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Integrating Data
Overcome the challenges, appreciate the varieties, and apply the process of data integration.
Learn all about data integration and become a data integration hero instead of following the masses and running in the opposite direction at the mere mention of the word “integration”. Understand why organizations avoid data integration and often wind up with spider web environments containing siloed applications instead of an enterprise database which excites analysts and data scientists. Distinguish the different types of database, attribute, key, index, encoding, measurement, format, definition, KPI, calculations, summarization, selection criteria, data exclusion, lineage, and timing. Apply identification, equivocation, and physical conversion levels of integration for both structured and textual data. Leverage deidentification, proximity analysis, alternate spelling, stop word resolution, homographic resolution, stemming, taxonomical resolution, inline contextualization, classification, and acronym resolution. Learn how to combine structured and textual data in the context of three levels of interaction. Follow the steps of scope, model, and map in integrating structured data. Follow the steps of scope, connect taxonomies, ingest raw text, and determine analytical processes in integrating textual data. Apply integration best practices, including identifying integration roles, developing a reusable data integration process, and documenting the integration benefits. Compare taxonomies with data models. Know how data integration helps data science.
To reinforce all of the concepts within the book, we include a detailed case study on data integration.
Learn all about data integration and become a data integration hero instead of following the masses and running in the opposite direction at the mere mention of the word “integration”. Understand why organizations avoid data integration and often wind up with spider web environments containing siloed applications instead of an enterprise database which excites analysts and data scientists. Distinguish the different types of database, attribute, key, index, encoding, measurement, format, definition, KPI, calculations, summarization, selection criteria, data exclusion, lineage, and timing. Apply identification, equivocation, and physical conversion levels of integration for both structured and textual data. Leverage deidentification, proximity analysis, alternate spelling, stop word resolution, homographic resolution, stemming, taxonomical resolution, inline contextualization, classification, and acronym resolution. Learn how to combine structured and textual data in the context of three levels of interaction. Follow the steps of scope, model, and map in integrating structured data. Follow the steps of scope, connect taxonomies, ingest raw text, and determine analytical processes in integrating textual data. Apply integration best practices, including identifying integration roles, developing a reusable data integration process, and documenting the integration benefits. Compare taxonomies with data models. Know how data integration helps data science.
To reinforce all of the concepts within the book, we include a detailed case study on data integration.
136 pages, Kindle Edition
Published August 28, 2022
7 people want to read
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