema enforcement, time‑travel, and a unified sink for both streaming and batch processing.

Final Comparison : Hudi excels in IUD performance and merge‑on‑read queries but is less suited for pure streaming use cases. Delta Lake’s strong Spark integration and unified batch‑stream architecture make it attractive for Lambda‑style pipelines. CarbonData provides the earliest feature set with advanced indexing and AI engine integration. The choice depends on specific workload requirements, ecosystem preferences, and community support.