X
Start evaluating software now

 Security code
Already have a TEC account? Sign in here.
 
Don't have a TEC account? Register here.

Field Service Management (FSM)
Field Service Management (FSM)
Field service management (FSM) software is a set of functionalities for organizations or departments within organizations that have as main focus the intallation, maintanance, reparing, and meter r...
 

 etl data mart

Core PLM--Product Data Management - Discrete RFI/RFP Template

Product Data Management (PDM), Engineering Change Order and Technology Transfer, Design Collaboration, Process and Project Management, Product Technology Get this template

Read More
Start evaluating software now

 Security code
Already have a TEC account? Sign in here.
 
Don't have a TEC account? Register here.

Field Service Management (FSM)
Field Service Management (FSM)
Field service management (FSM) software is a set of functionalities for organizations or departments within organizations that have as main focus the intallation, maintanance, reparing, and meter r...

Documents related to » etl data mart

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 data mart  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

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 data mart  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 Or Read More

Metagenix Reverse Engineers Data Into Information


Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries of the existing legacy systems (which are often incorrect). Other unique Metagenix approaches include an "open book" policy, which includes publishing product price lists on their web site and complete access to company officials, including CEO and President Greg Leman. According to Mr. Leman, "we’re pathologically honest".

etl data mart  offers the addition of ETL script generation and a lower price point than Evoke. MetaRecon 2.5 includes a desktop version which provides profiling and analysis, transformation mapping, repository maintenance, reports, DDL and XML generation in a one client/one server license arrangement on Windows NT/2000 for $25,000 per year which competes directly with Evoke Axio on a feature-for-feature basis. Prior to the release of these products, the only solution was custom coding by in-house IT staff, an expensive Read More

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 data mart  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

Ardent Software Enters the SAP Data Extraction Market


Ardent Software has announced the addition of SAP extraction and load capabilities to their DataStage product, increasing their strength in the Extract/Transform/Load tool market. Due to the prevalence of SAP in the Enterprise Resource Planning space, this addition will provide a competitive advantage over some of the other major ETL vendors such as Sagent and Computer Associates.

etl data mart  very few of the ETL ( Extract/Transform/Load ) tool vendors have produced modules that allow extraction from SAP. The vendors have had difficulty due to the specialized knowledge of SAP required to produce the custom code which could understand SAP''s internal data formats. Ardent''s entry into this market will give it leverage with any corporation that uses SAP''s Enterprise Resource Planning system. The ability to integrate ERP systems with data warehouses and provide true enterprise-wide information is Read More

Considerations for Owning versus Outsourcing Data Center Physical Infrastructure


When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, data center life expectancy, regulatory requirements, and other strategic factors. This paper discusses how to assess these key factors to help make a sound decision.

etl data mart  for Owning versus Outsourcing Data Center Physical Infrastructure When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, 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 data mart  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 Read More

Data Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics


Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident decisions and take the right actions, you need more than just software—you also need the right data. This white paper discusses a solution to help ensure that your applications process reliable data so that your spend analyses and supplier risk assessments are accurate, trustworthy, and complete.

etl data mart  Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident decisions and take the right actions, you need more than just software—you also need the right data. This white paper discusses a solution to help ensure that your applications process reliable data so that your spend analyses and supplier risk Read More

Backing up Data in the Private Cloud: Meeting the Challenges of Remote Data Protection - Requirements and Best Practices


This white paper describes some of the common challenges associated with protecting data on laptops and at home and remote offices and portrays proven solutions to the challenges of protecting distributed business data by establishing a private cloud/enterprise cloud. Learn which best practices can ensure business continuity throughout an organization with a distributed information technology (IT) infrastructure.

etl data mart  up Data in the Private Cloud: Meeting the Challenges of Remote Data Protection - Requirements and Best Practices This white paper describes some of the common challenges associated with protecting data on laptops and at home and remote offices and portrays proven solutions to the challenges of protecting distributed business data by establishing a private cloud/enterprise cloud. Learn which best practices can ensure business continuity throughout an organization with a distributed information Read More

10 Errors to Avoid When Building a Data Center


In the white paper ten errors to avoid when commissioning a data center, find out which mistakes to avoid when you're going through the data center...

etl data mart  Errors to Avoid When Building a Data Center Proper data center commissioning can help ensure the success of your data center design and build project. But it''s also a process that can go wrong in a number of different ways. In the white paper Ten Errors to Avoid when Commissioning a Data Center , find out which mistakes to avoid when you''re going through the data center commissioning process. From bringing in the commissioning agent too late into the process, to not identifying clear roles for Read More

Business Basics: Unscrubbed Data Is Poisonous Data


Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data quality is low, and the short- and long-term benefits are great.

etl data mart  Basics: Unscrubbed Data Is Poisonous Data Introduction A manufacturing company experienced continually increasing overhead costs in its purchasing and materials departments. It also began to see increased inventory levels and production line delays. A consulting firm was brought in to analyze the company''s materials management processes and the level of competence of its materials and procurement staff. After mapping the entire process, from the creation of a bill of materials through post-sales Read More

Increasing Sales and Reducing Costs across the Supply Chain-Focusing on Data Quality and Master Data Management


Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.

etl data mart  Sales and Reducing Costs across the Supply Chain-Focusing on Data Quality and Master Data Management Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses 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 data mart  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 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 data mart  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 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 data mart  High-density Zones in a Low-density Data Center Deploying High-Density Zones in a Low-Density Data Center If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. Together, APC’s global teams work to fulfill their mission of creating delighted customers. To do this, the Company focuses its efforts on four primary application areas: Home/Small Office; Business Networks; Data Centers and Facilities; and Access Provider Networks. Source Read More