Introduction to Data Factory, a data integration service | Microsoft Docs
... (hourly, daily, weekly, etc.). For example, a pipeline may read input data, process data, and produce output data once a day. You can also run a workflow just one time. ...
... (hourly, daily, weekly, etc.). For example, a pipeline may read input data, process data, and produce output data once a day. You can also run a workflow just one time. ...
![[ Team LiB ] "If you are looking for a complete treatment of business](http://s1.studyres.com/store/data/004013275_1-ea1b58daecec37e9ce8071afc42e2da6-300x300.png)
[ Team LiB ] "If you are looking for a complete treatment of business
... In today's highly competitive and increasingly uncertain world, the quality and timeliness of an organization's "business intelligence" (BI) can mean not only the difference between profit and loss but even the difference between survival and bankruptcy. In helping senior executives and information ...
... In today's highly competitive and increasingly uncertain world, the quality and timeliness of an organization's "business intelligence" (BI) can mean not only the difference between profit and loss but even the difference between survival and bankruptcy. In helping senior executives and information ...
Tivoli Data Warehouse Version 1.3: Planning Planning and
... does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. IBM may have patents or pending patent applications covering subject matter described in this document ...
... does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. IBM may have patents or pending patent applications covering subject matter described in this document ...
About Accessing Data using e.Report Designer Professional
... Information in this document is subject to change without notice. Examples provided are fictitious. No part of this document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, for any purpose, in whole or in part, without the express written permission of Actua ...
... Information in this document is subject to change without notice. Examples provided are fictitious. No part of this document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, for any purpose, in whole or in part, without the express written permission of Actua ...
Data Warehousing Fundamentals
... This lesson identifies the analysis required to identify and categorize users that may need to access data from the warehouse, and how their requirements differ. Data access and reporting tools are considered. This lesson examines the role of data modeling in a data warehousing environment. The less ...
... This lesson identifies the analysis required to identify and categorize users that may need to access data from the warehouse, and how their requirements differ. Data access and reporting tools are considered. This lesson examines the role of data modeling in a data warehousing environment. The less ...
Data Movement Modeling
... The following example shows how the various diagrams work together when modeling a data transformation. The data movement diagram contains the transformation process with its input and output sources. You can build one or more data transformation diagram(s) to detail your transformations, and a tran ...
... The following example shows how the various diagrams work together when modeling a data transformation. The data movement diagram contains the transformation process with its input and output sources. You can build one or more data transformation diagram(s) to detail your transformations, and a tran ...
MapReduce: A major step backwards
... forced onto every MapReduce programmer, since there are no system catalogs recording the structure of records -- if any such structure exists. During the 1970s the DBMS community engaged in a "great debate" between the relational advocates and the Codasyl advocates. One of the key issues was whether ...
... forced onto every MapReduce programmer, since there are no system catalogs recording the structure of records -- if any such structure exists. During the 1970s the DBMS community engaged in a "great debate" between the relational advocates and the Codasyl advocates. One of the key issues was whether ...
expresstm database – ppdm wells and land
... regional mapping and well log correlation the well formation coverage establishes a regional perspective by providing a standardized stratigraphic column. We have also added detailed stratigraphic formation equivalence codes (Eras, Periods, Epochs, Stages) for every formation in the database. The da ...
... regional mapping and well log correlation the well formation coverage establishes a regional perspective by providing a standardized stratigraphic column. We have also added detailed stratigraphic formation equivalence codes (Eras, Periods, Epochs, Stages) for every formation in the database. The da ...
Dealing with inconsistent and incomplete data in a semantic
... be used to deal with IID in such databases. That is because FCA is a mathematical method that uses a lattice structure to reveal the associations among objects and attributes in a data set. The existing FCA approaches that can be used in dealing with IID in RDF databases include fault tolerance, Dau ...
... be used to deal with IID in such databases. That is because FCA is a mathematical method that uses a lattice structure to reveal the associations among objects and attributes in a data set. The existing FCA approaches that can be used in dealing with IID in RDF databases include fault tolerance, Dau ...
Package 'dplyr'
... as.data.frame is effectively a thin wrapper around data.frame, and hence is rather slow (because it calls data.frame on each element before cbinding together). as_data_frame just verifies that the list is structured correctly (i.e. named, and each element is same length) then sets class and row ...
... as.data.frame is effectively a thin wrapper around data.frame, and hence is rather slow (because it calls data.frame on each element before cbinding together). as_data_frame just verifies that the list is structured correctly (i.e. named, and each element is same length) then sets class and row ...
Data on the Web: From Relations to Semistructured Data
... and an inter-document structure (references to other documents through hyperlinks). The introduction of HTTP as a standard and use of HTML for composing documents is at the root of the universal acceptance of the Web as the medium of information exchange. The Database Culture There is another long-s ...
... and an inter-document structure (references to other documents through hyperlinks). The introduction of HTTP as a standard and use of HTML for composing documents is at the root of the universal acceptance of the Web as the medium of information exchange. The Database Culture There is another long-s ...
Link to technical report
... system. The REA database was primarily used for the storage of DRDC Atlantic environmental data. However, additional data sets from external sources were added for bathymetry and geological data. As use of the REA database increased, it became desirable to redesign the database to better serve the u ...
... system. The REA database was primarily used for the storage of DRDC Atlantic environmental data. However, additional data sets from external sources were added for bathymetry and geological data. As use of the REA database increased, it became desirable to redesign the database to better serve the u ...
Integrated Pan-Baltic Data Infrastructure for MSP
... strategies and scenarios. They envision the allocation of marine space to functions and activities based on new data sets and methodologies of data processing. They also offer a direction and vision as t ...
... strategies and scenarios. They envision the allocation of marine space to functions and activities based on new data sets and methodologies of data processing. They also offer a direction and vision as t ...
MongoDB Architecture Guide
... MongoDB deployments to address the hardware limitations of a single server, such as bottlenecks in RAM or disk I/O, without adding complexity to the application. MongoDB automatically balances the data in the sharded cluster as the data grows or the size of the cluster increases or decreases. Unlike ...
... MongoDB deployments to address the hardware limitations of a single server, such as bottlenecks in RAM or disk I/O, without adding complexity to the application. MongoDB automatically balances the data in the sharded cluster as the data grows or the size of the cluster increases or decreases. Unlike ...
tutorial.
... engine does not rely on SQL-style aggregation techniques. The time that an SQL-based tool takes to perform aggregation increases exponentially with the number of dimensions. With DecisionStream, the increase is linear which means that DecisionStream can deliver data extremely quickly. DecisionStream ...
... engine does not rely on SQL-style aggregation techniques. The time that an SQL-based tool takes to perform aggregation increases exponentially with the number of dimensions. With DecisionStream, the increase is linear which means that DecisionStream can deliver data extremely quickly. DecisionStream ...
Integrated Data Management: Manage Data Over Its Lifetime
... Automated compliance reporting, signoffs & escalations (SOX, PCI, NIST, ...
... Automated compliance reporting, signoffs & escalations (SOX, PCI, NIST, ...
White Paper
... analytical results to yet another platform that serves to inform users, customers, and applications. This approach is no longer acceptable – it simply takes too long. Because time-to-insight and timeto-action are critical, real-time analytics against live operational data yielding immediate actionab ...
... analytical results to yet another platform that serves to inform users, customers, and applications. This approach is no longer acceptable – it simply takes too long. Because time-to-insight and timeto-action are critical, real-time analytics against live operational data yielding immediate actionab ...
Understanding Data Warehouse Management
... design that minimizes the amount of data to be stored, while optimizing the potential of query performance. (I will discuss this further later on, because not all star schema designs have the same performance potential.) ...
... design that minimizes the amount of data to be stored, while optimizing the potential of query performance. (I will discuss this further later on, because not all star schema designs have the same performance potential.) ...
Preserving Transactional Data - Digital Preservation Coalition
... technologies that create them. Through a range of use cases – examples of transactional data – the report describes the characteristics and difficulties of these ‘big’ data for long-term access. Based on overarching trends, this paper will demonstrate potential solutions for maintaining these data i ...
... technologies that create them. Through a range of use cases – examples of transactional data – the report describes the characteristics and difficulties of these ‘big’ data for long-term access. Based on overarching trends, this paper will demonstrate potential solutions for maintaining these data i ...
Institutionen för datavetenskap Department of Computer and Information Science Yasser Rasheed
... them and thus doing Meta studies. A Meta-study is the method that takes data from different independent studies and integrate them using statistical analysis. If data is well described and data access is flexible then it is possible to establish unexpected relationships among data. It also helps in ...
... them and thus doing Meta studies. A Meta-study is the method that takes data from different independent studies and integrate them using statistical analysis. If data is well described and data access is flexible then it is possible to establish unexpected relationships among data. It also helps in ...
About the Tutorial
... About the Tutorial ..................................................................................................................................... i Audience ........................................................................................................................................ ...
... About the Tutorial ..................................................................................................................................... i Audience ........................................................................................................................................ ...
Data Warehousing Quick Guide
... Data mining functions such as association, clustering, classification, prediction can be integrated with OLAP operations to enhance the interactive mining of knowledge at multiple level of abstraction. That's why data warehouse has now become an important platform for data analysis and online analyt ...
... Data mining functions such as association, clustering, classification, prediction can be integrated with OLAP operations to enhance the interactive mining of knowledge at multiple level of abstraction. That's why data warehouse has now become an important platform for data analysis and online analyt ...
Test Data Extraction and Comparison with Test Data Generation
... Testing of database-intensive applications has unique challenges that stem from hidden dependencies, subtle differences in data semantics, target database schemes, and implicit business rules. These challenges become even more difficult when the application involves integrated and heterogeneous data ...
... Testing of database-intensive applications has unique challenges that stem from hidden dependencies, subtle differences in data semantics, target database schemes, and implicit business rules. These challenges become even more difficult when the application involves integrated and heterogeneous data ...
Big data
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.Analysis of data sets can find new correlations, to ""spot business trends, prevent diseases, combat crime and so on."" Scientists, business executives, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research.Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data were created; The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.Work with big data is necessarily uncommon; most analysis is of ""PC size"" data, on a desktop PC or notebook that can handle the available data set.Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires ""massively parallel software running on tens, hundreds, or even thousands of servers"". What is considered ""big data"" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered ""big"" one year becomes ordinary later. ""For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.""