esProc SPL is an open-source and JVM-based analyzing and computing engine for structured data and semi-structured data. To be specific, SPL allows you to write simply, and makes the running faster; SPL can be used either independently or embedded in applications. esProc SPL is mainly applied in scenarios such as replacing data warehouse to do off-line batch job and on-line query, serving as middleware for data-driven micro-service, replacing stored procedures, simplifying report calculation, mixed computing in real time, computation on files. esProc SPL provides a simple and easy-to-use development environment with complete debugging functions, enabling you to code step by step, and view the running result of each step in real time. SPL is a specially designed syntax system, and naturally supports step-wise calculation, and complex procedural calculation in particular. SPL has built-in rich data computing library, including the string, date and time, mathematical calculations, file and database read/write, JSON/XML multi-layer data parsing, and AI modeling and prediction. esProc SPL has very high integration, and can run independently, or be seamlessly integrated in applications to serve as an in-application computing engine to play an important role in scenarios such as micro-service, edge computing, and report data preparation. esProc SPL supports diverse data sources, including dozens of data sources like MongoDB, Elasticsearch, Hbase, HDFS and Influxdb. Such data can be calculated directly or in a mixed way without loading them into database. In addition, SPL provides its own efficient data file storage, these private data formats not only make performance higher, but allow you to store data based on business category in file system tree directory. In addition to off-line batch job and on-line query, esProc SPL also supports more application scenarios: data-driven micro-service, replacing stored procedures, eliminating intermediate tables from databases, handling endless report development requirements, programmable data routing to implement front-end calculation, mixed computation to implement real-time HTAP, and performing computation on files to implement Lakehouse.