connecting views, data entities, dimensions and datasets through relationships, allowing unprecedented insight into data pipeline infrastructure.simplifying updates to definitions by automatically populating the changes to all downstream entities.hiding away the complexity of underlying data systems, making them easily detachable for specific cloud vendors or data engines.reducing or even removing any learning curve for data operators, as they no longer need to understand the underlying logic behind their queries.The common definition model offers several benefits to data operators: This standardization aligns the meaning of your data across every stakeholder - from business operators to data scientists. This makes your data accessible and functional for internal consumers, without requiring them to understand the SQL expressions used or specific domain knowledge. Through this common definition model, business terms are mapped to the underlying data. Single Origin's common definition model is composed of views, dimensions, and data entities. Now, we’ll take a look at each of the three parts in detail. In practice, the common definition model abstracts away the calculation logic and domain knowledge rule, leaving a simple and familiar interface for users. In Single Origin, data semantics consist of three parts: definition model, SQL-based calculation logic, and domain knowledge rule. Single Origin uses the open source Calcite to parse, process and relate SQL queries. In addition, it is expressive and extensible, and its semantics can be formulated as relational algebra, making comparisons between different SQL statements possible. SQL was an easy choice, as it is widely used in data analysis and supported by every cloud vendor/data processing engine. Single Origin relies on SQL expressions to define and communicate the meaning of data. This alignment of meaning across an organization enriches SQL queries for both humans (e.g., business operations) and machines (e.g., machine learning features). One way semantic management achieves this is through embedding definitions and meaning into data pipelines and workflows. Semantic management is the key to simplifying your data stack, improving collaboration across company verticals and generating higher quality insights from your data, faster. Semantic management, then, is the collecting, refining, governing and consuming of data definitions within an organization. One way to imbue this meaning is through data definitions. In the context of this article, data semantics are all about the meaning of data, like what it is and how to use it, under different business contexts. Then we’ll examine why adopting a semantic management tool is crucial for data-intensive organizations to efficiently scale and collaborate. Given that semantic management is a relatively new field, let's begin with a quick definition. Then, we’ll look at some of the novel features Single Origin offers through its unique approach to semantic management. Today, we will look at how Single Origin defines and implements a semantic management system. In this series, we've explained what problems Single Origin solves and demonstrated how SQL queries are deconstructed into a common definition model, allowing them to be reconstructed into reusable components. Welcome back to our three-part series on the Single Origin platform.
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