DOCSLIB.ORG

Introductions Training Agenda

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Introductions Training Agenda www.sungardhe.com <name of school> <date> <consultant name> Banner Performance Reporting and Analytics Introductions Name Title/function Job responsibilities Expectations www.sungardhe.com 2 Training Agenda Introductions Banner ODS Overview Banner ODS Architecture Banner ODS Concepts The Administrative User Interface Developing a Reporting Strategy Testing and Documentation Navigating through Banner ODS Questions and Concerns Report Writing Workshop www.sungardhe.com 3 1 Course Goals To provide the participants with an understanding of the Module of concentration and related information within the Banner Operational Data Store (ODS). To provide an understanding of the Metadata and Business Concept Diagrams so you are able to successfully support the ad hoc operational reporting needs as appropriate from Banner ODS. Create reports from Banner ODS using the Universities reporting tool. www.sungardhe.com 4 Course Objectives After completing this course, you will be able to: Understand what Banner ODS is and how it operates Understand how to use the Banner ODS data in conjunction with your reporting tool Navigate knowledgeably through the Banner ODS metadata Develop a reporting strategy www.sungardhe.com 5 www.sungardhe.com Information - The Common Component 2 Information: The Common Component Information is needed to address performance obligations Information is needed by: Executives : to monitor progress towards institutional priorities Administrators : to monitor daily operations IT : to provide enterprise intelligence and production reports IR : to monitor institutional trends, compliance Information and institutional intelligence required for measurable performance improvements www.sungardhe.com 7 Information: The Common Component In order to achieve their institutional mission, colleges and universities must define their business objectives to address and combat these pressures, such as: increase operational efficiency – maintain costs maximize funding – respond timely and appropriately to ensure revenue opportunities, i.e., governmental reporting, grant applications, etc. optimize accountability – legislative reporting, accreditation, budgetary reporting increase competitive positioning – institutional rankings, align curriculum to constituents’ demands The common component to these and other objectives is timely access to the information that is needed to achieve these objectives. www.sungardhe.com 8 Information: The Common Component Information needs to reach all levels of campus Data from lower levels must be transformed to upper levels Refined focus on supporting the actual business processes EXECUTIVES: Need visibility into progress towards our goals, objectives Performance “Am I achieving my goals?” data MANAGEMENT: Performance Need timely trends, summaries, analytics Management of our operations “How am I doing?” Trend, KNOWLEDGE WORKERS: Need to analyze trends and root summary Enterprise Data Warehouse data causes “Why is this happening?” STAFF: Detailed Need detailed reports in many data Operational Data Store formats and ad-hoc access “What is going on?” What do I need to do?” www.sungardhe.com 9 3 www.sungardhe.com Banner ODS Overview Banner ODS Overview BPRA Data Warehouse Solution Ensures Consistent Reporting Results Common Data Source Common Business Concepts Banner Operational Data Store (ODS): Ad-hoc querying and daily reporting Enterprise Data Warehouse: Historic, trend reporting and analytics • Data Snapshots Based EDW: History on Dates and Events Jan Feb Mar Apr May … • Data Sets ‘Frozen’ for Point in Time Tools Admin ODS: Today • Historic, Trend Reporting • Analytics • One Set of Data Finance HR EM Student • Data Changes Daily • Daily Reporting Advancement AR www.sungardhe.com 11 Banner ODS Overview Performance Reporting and Analytics Architecture Operational Enterprise Performance Data Store Data Warehouse Management Legacy Common Data Model Enterprise Denormalized Structures Star Schemas Digital Dashboard Banner ETL Operational ETL Enterprise Data Store Data Enrollment Warehouse Funnel Analytical Applications Other Reporting Reporting Tools Tools OLAP Business Intelligent Tools Channels Operational Reports Enterprise Reports Multi-Dimensional Trends/Forecasts Analysis www.sungardhe.com 12 4 Banner ODS Overview ODS and the Reporting Tool Oracle The Reporting Toolset Banner Data Report Output ODS Definition Report Definition Data Application Server End User www.sungardhe.com 13 Banner ODS Overview Simplifies information access Provides timely information to support all levels of management Improves information access performance Provides access to historical and summarized information Data refresh occurs at your specified interval Ensures consistent reporting results by providing a common data source and common business concepts www.sungardhe.com 14 Banner ODS Overview Data models and reports can be tailored to department-specific needs Uses Human Resources, Finance Fund/Orgn security Allows use of web-based reporting tools with graphical capabilities The ODS Administration component is web based Allows you to share solutions in an open environment www.sungardhe.com 15 5 Banner ODS Overview What is the ODS? Banner ODS uses reporting views to provide access to the data. Security Display rules Banner ODS tables and reporting views were constructed with the business needs of Higher Education administration in mind. www.sungardhe.com 16 Banner ODS Overview Why use the ODS? Ability to produce reports without the overhead of a transactional system. Built to address reporting queries not for efficiency of data capture. Provides for the freeze of data to accommodate point in time reporting. Most upgrades to the administrative system do not affect Banner ODS. www.sungardhe.com 17 Banner ODS Overview Key Features Banner ODS tables are constructed specifically for reporting. Banner ODS resides on a separate reporting Server. Banner ODS is populated from Banner as the source system using composite views. www.sungardhe.com 18 6 Banner ODS Overview Business Concepts Advancement Human Resources Common Finance Advancement Prospect Employee Advancement Rating Human Resource Event Annual Giving Budget Availability Ledger Application Institution Campaign Giving History Budget Detail Human Resource Faculty Organizational Entity Constituent Encumbrance Payroll Person Demographic Constituent Entity Endowment Distribution Position Person Role Designation Giving History Endowment Units Person Supplemental Gift Fixed Asset Relationship Organizational Constituent General Ledger Pledge Grant and Project Grant Ledger Invoice Payable Operating Ledger Purchasing Payable Transaction History www.sungardhe.com 19 Banner ODS Overview Business Concepts Financial Aid Accounts Receivable Student Financial Aid Application Financial Aid Award and Distribution Receivable Customer Financial Aid Fund Active Registration Receivable Revenue Admissions Application Advisor Student List Course Catalog Enrollment Management Faculty Assignment Government Reporting Recruitment Information Residential Life Schedule Offering Student Detail www.sungardhe.com 20 Banner ODS Overview Definitions ODS – Operational Data Store EDW – Enterprise Data Warehouse ETL – Extract, Transform and Load OWB – Oracle Warehouse Builder OLAP – On Line Analytical Processing Source – Where the data is coming from Target – Where the data is going to www.sungardhe.com 21 7 Banner ODS Overview Naming Conventions SunGard HE Banner Composite view – Ax_name PERSON_UID SunGard HE Banner Operational Data Store Database tables – MxT_name Reporting views – English name reports Subset of Reporting views – English name_SLOT www.sungardhe.com 22 www.sungardhe.com ODS Architecture ODS Architecture Populating the ODS Banner ODS is populated 3 ways. Initial Load Done during install Refresh Nightly? Weekly? Incremental Load Used when large amounts of data have been added to Banner Run the necessary Load Rule using the Administrative Tool www.sungardhe.com 24 8 ODS Architecture Initial Load Process Banner Operational Data Store ReportingReporting Tool Tool Reporting Views Person Employee Recruitment Op. Ledger Gift Academic Banner Data Tables View Security and Display Rules Operating Ledger Person View View Employee Academic Table Table Gift View Employee View Operating Person Gift Recruitment Ledger Table Table Table Recruitment View Academic Study Table Composite Views Composite Tables OWB www.sungardhe.com 25 ODS Architecture Incremental Refresh ReportingReporting Tool Tool Reporting Reporting View View Triggers Triggers Triggers Triggers Triggers Reporting Reporting Reporting View View View Change Change Change Table Table Table Security and Display Rules Change Change Table Table Composite Composite Table Table Composite Composite Composite Extract View Extract View Table Table Table Extract View Extract View Extract View PL/SQL PL/SQL PL/SQL ETL DELETE UPDATE Banner DBLINK ODS www.sungardhe.com 26 www.sungardhe.com Banner ODS Concepts 9 Banner ODS Concepts Composite and Slotted Tables Composite Table Include the main data that is extracted from Banner and stored in Banner ODS Slotted Table Store data values for a specific code related to a base table Optimizes the speed of queries Need to keep these synchronized www.sungardhe.com 28 Banner ODS Concepts List of Values Banner ODS has a database schema called ODSLOV which owns the list of value views. Most, but not all, of the views are based
Load more

Recommended publications
  • When and Why to Put What Data Where the Usage of ODS and Data Mart Platforms in a Well Architected Data Warehouse
    Data Warehousing When and Why to Put What Data Where The usage of ODS and data mart platforms in a well architected data warehouse By: Rob Armstrong Teradata Corporation When and Why to Put What Data Where Table of Contents Executive Summary 2 Executive Summary Operational Data Stores 3 Companies are trying to overcome two seemingly conflicting Data Warehouse Core 3 challenges. The first is how to integrate more historical Data Marts 4 Conclusion 5 and cross-functional analytic processes into their front-line About the Author 5 operations to take the most relevant actions against the most profitable customers. The second challenge is how to integrate more timely interactions and transactions into the back-end analytics so that strategy can be measured and fine tuned at an ever increasing pace. In order to balance these two objectives, companies are looking to push data to the process by incorpo- rating operational data stores and data marts into their data management architectures. Unfortunately, this constant pushing of data results in more data inconsistency, more data latency, and a more fractured understanding of the business Author’s note: This paper is a companion as a whole. Understanding the correct usage of the layers helps of the “Optimizing the Data Warehouse Layer” white paper. While this paper to alleviate some of the problems. However, the real solution describes the differences and functions of the ODS and data mart layers, the other is to integrate the data once and drive the process to the data. paper discusses how to best optimize your Having said that, and understanding the larger challenge of core data warehouse repository layer to minimize data movement, minimize data centralization will keep some on the distributed path, there are management costs, and increase reusability some guidelines to understanding how and when to use the and end-user accessibility.
    [Show full text]
  • Selecting a Data Warehouse Architecture David Septoff, SAS, Rockville, MD Kevin Simmons, SAS, Rockville, MD
    Selecting a Data Warehouse Architecture David Septoff, SAS, Rockville, MD Kevin Simmons, SAS, Rockville, MD Abstract Data Warehouse projects have provided end-users with after implementation begins. Waiting until after extraordinary returns on investment. Maintaining those implementation begins usually increases the amount of extraordinary returns requires an architecture that is rework. Most developers will admit that it is more time robust, flexible and scalable. Careful thought must be consuming and difficult to do rework than to do the job given to the selection of the data warehouse and right the first time. For this reason, there is little doubt decision-support architecture. A mistake in this area about the value of a carefully architected data warehouse often leads to the deployment of an application that and decision-support environment. There has been, cannot provide long-term support for the data and however, a great deal of debate surrounding the "best" analytical needs of the end-user community. architecture to deploy. The selection of the "best" architecture will be driven by such factors as: available Selecting the most appropriate architecture requires technology, business requirements, commitment to the examination of a number of factors. Some of those development effort, skill level of the developers, and the factors are rooted in technical constraints, while others availability of resources. are rooted in how end-users are organized. There are also political issues that must be noted during selection There are several architectures, which can be supported of the architecture. The first step in selecting an via the SAS ® System as well as products from other architecture is to understand your decision-support vendors.
    [Show full text]
  • Big Data Solutions and Applications
    P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management.
    [Show full text]
  • Memory Databases for Enterprise Applications
    Main Memory Databases for Enterprise Applications Jens Krueger, Florian Huebner, Johannes Wust, Martin Boissier, Alexander Zeier, Hasso Plattner Hasso Plattner Institute for IT Systems Engineering University of Potsdam Potsdam, Germany jens.krueger, florian.huebner, johannes.wust, martin.boissier, alexander.zeier, hasso.plattner @hpi.uni-potsdam.de { } Abstract—Enterprise applications are traditionally divided in While most of these variants see the solution in the redun- transactional and analytical processing. This separation was dant storage of data to achieve adequate response times for essential as growing data volume and more complex requests specific requests, the flexibility of the system is compromised were no longer performing feasibly on conventional relational databases. through the necessity of a predefined materialization strategies. While many research activities in recent years focussed on Furthermore this greater complexity increases total costs of the the optimization of such separation – particularly in the last system. decade – databases as well as hardware continued to develop. On The trend of Cloud Computing of recent years offers the one hand there are data management systems that organize additionally potential for saving costs. Both - providers as data column-oriented and thereby ideally fulfill the requirement profile of analytical requests. On the other hand significantly well as customers benefit as operational savings are passed more main memory is available to applications that allow to on to service users. As well the inherent flexibility of the store the complete compressed database of an enterprise in use of the cloud concept, including its customized payment combination with the equally significantly enhanced performance. model for clients to optimize their costing as only the really Both developments enable processing of complex analytical needed computer capacity is employed.
    [Show full text]
  • Modern Data Integration Paradigms
    Modern Data Integration Paradigms How Companies Enable Digital Transformation and Data-Driven Decisions Matthew D. Sarrel The Bloor Group This study was prepared by the Bloor Group, an independent market analysis firm, and sponsored by IRI, The CoSort Company. Additional sections at the end reflect the roles that the IRI Voracity platform can play in these paradigms. Table of Contents Introduction ……………………………………………………………………………………………………….. 3 Inherent Challenges of a Changing Technical Landscape ……………………………………. 4 Data Integration Platforms and Decision Criteria ………………………………………………. 5 Operational Data Store (ODS) / Enterprise Data Hub (EDH) ……………………………... 6 The ODS in More Detail …………………………………………………………………………………. 7 When the ODS Makes Sense …………………………………………………….............................. 8 ODS Example ………………………………………………………………………………………………. 9 Modern ODS Manifestations ………………………………………………………………………….. 10 Enterprise Data Warehouse (EDW) ……………………………………………………….…………. 11 (Slightly) Deeper Dive ………………………………………………………………............................ 13 EDW Uses and and Benefits ……………………………………………………………………………. 15 EDW Evolution ……………………………………………………………………………………………. 16 The Bottom Line on EDW ………………………………………………………………………………. 17 Logical Data Warehouse (LDW) …………………………………………………………………………. 18 LDW #trending …………………………………………………………………………………………... 19 LDW Details ………………………………………………………………………………………………... 22 Data Lake ……………………………………………………………………………………………………………. 24 Data Lake Architecture ………………………………………………………………………………….. 26 Benefits of the Data Lake ……………………………………………………………………………….
    [Show full text]
  • Introduction: Operational Data Store (ODS)
    Introduction: Operational Data Store (ODS) An Operational Data Store (ODS) is an integrated database of operational data. Its sources include Legacy Systems and it contains current or near term wise data. An ODS may contain 30 to 60 days of Data information. The goal of Operational Data Store is to integrated data information from different heterogeneous sources and homogeneous so it facilitates operational coverage in stipulated time period or near to it. Normally data information in the ODS are structured like the source systems, during integration the information is clean, De-Normalized and Business Rules applied ensure data information integrity. These integration processes is at lowest granular level and arise frequently throughout the day. Usually an ODS is not suitable for historical and trend analysis Data for future goal Decision Makers. An ODS is used for being temporary staging area for a Data Warehouse. Its main intension is to handle specific limit size data. An ODS does not contain static data but it contains data which are continuously updated through the Business Operations. ODS is particularly designed to perform relatively simply queries on small amount of data. It is contrast to Data Warehouse in which to perform complex queries on high amounts of data. ODS is short term memory and it stores recent and term wise data information. Data Warehouse It is a collection of integrated database which is designed for query and analysis of processing data. It is designed to consolidate data from multiple heterogeneous and homogeneous data sources. Data Warehouse allows integrating data from several external and internal sources and separate analysis from transaction workload.
    [Show full text]
  • Data Architecture Best Practices
    SQUARED Methods Harness the power of technology… White Paper Series Data Architecture Patterns & Guidelines Executive Summary Increasingly, businesses of all sizes thrive on data. Success within What this white paper is the Information Technology hinges on how well organizations about? collect, manage and extract value from data. At every step, organizations need to leverage accurate, timely and trusted data Data is lifeblood of organizations for decision-making. This is very challenging task since many of the today. It is critical to have applications and systems deployed within organization are based accurate, timely and trusted data on commercial IT products, custom developed components or in order to ensure effective legacy applications for which there is low understanding and decision-making. knowledge available. This means there is limited visibility into the data assets to business users. In order to transform data from its This paper discusses the best origin in applications to a credible element in the decision-making practice in creating a Data process, it is imperative that organizations understand data and are Architecture that supports all able to present it in a consistent, shared manner and make it types of data access, from transactional to reporting/KPI and available on demand to support the key decision support systems dashboards for organization’s business users White Paper Series Data Architecture Patterns & Guidelines Need for Enterprise Data Architecture In order to ensure that critical data assets are available to enterprise wide users in accurate, timely manner when needed, it is important to have a clear view of how to integrate these “islands” of data within organization.
    [Show full text]
  • Data Warehouse – the Utility’S Smart Grid Clearinghouse Version 3.0 April 26Th, 2010
    Data Warehouse – The Utility’s Smart Grid Clearinghouse Version 3.0 April 26th, 2010 1 Descriptions of Function This use case describes how the essential data elements are incorporated into The Utility’s smart grid clearinghouse 1.1 Function Name Data Warehouse 1.2 Function ID IECSA identification number of the function 1.3 Brief Description The data warehouse provides a persistent storage mechanism to integrate data from disparate systems for the purpose of reporting and analysis. 1.4 Narrative The data warehouse receives a regular feed of information from smart grid systems via the Operational Data Store (ODS). All of these data feeds are processed through an Extract, Transform and Load (ETL) mechanism, which perform the following operations as necessary: • Extract the data elements of interest • De-identify the data as needed • Verify the quality of the data • Report data problems • Reduce or aggregate the data if needed • Load the valid data into the warehouse Data Warehouse The Utility's Smart Grid Clearinghouse v3.1.doc.doc 1 8/17/20 Printed 8/17/2010 • Insure that all data is accounted for and none is duplicated The design of the database is optimized for analysis and reporting given the metrics required and the data segments identified during requirements gathering. After the warehouse is populated via ETL processes, reporting, analysis and modeling are be conducted through the use of business intelligence tools. Data Warehouse The Utility's Smart Grid Clearinghouse v3.1.doc.doc 2 8/17/20 Printed 8/17/2010 Figure 1-1 Context Diagram for Smart Grid Clearinghouse 1.5 Actor (Stakeholder) Roles Grouping (Community) , Group Description Actor Name Actor Type (person, device, system Actor Description etc.) Smart Grid System Information system that handles data from the ODS (data warehouse) in a Clearinghous reporting format specified by the DOE e ODS Sub-System Operational Data Store is a sub-system of The Utility’s data warehouse, which stores operational data i.e.
    [Show full text]
  • A Data Warehouse Design for a Typical University Information System
    Journal of Computer Science & Research (JCSCR) - ISSN 2227-328X JCSCR http://www.jcscr.com Journal of Computer Vol. 1, No. 6, Pages. 12-17, December 2012 Science & Research © The Authors A Data Warehouse Design for A Typical University Information System Youssef Bassil LACSC – Lebanese Association for Computational Sciences Registered under No. 957, 2011, Beirut, Lebanon Abstract Presently, large enterprises rely on database systems to manage their data and information. These databases are useful for conducting daily business transactions. However, the tight competition in the marketplace has led to the concept of data mining in which data are analyzed to derive effective business strategies and discover better ways in carrying out business. In order to perform data mining, regular databases must be converted into what so called informational databases also known as data warehouse. This paper presents a design model for building data warehouse for a typical university information system. It is based on transforming an operational database into an informational warehouse useful for decision makers to conduct data analysis, predication, and forecasting. The proposed model is based on four stages of data migration: Data extraction, data cleansing, data transforming, and data indexing and loading. The complete system is implemented under MS Access 2010 and is meant to serve as a repository of data for data mining operations. Keywords Data Warehouse, DBMS, Data Mining, Information System 1. Introduction Nowadays, almost every enterprise uses a database to store its vital data and information [1]. For instance, dynamic websites, accounting information systems, payroll systems, stock management systems all rely on internal databases as a container to store and manage their data.
    [Show full text]
  • Data Warehousing
    Data Warehousing Jens Teubner, TU Dortmund [email protected] Summer 2018 © Jens Teubner · Data Warehousing · Summer 2018 1 Part II Overview © Jens Teubner · Data Warehousing · Summer 2018 13 Data Warehouse So what is a data warehouse? A data warehouse is a database ! Typically a rather large one ! Think of multiple terabytes ∼ a few petabytes Data warehouses are tuned for analytics “Tune”? © Jens Teubner · Data Warehousing · Summer 2018 14 OLTP $ OLAP OLTP (Online Transaction Processing): Day-to-day business operations ! Mix of insert, update, delete, and read operations ! e.g., enter orders, maintain customer data, etc. System sometimes called operational data store (ODS) ! Up-to-date state of the data From a database perspective: ! Short-running operations ! Most queries known in advance ! Often point access, usually through indexes ! write access ; ACID principles © Jens Teubner · Data Warehousing · Summer 2018 15 OLTP $ OLAP OLAP (Online Analytical Processing): Provide data for reporting and decision making ! Mostly read-only access ! e.g., resource planning, marketing initiatives Need archive data; (slightly) outdated information might be okay ! Report changes over time ! Can use separate data store (non-ODS) From a database perspective: ! Long-running operations, mostly read-only ! Queries not known in advance, often complex (; indexing?) ! Might need to scan through large amounts of data ! Data is (almost) append-only. © Jens Teubner · Data Warehousing · Summer 2018 16 Transactional vs. Analytical Workloads OLTP ODS OLAP DM / DW Business Focus Operational Oper./Tact. Tactical Tact./Strat. DB Technology Relational Relational Cubic Relational Transaction Count Large Medium Small Small Transaction Size Small Medium Medium Large Transaction Time Short Medium Medium Long DB Size in GB 10–400 100–800 100–800 800–80,000 Data Modeling Trad.
    [Show full text]
  • From Open Data & Linked Data to Ontology
    From Open Data & Linked Data to Ontology example: http://www.disit.dinfo.unifi.it/siimobility.html 1 From Open Data to Linked Data To map Open Data into Linked Data: 1. Map the data to RDF: selecting/writing a domain ontology with standard terminology, convert data to RDF according to this ontology; 2. Link to external source: find links from the metadata to other repositories (Dbpedia, GeoNames, etc), 3. Curate the Linked Data: to ensure that the published information/link to other source are accurate. 2 First step useful tools (Map the data to RDF) Extract, Transform and Load (ETL): Process used in database and data warehousing that involves: • Extracting data from outside sources; • Transforming it to fit operational needs, which can include quality levels; • Loading it into the end target (database, operational data store or data warehouse). Useful tools to prepare data to RDF ​​translation 3 Pentaho Data Integration (Kettle) • Free, open source (LGPL) ETL (Extraction, Transformation and Loading) tool; • Powerful Extraction, Transformation and Loading (ETL) capabilities; • It use an innovative, metadata-driven approach; • Graphical, drag and drop design environment; • Scalable, standards-based architecture; • K.E.T.T.L.E, a recursive acronym for "Kettle Extraction, Transport, Transformation and Loading Environment". 4 Pentaho Data Integration (Kettle) Designed to: • Collect data from a variety of sources (extraction); • Move and modify data (transport and transform) while cleansing, denormalizing, aggregating and enriching it in the process; • Frequently (daily) store data (loading) in the final target destination, usually a large, dimensionally modelled database (or data warehouse). 5 Kettle’s 4 main programs • Spoon: graphically oriented end-user tool to model the flow of data from input through transformation to output (transformation); • Pan is a command line tool that executes transformations modelled with Spoon; • Chef: a graphically oriented end-user tool used to model jobs (transformations, FTP downloads etc.
    [Show full text]
  • Conceptual Design Model Using Operational Data Store (Codmods) for Developing Business Intelligence Applications
    IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.3, March 2011 161 Conceptual Design Model Using Operational Data Store (CoDMODS) for Developing Business Intelligence Applications Muhamad Shahbani † and Norshuhada Shiratuddin† †Graduate Department of Computer Science, College of Arts and Sciences, Universiti Utara Malaysia, Kedah, Malaysia. Summary heterogeneous sources differs significantly from Building a Business Intelligence (BI) application is very requirements analysis for a transactional information challenging as it is a young discipline and does not yet system [3]. offer well-established strategies and techniques for the BI is the process of gathering meaningful information developments process when compared to the software about the subject matter being researched [4]. In engineering discipline. Furthermore, information information system perspective, BI is a combination of requirements analysis for BI applications which integrate operational data with analytical tools to present complex data from heterogeneous sources differs significantly from and competitive information to planners and decision requirements analysis for a conventional information makers [5]. Furthermore, making decision based on system. Conceptual Design Model Operational Data Store comparing responses coming from different sources in (CoDMODS) to build BI application that focuses on operational data has always been difficult [6]. The operational information to support business operations is structures commonly used in BI architecture are an proposed. In this model, combination of community operational data store (ODS), data warehouse (DW) and interaction and data integration approach were used to data mart. To date, there are many topics researched in identify the requirements for developing BI application. DW structure (which support analytical information) but Furthermore, how the operational data store can be used fewer studies on ODS structure.
    [Show full text]