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Discrete Manufacturing (ERP)
Discrete Manufacturing (ERP)
The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as or...
 

 etl contains information on data warehousing

Product Information Management (PIM) RFI/RFP Template

Integration with Back-Office Systems, Product Information Repository, Data Distribution and Synchronization, Employee Productivity, Application Technology Get this template

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Discrete Manufacturing (ERP)
Discrete Manufacturing (ERP)
The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as or...

Documents related to » etl contains information on data warehousing

A Definition of Data Warehousing


There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

etl contains information on data warehousing  are currently over 50 ETL tools on the market. The data acquisition phase can cost millions of dollars and take months or even years to complete. Data acquisition is then an ongoing, scheduled process, which is executed to keep the warehouse current to a pre-determined period in time, (i.e. the warehouse is refreshed monthly). Changed Data Capture: The periodic update of the warehouse from the transactional system(s) is complicated by the difficulty of identifying which records in the source have changed Read More

Computer Associates Splashes Into the Data Warehousing Market with Platinum Technology Acquisition


Computer Associates DecisionBase is an Extract/Transform/Load tool designed to help in the population and maintenance of data warehouses. First released in March of 1998, the product is geared towards large implementations with the need for metadata management. The current release is 1.9, which is the fourth release of the product.

etl contains information on data warehousing  of vendors in the ETL market in the mid-1990''s was small, comprised of basically four companies (Prism, Carleton, Evolutionary Technologies, Trinzic) plus some modest offerings from IBM. In the past four years, the space has become very crowded, with over fifty vendors competing in various market niches (e.g. specializing in access to VSAM databases). Four vendors still primarily control the general market, including Ardent, Computer Associates, Informatica, and Sagent, with some offerings from IBM and Read More

Informatica Morphs into Enterprise Decision Support Vendor


Informatica Corporation, long an established vendor in the Extract/Transform/Load (ETL) market space, has morphed themselves into a vendor of solutions for the "e-Business infrastructure". In conjunction with their contract to build an enterprise data warehouse for the United States Postal Service and their MX2 metadata exchange initiative, Informatica is reinventing itself.

etl contains information on data warehousing  same machine as the ETL server as parsing files across an NFS mount is inefficient). In addition, the product contains an open metadata repository, a pipelined, multi-threaded server engine, and a scheduling mechanism. PowerMart was followed by PowerCenter in 1998. PowerCenter is an enterprise data integration hub that integrates and unifies diverse data sources, including ERP systems. In addition to PowerMart for data movement, the product contains a Global Repository which, via Informatica''s MX2 API, Read More

Oracle Further Orchestrates Its SOA Forays Part Two: Strategy


Oracle's vision of a complete collaborative e-Business solution requires a database strategy, an application server strategy, and an e-business strategy. Will users buy into this vision?

etl contains information on data warehousing  extraction, transformation, and loading (ETL), and data mining. Now Oracle has created what is likely the most complete integration solution available. In fiscal 2004, the vendor released a new version of its technology platform software, dubbed collectively Oracle 10g . The software provides a platform for developing and deploying applications on the Internet and on corporate intranets, and it comprised of database management software, application server software, and collaboration software, including Read More

Distilling Data: The Importance of Data Quality in Business Intelligence


As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence environment. This article looks at issues in data quality and how they can be addressed.

etl contains information on data warehousing  extract, transform, and load (ETL) process in a data warehousing system extracts records from data source(s), transforms them using rules to convert data into a form that is suitable for reporting and analysis, and finally loads the transformed records into the destination (typically a data warehouse or data mart). Data cleansing is an integral part of the transformation process and enforces business and schema rules on each record and field. Data cleansing involves the application of quality screens Read More

Deploying High-density Zones in a Low-density Data Center


New power and cooling technology allows for a simple and rapid deployment of self-contained high-density zones within an existing or new low-density data center. The independence of these high-density zones allows for reliable high-density equipment operation without a negative impact on existing power and cooling infrastructure—and with more electrical efficiency than conventional designs. Learn more now.

etl contains information on data warehousing  high-density zones,low-density data center,data center cooling density zones,data center infrastructure density zones,data center efficiency,high-density zones data center dynamics,data center designs high-density zones,apc data center,low-density power cooling infrastructure Read More

Scalable Data Quality: A Seven-step Plan for Any Size Organization


Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

etl contains information on data warehousing  Data Quality: A Seven-step Plan for Any Size Organization Melissa Data’s Data Quality Suite operates like a data quality firewall – instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Scalable Data Quality: A Seven-step Plan for Any Size Organization : Data quality (Wikipedia) Scalable Data Quality: A Seven-step Plan for Any Size Organization Data Quality is also known as : Customer Read More

The World in Crisis: A Report on the Impact on Global Outsourcing Relations


As companies come to grips with current fiscal realities, choices may result more from reaction than the use of sound judgment. Removing vital resources and reducing services can create an even lower return on investment (ROI) than existed before downsizing. Outsourcing may offer a solution. Learn about the effects that the present financial situation will have on the outsourcing community, including buyers and suppliers.

etl contains information on data warehousing   Read More

The Necessity of Data Warehousing


An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding technology selection and access to historical 'legacy' data are also discussed.

etl contains information on data warehousing  The selection of the ETL tool requires an understanding of the source data feeds. The following issues should be considered: Many warehouses are built from legacy systems that may be difficult to access from the computer network. ETL tools often do not reside on the same machine as the source data. The data structures of the legacy systems may be hard to decompose into raw data. Legacy data is often dirty (containing invalid data, or missing data). Care must be taken in the evaluation of the tool to Read More

Customer Data Integration: A Primer


Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a customer relationship management initiative can help provide organizations with a successful framework to manage data on a continuous basis.

etl contains information on data warehousing  Master Data Management,MDM,Customer Data Integration,CDI,Data Integration,CRM,BI,business intelligence,data standardization,data consolidation,Customer Relationship Management Read More

Six Steps to Manage Data Quality with SQL Server Integration Services


Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

etl contains information on data warehousing  Steps to Manage Data Quality with SQL Server Integration Services Melissa Data''s Data Quality Suite operates like a data quality firewall '' instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Six Steps to Manage Data Quality with SQL Server Integration Services : Data quality (Wikipedia) Six Steps to Manage Data Quality with SQL Server Integration Services Data Quality is also known as : Read More

Linked Enterprise Data: Data at the heart of the company


The data silos of today's business information systems (IS) applications, and the pressure from the current economic climate, globalization, and the Internet make it critical for companies to learn how to manage and extract value from their data. Linked enterprise data (LED) combines the benefits of business intelligence (BI), master data management (MDM), service-oriented architecture (SOA), and search engines to create links among existing data, break down data walls, provide an open, secure, and long-term technological environment, and reduce complexity—read this white paper to find out how.

etl contains information on data warehousing  data silo,business information systems,linked enterprise data,LED,business intelligence,BI,master data management,MDM,service-oriented architecture,SOA,search engines,Antidot,Antidot white paper,semantic web Read More

Master Data Management: Extracting Value from Your Most Important Intangible Asset


In a 2006 SAP survey, 93 percent of respondents experienced data management issues during their most recent projects. The problem: many organizations believe that they are using master data, when in fact what they are relying on is data that is dispersed throughout the enterprise. Discover the importance of master data and how the ideal master data management (MDM) solution can help your business get it under control.

etl contains information on data warehousing  master data management,mdm,data management,product information management,mdm tool,master data management tool,sap master data management,master data management soa,master data management bi Read More

Data Blending for Dummies


Data analysts support their organization’s decision makers by providing timely key information and answers to key business questions. Data analysts strive to use the best and most complete information possible, but as data increases over time, so does the time required to identify and combine all data sources that might be relevant.

Data blending allows data analysts a way to access data from all data sources, including big data, the cloud, social media sources, third-party data providers, department data stores, in-house databases, and more, and become faster at delivering better information and results to their organizations. In the past, the challenge for data analysts has been accessing this data and cleansing and preparing the data for analysis. The access, cleansing, and preparing data stages are complex and time intensive. These days, however, software tools can help reduce the burden of data preparation, and turn data blending into an asset.

Read this e-book to understand why data blending is important, and learn how combining data means that you can get answers to your business questions and better meet your business needs. Also learn how to identify what features to look for in data blending software solutions, and how to successfully deploy these tools within your business. Data Blending for Dummies breaks the subject down into digestible sections, from understanding data blending to using data blending in the real world. Read on to discover how data blending can help your organization use its data sources to the utmost.

etl contains information on data warehousing  data blending, data analyst, data source, data analysis, data software, data cleansing, data access, data blending software Read More