Apache Paimon vs Databricks Lakebase

Apache Paimon is a lake format for realtime lakehouse architectures, supporting streaming and batch operations with Flink and Spark. Primary-key tables handle large-scale streaming updates through configurable merge engines, including deduplication, partial updates, aggregation, and first-row updates. Append tables support batch and streaming processing, small-file merging, and compaction with Z-order sorting. Paimon also provides ACID transactions, time travel, schema evolution, and metadata for petabyte-scale datasets and many partitions. Listed integrations include Flink, Spark, Hive, Trino, Presto, StarRocks, and Doris, though supported operations vary by engine and version. Documented CDC sources include MySQL, PostgreSQL, Kafka, MongoDB, and Pulsar. Other capabilities include blob and vector storage, full-text search, and global indexing. PyPaimon provides catalog, table, Arrow, and pandas APIs plus a command-line tool; core reads and writes do not require a JVM or a running Flink or Spark cluster. It is free at free, with users choosing connectors and storage plugins for their environment.

Databricks Lakebase has no write-up on Specifiction yet; its figures are above.