KeySQL is a structured query language that employs a keyobject (k-object) data model. The keyobjects work as data definitions for the k-object instances, which represent the data itself.
KeySQL comprises the same high-level data manipulation statements SQL does. At the same time, it can handle data of any complexity.
Universal data model founded in math structure of hereditarily-finite sets
Business friendly bottom up data definition
Data objects of any complexity
Structural data transformations
Grouping objects by objects
Organizations collect massive amounts of JSON data, however this data is not available for ad hoc analytics, as analysts lack suitable tools or programming expertise. We show how an analyst with basic SQL skills can be highly productive with JSON data. These examples are based on a sample JSON data set that is popular in the MongoDB world.
MRF Explorer is an online service, that makes healthcare price-transparency data accessible to everyone.
The purpose of MRF Explorer is to provide MRF data as a CSV file that can be viewed and analyzed in Excel. You can narrow the data to specified EINs, NPIs and Billing codes.
For the first time patients and all stakeholders in the medical industry gain access to contractual rates for medical services and drug pricing that until recently was hidden in secret contracts.
MongoDB is a popular system for storing and processing JSON formatted files. It does not presume any logical structure of the data in the files or automatically verify the data consistency. MongoDB makes it easy to load the files but not as easy to query them. It is oriented mostly towards programmers who must know the expected logical structure of the data and deal with the data quality issues with application code. Though MongoDB provides a set of data processing functions, they require a programmer’s mindset and productivity is low compared to SQL. Typically, a page of code must be produced to express a SQL statement of just several lines.
Summarizing, MongoDB facilitates efficient ingestion of JSON, but, unlike SQL, is not suitable for ad hoc querying or analysis of the data.
Rather than dealing with files, KeySQL follows the SQL database paradigm but employs a more general key-object data model allowing for objects of arbitrary complexity. Particularly, the conversion between JSON and key-object instances is easy and straightforward. KeySQL employs the same SELECT, INSERT, UPDATE, and DELETE transactional statements as SQL, but they are applied to key-object stores rather than flat tables. There are several additional statements and options necessary for restructuring, joining, and grouping of complex data which have no analog in standard SQL, as SQL deals exclusively with a simpler table data structure.
Thus, KeySQL provides a universal data platform naturally integrating the processing of all SQL and NoSQL data sources.
KeySQL platform is built for the cloud and supports a variety of client applications.
KeySQL Server is a parallel and scalable high-performance transactional engine supporting k-object data model and written in C++. Its transparent and replaceable data storage is currently formed by industry leading and open-source databases supporting standard SQL. The server can work with several heterogeneous databases at the same time, providing a performant scalable data layer over a plurality of otherwise non-scalable SQL databases. Other types of data storage will be added in the future.
Included in docker image
KeySQL Studio is a web application that runs in all popular browsers. Studio provides an elegant user interface for analyst and administrator interaction with KeySQL Server. Studio’s import function makes for easy loading of JSON/BSON, CSV, and XML data. There is no need to define the data structure before import, or to perform complicated ETL or ELT. Studio can automatically recognize the schema of your JSON/BSON data, including the data type, repeating values, and arrays. Upon import the structure of your data is easily viewed and navigated via the Studio Explorer, a tree-like display of the data structure. Due to the Flexible Schema of the KeySQL Universal Database, import can ingest both flat and non-flat data, including variations in the structure of data, providing flexibility similar to that of document databases. Studio’s export function makes for easy sharing of data. Data can be exported using JSON, native KeySQL format, CSV, or XML. Administration is easy, using an intuitive graphical interface to manage user accounts, check and set permissions, and create new schemas. The user interface for command execution is friendly for data analysts, with features such as automatic capture of command history, pre-built KeySQL snippets, and online help.
Included in docker image
The KeySQL Data Modeling Tool is a state-of-the-art web application for modeling complex, non-flat data. It is useful for:
The Data Modeling Tool can be run stand-alone but works in tandem with KeySQL Studio to deliver additional functionality.
The KeySQL REST API is a standard API that supports HTTP requests for accessing KeySQL Server. A wide range of Business Intelligence tools such as Power BI can directly query KeySQL Server via this API. In addition, this API can be used to rapidly move data between KeySQL Server and databases, data lakes, and proprietary systems.
Included in docker image
Java developers are provided with a Java SDK and driver fully supported by Keyark. Novice developers can compose commands in KeySQL Studio, such as “SELECT Name, Email FROM Contacts”, embed this code into a single Java statement, and fetch the result set, with only a few lines of simple code.
Published in Maven repository
Python developers are provided with a Python SDK and driver fully supported by Keyark. The SDK is similar to the Java SDK, providing both a friendly environment for novice developers as well as a sophisticated class library for more advanced developers.
Windows / Linux / MAC installer
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