Tag: Got SQL

It is common that a contemporary information system has multiple or diverse data sources, especially an analytic application, which often needs to perform data analysis on several sources, sometimes of different types, through mixed computation. There are a variety of data sources, including RDB, NoSQL, files like CSV and Excel, JSON, XML, HDFS, Elasticsearch, Kafka, etc. Implementing a mixed computation on heterogeneous sources is not easy because they vary greatly in their computing ability. RDBs boast powerful ability on which computations can rely on, while NoSQL databases have rather weak computing ability and files do not have any. It is

What kind of code is considered low？ The term “Low-Code” has become popular in the past years, springing into existence a number of corresponding entrepreneurial teams. We only knew there were long codes and short codes before, only now do we know there are still high and low codes. Intuitively, the so-called low code is to make the code easier to write, in other words, the amount of code (which can be understood as workload) is less when doing the same task; In addition, another important indicator of low code is that the requirements for developers should be low enough.

As microservices get popular, there are and will be more and more applications developed based on microservices framework. To accomplish data processing tasks within microservices conveniently and efficiently thus becomes an inevitable issue. For a conventional monolithic application, we can implement computations in the database using the specialized, set-oriented SQL. The database’s powerful computing abilities enable quite easy data processing. In the microservices frameworks, a database is mainly used to realize data persistence and data is processed at the application (server) side. Hardcoding in a high-level language (mainly in Java) is the main approach used to process data at the server

BIRT is a popular open-source reporting tool. It is convenient for report development with its independent IDE and wizard and rich functionalities. But it has difficulty in handling reports with complex and special formats. The major reason for this is its weak data preparation ability. BIRT scripted data set is not nearly as good as the specialized data processing engine. Often hardcoding is needed for developing complex reports, making the process very complicated. The open-source esProc SPL can make a professional and excellent partner. SPL is a specialized structured data computing engine. It offers rich class libraries, all-around, database-independent computational

Whether reporting, querying or analyzing, it always involves the data preparation. Data preparation is to process the raw data into a form that can be presented or analyzed (such as the common report dataset). However, data preparation is often not easy and may face various complex situations. Challenges in data preparation Diverse data sources The data of business comes from diverse sources at present. Theoretically, any data source can be used as the source of report data at the same time, such as RDB, NoSQL, CSV, Excel, Json, Hadoop, etc., which leads to the problem of diverse data sources in report development.

Reporting tools are now mostly used for building reports within applications. A mature reporting tool provides a lot of visualization configurations, a wealth of chart types, and various other functionalities, including export and print, making report development convenient and simple. In real-world situations, however, there is a certain thorny problem at a deep level during the report development process that bothers even reporting tool veterans much. Why do reporting tools have these problems? It appears that report development is to present data as a table or chart (graph) in a specified layout, which is reporting tools good at. Yet, the original

Some class libraries, such as sf.json\Gson\JsonPath, can parse JSON, but most of them have done little to further it. A few can handle the simplest conditional queries. Hardcoding is the only choice for handling almost all common computations if users choose to use them. Embedded databases have more computing abilities. Their complicated loading process, however, makes them only suited to tasks having very low real-time requirements. The power of SQL, designed based on two-dimensional structured data rather than intended for the multilevel JSON, is bound to be greatly reduced if it is used to compute JSON. There is the open-source

Business logic usually involves complicated procedures and computations and database writing and reading. Stored procedures are not suitable for handling many scenarios due to various reasons, including complex privilege granting, potential security risks, code un-migratability, high technological threshold, programming difficulty, and so on. Being not good at handling complex procedures, SQL is not fit for implementing business logic alone. It has to work with a high-level language like Java to get the job done. Yet, SQL and high-level languages have disparate syntax styles and data structures. Their cooperation is hard, and development efficiency has been low. In view of this,

MongoDB is a typical NoSQL database. Its document-oriented structure makes both storage and access convenient and efficient. But the database has rather weak computing ability. Computations on MongoDB data, particularly complex ones, are hard to handle. A data computing engine having powerful computing capability is needed to work with MongoDB to achieve relevant computing tasks. The open-source esProc SPL is a specialized structured data computation engine. It supplies rich class libraries and all-around, database-independent computational capabilities. SPL has an independent procedural syntax that is particularly good at handling complex computations. It can help MongoDB increase its ability to compute, accomplish

Both XML and JSON are structured text formats good at recording hierarchical data that carries general and rich information. They are often used to interchange data and transmit information in systems like WebService/RESTful and Microservices. The hierarchical data has much more complicated structure than the traditional two-dimensional tables, and are difficult to process after being retrieved. In the days when there were no specialized JSON/XML processing technologies, JAVA developers usually turned to hardcoding or loaded JSON/XML data to the database for further processing. Hardcoding has weak computing ability, produces too much code and has very slow development efficiency. Loading data