Extend the Reach of Data Science with Data Virtualization
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Data virtualization allows for faster and more extensive data science projects by enabling business analysts to integrate disparate data sources through a virtual database without needing programming skills. This virtual environment improves data accessibility, reduces complexity of data integration, and speeds up projects through modular reusable views and faster data retrieval times compared to traditional data warehousing. Data virtualization is advantageous for data science involving machine learning by facilitating materialization of training data and access to real-time data.
Extend the Reach of Data Science with Data Virtualization
- 2. Extend the Reach of Data Science with Data Virtualization John O'Brien Principal Advisor and CEO , Radiant Advisors
- 3. Copyright 2019. Radiant Advisors All Rights Reserved. DATA SCIENCE PROJECT CHARACTERISTICS 3 Extend the Reach of Data Science with Data Virtualization Bulk of work in data science projects involves integrating many disparate data sets to create extremely wide data Data science data requires as many data sets as possible to be integrated in such a way that the business context aligns with the goals of the project Data-savvy business analysts are knowledgeable with business systems’ data and SQL but are not programmers
- 4. Copyright 2019. Radiant Advisors All Rights Reserved. BUSINESS SEMANTIC LAYER FOR DATA SCIENCE 4 Extend the Reach of Data Science with Data Virtualization Dedicate a virtual database to business analysts and data scientists for building integrations that can be assembled into broader business contexts Make more heterogeneous data available to data science projects without data acquisition and storage Data virtualization architecture needs to account for how to assemble wider integrated data sets that are built upon subsets for data science usage
- 5. Copyright 2019. Radiant Advisors All Rights Reserved. ADVANTAGE OF A SQL QUERY ENGINE 5 Extend the Reach of Data Science with Data Virtualization Fast data retrieval times come from the SQL engine in data virtualization that generates optimized query execution plans for the underlying databases SQL-based joins and unions can break down complex integrated views into subsets of views that are easily created, verified, and reused Caching data allows faster data science when iteratively working with the same data set
- 6. Copyright 2019. Radiant Advisors All Rights Reserved. FASTER DATA SCIENCE FROM REUSABILITY 6 Extend the Reach of Data Science with Data Virtualization Data science projects are faster when they move from complex one-time integrations to modularity for reuse Data prep can be organized as a series of views upon views that build a data set for data science input Views that are created as basic modules of data integration can be assembled into complex, integrated, cross-subject-area data sets for data science
- 7. Copyright 2019. Radiant Advisors All Rights Reserved. FASTER DATA SCIENCE FROM DATA REFRESHES 7 Extend the Reach of Data Science with Data Virtualization Machine learning model training, supervised reinforcement, and unsupervised techniques ▪ Materialize training data from a virtual table that stores its results in another database for machine learning supervised training ▪ Access real-time data from a virtual table for the latest data to be used in machine learning reinforcement training ▪ Cache data sets to alleviate performance bottlenecks
- 8. Copyright 2019. Radiant Advisors All Rights Reserved. SUMMARY 8 Extend the Reach of Data Science with Data Virtualization Data science projects that leverage data virtualization benefit from having access to all heterogeneous data accessible in the enterprise A data science-oriented virtual data architecture will accelerate development and increase quality by reducing the complexity of data science integrations Business analysts can work in a data virtualization environment to contribute to data science projects
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