0 views collected around this technical thread.
This article explains the motivation for an SQL parser in big‑data ecosystems, describes lexical, syntactic and semantic analysis, compares ANTLR and Apache Calcite as parser solutions, and provides complete code examples and deployment steps for building a functional SQL parsing engine.
The article explains why adding a SQL parser to big‑data platforms such as Hive, Spark, Flink or Kafka simplifies development, compares ANTLR and Apache Calcite implementations, shows code examples, and concludes that Calcite’s lower learning curve and greater flexibility make it the preferred choice for production‑grade SQL layers.
The article describes how fragmented real‑time, batch, and online data‑warehouse pipelines suffer from low productivity and inconsistent data quality, and introduces a unified SQL engine built on Apache Calcite that parses, optimizes, and compiles a single SQL statement into executable plans for ODPS, Flink, or Java, leveraging Janino code generation, multi‑backend state storage, and snapshot‑join semantics to boost performance and simplify development.