COG-632 exam Dumps Source : IBM Cognos 10 BI Metadata Model Developer
Test Code : COG-632
Test denomination : IBM Cognos 10 BI Metadata Model Developer
Vendor denomination : IBM
braindumps : 53 true Questions
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With a robust ETL (Extract, transform, and cargo) manner, a company can dig abysmal into massive records units via queries and retrieve significant counsel that can breathe a top-notch concept for the company. furthermore, with effectual integration, that you can steer transparent of any delays in decision-making, which may abet you gain first-mover talents in an already saturated market. With InfoSphere DataStage, IBM brings you a sophisticated ETL device in order to no longer best abet you construct information, however additionally connote you can present it graphically and Take selections in rectify time, saving you from a misplaced chance.
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How IBM Cognos is distinctive
in contrast to InfoSphere DataStage, IBM Cognos is a commerce intelligence appliance that is web-based. It presents entertaining rig for analytics, reporting, metrics, score carding, and monitoring. The a few add-ons of IBM Cognos that encompass a framework manager, cube dressmaker, and transformer permit for the efficient processing of facts and formulation of relationships to achieve at actionable effects. The Cognos express addition additionally allows company users to plot, budget, and forecast, which is constructive in production, earnings, advertising, analysis and development, and other departments.
With a enterprise intelligence device dote Cognos by way of your facet, you could arrive at advantageous and principal information that is enjoyable and catered to your particular wants. The sources could breathe previous commerce information or records picked up from numerous sources within the users’ neighborhood to aid you devour in humor the market traits and what can breathe anticipated in the future. and not using a BI tool, surviving this changed era may well breathe a problem. With ExistBI’s IBM Cognos practising, you devour got more suitable probabilities of stumbling upon whatever thing entertaining in an trouble to interest the hundreds than devoid of it.
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IBM’s cloud computing strategy is clear-cut to some extent with Cognos 10, the latest unencumber of the commerce intelligence platform.
As a participate of Cognos 10, IBM is delivering a cloud-based provider they denomination Cognos 10 within the Cloud.
It’s a hosted provider for companies that wish to transfer its license to the cloud or fully host online.
The strategy offers some perception into the IBM utility group’s fashion to cloud computing.
We discern three distinctions to the Cognos approach and how it reflects on IBM’s approach.Hosted, now not SaaS
The carrier has the identical aspects that comes with Cognos 10, arguably the highlight of IBM’s tips on demand convention. It aspects predictive analytics and gregarious collaboration capabilities through its integration with Lotus Connections.
Cognos 10 in the Cloud is not in the vein of a traditonal SaaS provider. You pay a license fee for internet hosting your license. shoppers might moreover switch the license from on-premise to the IBM Cloud. valued clientele might moreover also determine to purchase a hosted license without investing within the on-premise carrier.Streaming information from the Cloud
Cognos 10 is a ponderous responsibility analytics engine. SPSS is baked in. It pulls in information from the cloud, including unstructured statistics. users can employ third-celebration information to collect a historical evaluation, discern a true-time circulation or conclude “what if,” analysis.less About Cloud, greater About beginning
It’s questionable if there's demand for a pure cloud computing service. companies devour abysmal investments in information centers but they are searching for the prerogative way to prolong to a cloud environment. That’s a trend they discern a Great deal from SAP, VMware, Microsoft and a host of others.
IBM says it’s approach with Cognos 10 is much less about the cloud than start. valued clientele want a area to position their application license. That’s the genesis IBM is making with Cognos 10 within the Cloud.
we devour questions concerning the IBM method. it’s just a small participate of IBM’s cloud providing but it’s illustrative of a larger strategy to accomplish employ of the cloud as an option birth model for usual application offerings.
IBM provides collaboration, cellular abet and straightforward-to-use analytics to its flagship BI application.
At its current company Analytics discussion board in Las Vegas this week, IBM (NYSE: IBM) persisted its aggressive shove into enterprise intelligence and analytics with a number of current product releases, including the newest release of its flagship BI application, Cognos 10.
additionally at the current convention for Cognos and SPSS users, large Blue unveiled DB2 10 database utility with a forty percent performance boost, IBM InfoSphere Server eight.5 statistics integration and pleasant software, and a expertise preview of Hadoop-primarily based large data analytics application.
Cognos 10 boasts a few current facets, together with collaboration and analytics capabilities and iPad, iPhone and BlackBerry help.
IBM expenses Cognos 10 as "probably the most stupendous analytics offering because the acquisition of Cognos" three years in the past. The addition of Lotus Connections software adds gregarious collaboration and the capability to talk as considerations arise.
IBM moreover claims "intuitive navigation capabilities and simplified user event" that "eliminates the frustration of having to interrupt user instruct of notion on account of the necessity to swap between views to collect the prerogative tips."
The application offers an built-in view of antiquated assistance with precise-time updates. users can select various views from essential true-time suggestions to advanced predictive analytics.
IBM said Cognos 10 "takes analytics out of the typical structure of reports and charts to a more interactive design and broader evaluation and perception, making it less demanding by using proposing built-in photographs and movies to steer commerce clients. they could now profit instant perception with analytics in an simple to employ layout every time, anywhere on their mobile gadgets."
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Data Warehouse Concepts
What is a Data Warehouse?
A warehouse in universal terms is a repository where they store their information. Coming to the data warehouse, it is a collection of data organized in a specific manner and categorized information.
A data warehouse stores historical data of an organization so that they can analyze their performance over the past years and device for the future.
The favorite definition of the data warehouse by WH Inmon:
A data warehouse is:
Another definition from Ralph Kimball is more precise:
A data warehouse is a copy of transaction data specifically structured for query and analysis.
What is a Data mart?
A data mart is a subset of a data warehouse. Suppose they devour an organization established in many different locations and each location maintains a data warehouse which they summon it as data mart because a warehouse will devour everything the data integrated and as far as data mart is considered it will breathe a participate of the data warehouse.
What are uses of having a Data Warehouse?
A Data Warehouse, in general, is used to analyze trends over a epoch of time and enhances the decision making of an organization. Once the data is loaded into the warehouse will breathe creating an OLAP cube or directly employ the data to analyze trends. Basing on which the top even management will approach their future commerce strategies.
Data warehouse Architecture
We will multiple sources there everything the operational data will breathe stored. Some may store them in flat files and some in databases. They will devour to read everything the sources and accomplish ETL (Extract, Transform and Load operations) which will breathe used to integrate data from these sources and transform them into one unique structure and then load into their target data warehouse.
Data Warehouse Architecture
There are 2 approaches in designing your data warehouse. First one is “Top-Down approach” and second is “Bottom-up approach”.
Top — Down Approach: The above image shows the top-down approach, where they are reading data from multiple sources and transforming the data and loading into your warehouse, then on top of that they are creating their data marts.
Bottom-up Approach: The antithetical of the above approach is this. Here they will breathe creating data marts first and then they will create their data ware house on top of everything the data marts.
Data Warehouse Terms
The schema is a logical arrangement of tables in a data warehouse. They devour schemas in relational databases. Very much dote very Data warehouse has schema’s namely Star schema, Snowflake schema, and Fact-Constellation.Star Schema
In this logical arrangement of tables, the fact table will breathe at the center of the schema surrounded by multiple dimension tables. Fact and dimension tables are linked via a Primary — Foreign Key relationship. Fact table will breathe having queer keys of everything the Dimension tables along with the facts whereas a Dimension table will breathe having attributes which picture the Dimension.Snowflake Schema:
In this logical arrangement of tables, they devour dimension tables connected to other dimension tables which in turn are connected to fact table via a primary-foreign key relationship. Else they can grunt that the Dimension tables in Snowflake schema are normalized. Because of the normalization, the data redundancy will breathe reduced and a lot of storage space is saved.Fact Constellation Schema:
A fact constellation schema, unlike the start or snowflake, will devour multiple fact tables. It is moreover called as Galaxy Schema.Star Schema Example
In this schema shown, a fact table (sales) is connected to multiple dimension tables item, location, time and branch. Each dimension table has attributes describing the dimension and fact table has the queer key of everything dimension tables along with facts dote dollars_sold and units sold.Snowflake Schema Example:
In this schema, a fact table (sales) is connected to multiple dimensions, whereas the dimensions particular and location are again connected to City and Supplier dimension respectively.Fact Constellation schema Example:
In this schema, they can find two fact table Sales and Shipping connecting with each other and are again connected to multiple dimension tables individually.
Now they are jumping into the actual topic “Cognos commerce Intelligence”.Overall DWH architecture
We will breathe dealing with Cognos reporting appliance and Cognos Metadata managing appliance and other useful tools that Cognos BI has.
Let’s evanesce through where BI will actually Come into picture
The below image shows overall Data warehouse Architecture. From data load to using the data loaded into the tables for enhancing the commerce of your organization. From everything the data sources (may it breathe Operational databases, Flat files etc.) they load the data into data warehouse through different ETL process and creating cubes and using the data for mining purpose.
The below pattern will picture the commerce Intelligence rush how the data will breathe used for commerce optimization. commerce intelligence is a process from where you will breathe able to derive methods to enhance your commerce with the data that you have. Using BI one will breathe able to ascertain at their data at different levels and will breathe able to accomplish decisions to accomplish their commerce better.
Cognos BI will breathe used to generate reports (list, crosstab, and charts). The below pattern will point to you the entire rush that happens with Cognos.
The first step in using Cognos BI is to accumulate requirements. This is practicable when you understand your commerce structure and the data that drives your business. Once the requirement is finalized the next step is to create your framework metadata model. You can select either to you star schema or snowflake schema or you can employ the existing relational model.
After this step is done with they devour to publish the model that they devour created. Publishing the model will accomplish your metadata model available for reporting purpose. Once your model is published, they can employ the model to create different types of reports as per your requirement and they will schedule the reports basing your requirement.
Different Versions of Cognos
Cognos ReportNet (CRN) is a web-based software product for creating and managing ad-hoc and custom-made reports. ReportNet is developed by the Ottawa-based company Cognos (formerly Cognos Incorporated), an IBM company. The web-based reporting appliance was launched in September 2003. Since IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet dote everything other Cognos products.
IBM Cognos 8 BI, initially launched in September 2005, combined the features of several previous products, including ReportNet, PowerPlay, Metrics Manager. There are moreover Express and Extended versions of Cognos 8 BI. replete features:
We can discern different components and how they are fitting in. The top most layer, where they can discern Cognos Connection, Administrator, commerce Insight and different studios. They everything are web-based and end-user needs not to install any client side software if he has the latest web browser installed.
The bottom layer is basically data layer where you may devour homogeneous or heterogeneous database systems. Data may breathe relational or multi-dimensional. On top of it, they can discern three modeling tools there — Framework Manager, Transformer, and Metric Designer. everything of them are client based installation.
We’ll maintain the rush of components from top to bottom as shown in below BI components figure.
Before getting into the actual topics about how you create the model, deploy it and employ it to create reports they devour to first know the architecture of Cognos.
Cognos commerce Intelligence framework is 3- tier architecture. The first tier of the architecture will breathe having the Web server which is responsible for the accessing the user interfaces. The second tier will breathe having IBM Cognos BI Server which will breathe having gateways and dispatchers to route requests from different UI’s to Database and communicate back to the server.
The Third Tier will devour the data sources. You can devour multiple data sources and they will connect to the Cognos server using JDBC or API’s.
The Below pattern shows the architecture of Cognos BI:
In the data tier, you can discern one more component called the “Content Store” which is a repository for the Cognos server. Whenever you save any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing protest created will fetch the data from the Content Store. everything this labor will breathe performed by the “Content Manager”.
Content Manager: The content manager is responsible for connecting with the Content Store database and saving any report or model back to the Content Store.
Dispatcher: The job of dispatchers is to route requests sent from and to the Web UI/Windows based components.
Web-based and Windows Based components of Cognos
Cognos BI has two types of components,
1. Web-Based components.
2. Window based components.
Web-based components includes
I’d dote to thank Patrick Callahan, Senior Director at AmberLeaf for his contributions to this article.
Based on the interest from my final article on hosted analytics, I decided to continue the trend and talk about businessintelligence in a software as a service (SaaS) mode. Customer relationship management (CRM) applications flocked to SaaS models based on the success of salesforce.com. Traditional on-premise vendors dote Siebel, SalesLogix, Microsoft, Oracle, Onyx and the leisure followed suit by providing SaaS solutions. The lure of no administration, no hassle upgrades, low switching barriers and fastimplementation times attracted many customers into the current model.
However, commerce intelligence, database marketing and data warehousing devour typically lagged these types of trends. It reminds me of the shift to the offshore implementation and uphold model. Datawarehousing lagged this trend due to the closer relationship between users and their uphold staff, the numerous requests for small enhancements that necessity to breathe performed quickly and the massiveamount of systems integration between legacy systems.
Trepidation with SaaS models for commerce intelligence (BI) appeared for the very reasoning. As commerce intelligence became the center of large data integration and data attribute efforts, concernsover the scalability and sophistication of SaaS vendors in this admiration raised cautionary flags. In fairness, CRM systems are not without their data integration challenges, and the SaaS vendors haverisen to the occasion with APIs and other connectivity tools.
Nevertheless, progress cannot breathe stopped. It is BI’s turn to flock to the cloud and the software industry is responding.Data Warehouse Infrastructure The first major technologies from BI SaaS models are coming from the infrastructure side. current startups are aiming at the Teradata/Netezza marketplace as highly scalable, incredibly fast, query(and data loading) processors. Using proprietary databases (sometimes columnar, inverted index structures with unique compression algorithms, sometimes with proprietary hardware, sometimes not), SaaSvendors are raising the bar in cost per performance benchmarks. Though Netezza is probably noiseless the leader in the performance category, Netezzainstallations suffuse a large premium for the effort.
Vertica may breathe the leader in this category. With some formalized benchmarks against Oracle, some price/performance studies against Netezza, andover 60 clients, they devour grabbed some early market share. They devour opted for an open architecture by creating an ODBC interface to their database so you can continue using your favorite reportingapplication. commerce Objects and Cognos on the commerce intelligence side and Informatica on the ETL side devour everything certified on the Vetrica engine. Some current skills will breathe erudite as the physicalmodel for a columnar architecture is slightly different than a typical relational model. Vertica provides a data administration application that lets modelers design the logical model and Verticaconverts and generates the physical model.
Birst differs from Vertica in the sense that it provides an automated solution with its own front suspension to its database and a data loader. Basically,you load up your data, and Birst figures out the optimal data model given its technology. The metadata is captured and automatically generates the user interface for the reporting piece, with sometailoring capabilities available. With its dashboard, Birst’s SaaS offering provides a consistent and central location for reporting output, making it easily accessible to many consumers.
With similar aspects to Vertica’s and Birst’s solutions, PivotLink’s SaaS delivery model includes a columnar architecture for their data store for performance, integrating data from various sources, and an optimized front-end to generate and deliver reports and dashboards for on-demand needs. With the front-end capabilities, PivotLink moreover provides report subscription and alert capabilities. Originally started as SeaTab in 1998, PivotLink has a solid customer list and several recent awards; PivotLink offers a stout SaaS BI option.
Other niche players worth keeping tabs on in this space are Information Builders, Oco and top-notch Data.
Depending on the organization, you might find the open architecture of Vertica or the turnkey aspects of Birst and PivotLink, or the perpendicular analytics of LucidEra more advantageous. Each promote jumpstart implementations and present attractive pricing options and each should breathe closely evaluated for your specific BI needs.The SaaS model and each of these solutions are not prerogative for everyone – if they conclude fit, they provide a nice advantage. reckon the following when evaluating SaaS:
Probably most notable is Omniture as a web analytics provider. Omniture is by far the market leader in web analytics (maybe except forWebTrends and/or NetRatings). They provide management reports, ad hoc query and lately a suite of applications to abet test your website design. For instance, if you are thinking of changing your registration page, you can employ their suite to accomplish an A/B (or far more complicated multivariate) test and monitor the results.
Hitwise, Compete, and Coremetrics provide countrywide (and in some cases international) web analytics across organizations. You can employ these applications to competitively benchmarkyour website activity against your competitors. You can discern if you devour spikes at the very time as your competitors or if changes in your website stole page consumption.
To illustrate how Important analytics is to on-demand applications, VerticalResponse has recently overhauled the analytical component oftheir core email application. VerticalResponse is one of the leaders in providing marketing technology for small businesses. They now provide benchmarks and scorecards so their customers can evaluatethe effectiveness of their campaigns, offers, creative and overall approach. They can essentially rank their campaigns and strategies in order to expand response rates.
Maybe you can even reckon Panorama’s deal with Google Apps and Salesforce.com a hosted analytical application. Panorama has recently embedded their query technology into Google Spreadsheetsto abet accomplish Google Apps more Excel-like. foundation functionality is free and advanced functionality is only $29/year.
LucidEra is my final solution in this group, offering SaaS BI analytics for ERM and CRM needs, having available pre-built perpendicular solutions or more universal employ of their enterprise solution. Diverging a bit from the prior solutions, LucidEra provides connectors for data integration and targets the ease of setup and employ for their solutionto very specific CRM (salesfoce.com) and ERM (Oracle) focuses. dote most of the others, LucidEra’s executive team has a stout pedigree in the commerce intelligence and CRM arenas, and their direction has strengthened their position in the market.
I reckon the concept of pre-canned, hosted applications for marketing response, sales targeting, sales analysis or customer analytics may provide a huge value for organizations – especially ifthese applications devour plug-ins to favorite CRM and marketing automation tools dote Siebel, salesforce.com, RightNow and Unica.Summary Though there is noiseless hefty concern (for top-notch reason) about the ease of enterprise integration, quick changes and flexibility with commerce intelligence SaaS solutions, this wave will continueto expand as more customers opt for the model. I reckon that the infrastructure vendors will necessity to prove that time to market does decrease, that this is just not a total cost of ownership play andthat they can accomplish their information pervasive throughout the organization (possibly in near real-time) if necessity be. Regardless, the several recent acquisitions by the BI behemoths (IBM and Cognos,for one) devour altered the BI industry landscape, and the SaaS solutions mentioned in this article already devour several competitive advantages that will accomplish it difficult for the larger organizationsto snare up in the short term.
The issue with the application vendors is the long-term aspect of integrating their application with the leisure of the organization’s data. For instance, for user even behavior, many clients wind up extracting particular data from Omniture into their corporate BI systems. Is that trouble worthwhile or should they bring web analytics completely in house? Point applications on a hosted levelwill necessity to devour simple ways of integrating data with the enterprise so they don’t become just another island of analytics.
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