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Software Functionality Revealed in Detail
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 data warehouse architecture


Best Practices for a Data Warehouse on Oracle Database 11g
Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW

data warehouse architecture  Practices for a Data Warehouse on Oracle Database 11g Best Practices for a Data Warehouse on Oracle Database 11g If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. Oracle has been helping customers like you manage your business systems and information with reliable, secure, and integrated technologies. Source : Oracle Resources Related to Data Warehouse : Data Warehouse (Wikipedia) Best Practices for a Data Warehouse on Oracle

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

Warehouse Management Systems (WMS)

A warehouse management system (WMS) should provide database and user-level tools in order for a company to optimize its storage facilities while at the same time providing user level task direction and activity support. The WMS should enable warehouse operators to optimize pick, put-away, and replenishment functions by employing powerful system logic to select the best locations and sequences. 

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Documents related to » data warehouse architecture

Ask the Experts Question: Do Organizations Really Need a Physical Data Warehouse Structure?


We recently had a question from one of our readers (through our Ask the Experts page) discussing QlikView’s approach to data collection. Reader’s Question “QlikView says its innovative way of collecting data and not needing a physical data warehouse (DW) structure is the right thing to do in DW/business intelligence (BI) solutions. Can one expect to build a sustainable / scalable corporate

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IBM and Deutsche Telecom Announce Plans for 100 Terabyte Data Warehouse


According to an announcement by International Business Machines on Thursday December 16, 1999, IBM is working with German telecommunications services company Deutsche Telekom to assemble the largest data warehouse in the world. When complete, the warehouse will contain up to 100 terabytes of customer and call records, to be used for Customer Relationship Management (CRM) applications.

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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.

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The Data Warehouse Institute (TDWI) Conference in San Diego: The Agile Approach to Business Intelligence (BI)


Next month a TDWI World Conference will be taking place in San Diego, California. What’s so special about this conference anyway? The answer is simple:  the general topic is going to be “Creating an Agile BI Environment”. Progressively in the last four or five years, the agile software development methodology has been jumping from the software development area to the data warehouse

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Enterprise Data Management: Migration without Migraines


Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an understanding of the entire ERP data lifecycle combined with industry-specific experience, knowledge, and skills to drive the process through the required steps accurately, efficiently, and in the right order. Read this white paper to learn more.

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Don't Be Overwhelmed by Big Data


Big Data. The consumer packaged goods (CPG) industry is abuzz with those two words. And while it’s understandable that the CPG world is excited by the prospect of more data that can be used to better understand the who, what, why, and when of consumer purchasing behavior, it’s critical CPG organizations pause and ask themselves, “Are we providing retail and executive team members with “quality” data, and is the data getting to the right people at the right time? Big Data can be a Big Deal - read this white paper for some useful tips on ensuring secure, quality data acquisition and management.

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Warehouse Management Systems: Pie in the Sky or Floating Bakery?Part Two: The Pareto Principle, Processes, and People: Assessing Your Warehouse Management System Needs


To ensure your warehouse management system is implemented as painlessly as possible, you must assess your warehouse situation before you decide on a warehouse solution. Using the Pareto Principle, where a minority of inputs yields the majority results; examining your processes; evaluating your personnel; monitoring the progress of implementation; and testing are the best ways to ensure both a successful launch and long term return on investment.

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Engineering and Architecture


The engineering and architecture (also known as civil engineering) industry deals with the planning, design, and maintenance of physical structures including roads, bridges, buildings, and so on. As one of the oldest engineering disciplines, engineering and architecture has a very long history and keeps evolving based on the advancement of science and technology.

Facing economic and technological waves together with shifting market demands, today’s engineering and architecture firms need to address the following trends or challenges in order to be successful in their business practices.

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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.

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Reinventing Data Masking: Secure Data Across Application Landscapes: On Premise, Offsite and in the Cloud


Be it personal customer details or confidential internal analytic information, ensuring the protection of your organization’s sensitive data inside and outside of production environments is crucial. Multiple copies of data and constant transmission of sensitive information stream back and forth across your organization. As information shifts between software development, testing, analysis, and reporting departments, a large "surface area of risk" is created. This area of risk increases even more when sensitive information is sent into public or hybrid clouds. Traditional data masking methods protect information, but don’t have the capability to respond to different application updates. Traditional masking also affects analysis as sensitive data isn’t usually used in these processes. This means that analytics are often performed with artificially generated data, which can yield inaccurate results.

In this white paper, read a comprehensive overview of Delphix Agile Masking, a new security solution that goes far beyond the limitations of traditional masking solutions. Learn how Delphix Agile Masking can reduce your organization’s surface area risk by 90%. By using patented data masking methods, Delphix Agile Masking secures data across all application lifecycle environments, providing a dynamic masking solution for production systems and persistent masking in non-production environments. Delphix’s Virtual Data Platform eliminates distribution challenges through their virtual data delivery system, meaning your data can be remotely synchronized, consolidated, and takes up less space overall. Read detailed scenarios on how Delphix Agile Data Masking can benefit your data security with end-to-end masking, selective masking, and dynamic masking.

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