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What is Data Lake

Chapter 6
Fostering Innovation and Competitiveness With FinTech, RegTech, and SupTech
A repository for storing unstructured and structured data that is downloaded in its raw form and stored by a highly scalable, distributed files system known as open source.
Published in Chapter:
Non-Technological and Technological (SupTech) Innovations in Strengthening the Financial Supervision
Marcin Flotyński (Adam Mickiewicz University, Poznań, Poland) and Kamilla Marchewka-Bartkowiak (Poznań University of Economics and Business, Poland)
Copyright: © 2021 |Pages: 31
DOI: 10.4018/978-1-7998-4390-0.ch006
Abstract
The application of non-technological and technological innovations provides support to supervisory institutions in the digitization of reporting and regulatory processes. This chapter deals with international applications of innovations in financial supervision (SupTech). The aim of the chapter is to present the most popular technological and non-technological solutions along with an evaluation of their usefulness in exercising supervision over the financial market. The authors discuss the types of innovations and the reasons for implementing them by supervisory institutions. Furthermore, they describe the most important non-technological supervisory solutions and technologies that supervisors can apply in creating SupTech tools. The study utilizes, among other things, data from reports and elaborations by central banks, supervisory institutions, and consulting companies. The authors' main focus is on the analysis of the solutions utilized by European Union member states.
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Chapter 2
Digital Technology Deployment in Multi-National Enterprises
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Chapter 11
Why Is Data So Hard?: Challenges, Solutions, and Future Directions
A conglomeration of data from many different sources that is easy to use and has all sorts of data in many different formats.
Published in Chapter: Why Is Data So Hard?: Challenges, Solutions, and Future Directions; From: Technology Leadership for Innovation in Higher Education
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Chapter 14
Data Stewards, Curators, and Experts: Library Data Engagement at Samuel J. Wood Library at Weil Cornell Medicine
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Chapter 8
Plan and Rules for Data Analysis Success: A Roadmap
Is a collection of storage instances of various data assets added to the originating data sources. These assets are stored in a near-exact, or even exact, a copy of the source format. The purpose of a data lake is to present an unrefined view of data to only the most highly skilled analysts, to help them explore their data refinement and analysis techniques independent of any of the system-of-record compromises that may exist in a traditional analytic data store (such as a data mart or data warehouse).
Published in Chapter: Plan and Rules for Data Analysis Success: A Roadmap; From: Big Data Analytics for Entrepreneurial Success
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Chapter 4
First of All, Understand Data Analytics Context and Changes
Is a collection of storage instances of various data assets added to the originating data sources. These assets are stored in a near-exact, or even exact, a copy of the source format. The purpose of a data lake is to present an unrefined view of data to only the most highly skilled analysts, to help them explore their data refinement and analysis techniques independent of any of the system-of-record compromises that may exist in a traditional analytic data store (such as a data mart or data warehouse).
Published in Chapter: First of All, Understand Data Analytics Context and Changes; From: Big Data Analytics for Entrepreneurial Success
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Chapter 5
Enterprise Data Lake Management in Business Intelligence and Analytics: Challenges and Research Gaps in Analytics Practices and Integration
A scalable data management platform for data of any structure, which is stored in its raw format to enable different types of analytics without predefined scheme.
Published in Chapter: Enterprise Data Lake Management in Business Intelligence and Analytics: Challenges and Research Gaps in Analytics Practices and Integration; From: Integration Challenges for Analytics, Business Intelligence, and Data Mining
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Chapter 7
Digital Knowledge Transfer for Banks: From a Lean Data Management Perspective
A centralized data repository to store, management, process, and secure all structured and unstructured data at any scale.
Published in Chapter: Digital Knowledge Transfer for Banks: From a Lean Data Management Perspective; From: Harnessing Technology for Knowledge Transfer in Accountancy, Auditing, and Finance
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Chapter 3
Big Data Applications in Business
Is a collection of storage instances of various data assets added to the originating data sources. These assets are stored in a near-exact, or even exact, a copy of the source format. The purpose of a data lake is to present an unrefined view of data to only the most highly skilled analysts, to help them explore their data refinement and analysis techniques independent of any of the system-of-record compromises that may exist in a traditional analytic data store (such as a data mart or data warehouse).
Published in Chapter: Big Data Applications in Business; From: Big Data Analytics for Entrepreneurial Success
Full Text Chapter Download: US $37.50 Add to Cart
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