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Software Functionality Revealed in Detail
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 etl perspectives on data warehousing

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.

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Core human resources (HR) includes the HR system of record that combines HR transactions, processes, and data. Main capabilities also include payroll management, benefits management, workforce management, and training management.  

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Documents related to » etl perspectives on data warehousing

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 performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

etl perspectives on data warehousing  used to limit the ETL processes to nodes 1 and 2 in the cluster and Ad-hoc queries to node 3 and 4. Workload Monitoring In order to have an overall view of what is happening on your system and to establish a baseline in expected performance you should take hourly AWR or statspack reports. However, when it comes to real-time system monitoring it is best to start by checking whether the system is using a lot of CPU resources or whether it is waiting on a particular resource and if so, what is that Read More

Unlocking Hidden Value from Investments in SAP NetWeaver Business Warehouse


Extending the capabilities and value of SAP NetWeaver Business Warehouse is a concern for users. To improve data use and fact-based decision making, and reduce stranded spreadsheets, SAP users can choose a business intelligence (BI) software solution such as IBM Cognos 8, with budgeting, planning, and forecasting functions. Find out more about how you can improve your business performance management content with BI.

etl perspectives on data warehousing  Transformation and Load | ETL Layer | Data Reporting | Data Migration | SAP Business Intelligence Tools | BW Accelerator | Business Intelligence Data Warehouse | BI Accelerator | Business Intelligence Software Providers | Online Analytical Processing | Data Warehouse Software | Business Intelligence Reporting Software | Business Intelligence White Paper | Ad-hoc Data Marts | Avoid Stranded Data | SAP Netweaver Advantages | Remodeling Data | Corporate Data Sources | Data in SAP and Non-SAP Systems | Read More

Business Intelligence: A Guide for Midsize Companies


Business intelligence (BI) is not a new concept. What’s new is that BI tools are now accessible for midsize companies. Managers can use BI to analyze complex information to support their decision-making processes, combining data from a variety of sources to get an integrated, 360-degree view of the company. Find out how to select the right BI software, the right vendor, and the right approach to implementing BI.

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Contemporary Business Intelligence and Its Main Components


Business intelligence (BI) represents the tools and systems that play a key role in the strategic planning process by allowing a company to manipulate corporate data for decision-making. But what exactly are the primary components of BI?

etl perspectives on data warehousing  also different from conventional ETL tools for data warehousing because it neither moves data nor creates new data stores of integrated data. Rather, it leaves data where it is, leveraging metadata repositories across multiple foundation enterprise systems, and visibly pulls information into new applications. As a result, customers may be content to trade in expensive DWs for a data extraction and presentation layer that sits on top of existing transactional systems—but only on the condition that they Read More

Data Quality: A Survival Guide for Marketing


Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.

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Data Quality: Cost or Profit?


Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.

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Appliance Power: Crunching Data Warehousing Workloads Faster and Cheaper than Ever


Appliances are taking up permanent residence in the data warehouse (DW). The reason: they are preconfigured, support quick deployment, and accelerate online analytical processing (OLAP) queries against large, multidimensional data sets. Discover the core criteria you should use to evaluate DW appliances, including performance, functionality, flexibility, scalability, manageability, integration, and extensibility.

etl perspectives on data warehousing  data warehouse model,data warehouse software,enterprise data warehouse,olap database,olap tools,data warehouse concepts,data warehouse schema,data warehouse system,data warehouse tools,data warehouse training,olap data warehouse,building a data warehouse,building the data warehouse,business intelligence olap,data warehouse data mart Read More

Data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise


While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help.

etl perspectives on data warehousing   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 perspectives 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

Data Storage in the Cloud-Can you Afford Not To?


Storing data in the cloud using Riverbed Technology’s Whitewater cloud storage gateway overcomes a serious challenge for IT departments: how to manage the vast, and ever-growing, amount of data that must be protected. This paper makes the business case for cloud storage, outlining where capital and operational costs can be eliminated or avoided by using the cloud for backup and archive storage.

etl perspectives on data warehousing  data storage in the cloud,cloud data storage,online data storage,offsite data storage,data storage cloud,data storage solution,data storage business,data storage,data storage internet,data storage service,best cloud storage,online data storage backup,microsoft cloud storage,cloud services,cloud storage providers Read More

Ramco HCM on Cloud Is on a Roll in the Middle East; Africa


Ramco Systems, an enterprise software vendor that has lately been focused on delivering enterprise resource planning (ERP) in the cloud and on tablets and smartphones, recently showcased its all new HR and talent management solution, Ramco HCM on Cloud (HCM standing for human capital management). Since the global launch of Ramco HCM on Cloud in June 2013, Ramco has added some of the largest

etl perspectives on data warehousing   Read More

Data Quality Strategy: A Step-by-Step Approach


To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.

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Demystifying Data Science as a Service (DaaS)


With advancements in technology, data science capability and competence is becoming a minimum entry requirement in areas which have not traditionally been thought of as data-focused industries. As more companies perceive the significance of real-time data capture and analysis, data as a service will become the next big thing. India is now the third largest internet user after China and the U.S., and the Indian economy has been growing rapidly. Read this white paper to find out more about how data SaaS is set to become a vital part of business intelligence and analytics, and how India will play a role in this trend.

etl perspectives on data warehousing  data science, big data, analytics, data SaaS, data scientist, India, data as a service, real-time data, data analytics Read More

The Operational Data Lake: Your On Ramp to Big Data


Companies recognize the need to integrate big data into their real-time analytics and operations, but this poses a lot of technical and resource challenges. Meanwhile, those organizations that have operational data stores (ODSs) in place find that, while useful, they are expensive to scale. The ODS gives real-time visibility into operational data. While more cost-effective than a data warehouse, it uses outdated scaling technology, and performance upgrades require very specialized hardware. Plus, ODSs just can't handle the volume of data that has become a matter of fact for businesses today.

This white paper discusses the concept of the operational data lake, and its potential as an on-ramp to big data by upgrading outdated ODSs. Companies that are building a use case for big data, or those considering an upgrade to their ODS, may benefit from this stepping stone. With a Hadoop relational database management system (RDBMS), companies can expand their big data practices at their own pace.

etl perspectives on data warehousing  operational data lake, Hadoop RDBMS, operational data store, transactional database, transaction processing, big data repository Read More

Overall Approach to Data Quality ROI


Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company. Of the many benefits that can accrue from improving the data quality of an organization, companies must choose which to measure and how to get the return on investment (ROI)—in hard dollars. Read this paper to garner an overall approach to data quality ROI.

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