Traditional operational views of capacity planning is not the same as BI Capacity planning. I created this presentation to help establish a BI Infrastructure Capacity planning process.
The document discusses capacity planning for an ETL system. It explains that capacity planning involves identifying current and future computing needs to meet service level objectives over time. For ETL systems specifically, capacity planning is challenging due to varying job types, data volumes and frequencies. The document outlines steps for capacity planning including analyzing current usage, identifying future needs, and striking a balance between performance, utilization and costs. It also discusses tools and metrics that can be used like trend analysis, simulation and analytical modeling of metrics like CPU utilization, storage consumption and network traffic.
Service based / modeled IT operations demands that Infrastructure needs are catered to with minimal disruptions and loss of user experience. Demand and capacity management for a critical cog in IT / service design to ensure that the service / infrastructure is fully available to users through its lifecycle
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
This document provides definitions for various terms related to ERP (Enterprise Resource Planning). It defines terms such as ABC classification, abstract data, access paths, accuracy, action messages, activity accounting, activity analysis, and more. The definitions are brief and provide the essential meaning and context for each term.
MIS 17 Cross-Functional Enterprise SystemsTushar B Kute
These presentations are created by Tushar B Kute to teach the subject 'Management Information System' subject of TEIT of University of Pune.
http://www.tusharkute.com
The document discusses the development of a database system to track information for a program that provides food to pets. It will include tables to store data on volunteers, customers, and scheduling. The benefits will be having all the program's information organized in a central database that can easily answer questions. Costs will include database software and creation. The system will use a database on a computer with adequate storage and security to store and retrieve the necessary data in an easy to use manner.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
The document discusses capacity planning for an ETL system. It explains that capacity planning involves identifying current and future computing needs to meet service level objectives over time. For ETL systems specifically, capacity planning is challenging due to varying job types, data volumes and frequencies. The document outlines steps for capacity planning including analyzing current usage, identifying future needs, and striking a balance between performance, utilization and costs. It also discusses tools and metrics that can be used like trend analysis, simulation and analytical modeling of metrics like CPU utilization, storage consumption and network traffic.
Service based / modeled IT operations demands that Infrastructure needs are catered to with minimal disruptions and loss of user experience. Demand and capacity management for a critical cog in IT / service design to ensure that the service / infrastructure is fully available to users through its lifecycle
Business intelligence (BI) provides processes, technologies, and tools to help organizations analyze data and make better business decisions. BI technologies gather, store, analyze and provide access to enterprise data. This helps users understand what happened in the past, what is happening currently, and make plans to achieve desired future outcomes. BI provides a single point of access to information, timely answers to business questions, and allows all departments to use data for decision making. Key BI tools include dashboards, key performance indicators, graphical reporting, forecasting, and data visualization. These tools help analyze trends, customer behavior, market conditions, and support risk analysis and decision making.
This document provides definitions for various terms related to ERP (Enterprise Resource Planning). It defines terms such as ABC classification, abstract data, access paths, accuracy, action messages, activity accounting, activity analysis, and more. The definitions are brief and provide the essential meaning and context for each term.
MIS 17 Cross-Functional Enterprise SystemsTushar B Kute
These presentations are created by Tushar B Kute to teach the subject 'Management Information System' subject of TEIT of University of Pune.
http://www.tusharkute.com
The document discusses the development of a database system to track information for a program that provides food to pets. It will include tables to store data on volunteers, customers, and scheduling. The benefits will be having all the program's information organized in a central database that can easily answer questions. Costs will include database software and creation. The system will use a database on a computer with adequate storage and security to store and retrieve the necessary data in an easy to use manner.
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
Basics of Microsoft Business Intelligence and Data Integration TechniquesValmik Potbhare
The presentation used to get the conceptual understanding of Business Intelligence and Data warehousing applications. This also gives a basic knowledge about Microsoft's offerings on Business Intelligence space. Lastly but not least, it also contains some useful and uncommon SQL server programming best practices.
The document provides an overview of data warehousing and decision support systems. It discusses how data warehouses evolved from databases used for transaction processing to integrated databases designed for analysis and decision making. Key points include:
- Data warehouses store historical data from multiple sources to support analysis and decision making.
- They address limitations of transactional databases that are optimized for real-time queries rather than complex analysis.
- Effective data warehousing requires resolving data conflicts, documenting assumptions, and learning from mistakes in the implementation process.
Framework for Real time Analytics
Real time analytics provide insights very quickly by analyzing data with low latency (sub-second response times) and high availability. Real time analytics use technologies like MongoDB while batch analytics use Hadoop. Real time analytics applications include predictive modeling, user behavior analysis, and fraud detection. Traditional BI systems are not well suited for real time analytics due to rigid schemas, slow querying, and inability to handle high volumes and varieties of data. MongoDB allows for real time analytics by flexibly handling structured and unstructured data, scaling horizontally, and analyzing data in-place without lengthy batch processes.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
The document presents information on data warehousing. It defines a data warehouse as a repository for integrating enterprise data for analysis and decision making. It describes the key components, including operational data sources, an operational data store, and end-user access tools. It also outlines the processes of extracting, cleaning, transforming, loading and accessing the data, as well as common management tools. Data marts are discussed as focused subsets of a data warehouse tailored for a specific department.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
This document discusses various tools used for business analysis including reporting tools, managed query tools, executive information system tools, OLAP tools, data mining tools, and application development tools. It provides details on specific tools like Cognos Impromptu, Cactus, and FOCUS Fusion.
This document discusses different areas and methods of data processing. It covers two main areas: business data processing which involves large volumes of input/output data and limited calculations, and scientific data processing which involves limited input data but many calculations. The key data processing operations are recording, verifying, duplicating, classifying, calculating, summarizing, reporting, merging, storing, retrieving, and feedback. The main methods of processing data are batch processing, online processing, real-time processing, and distributed processing.
Jitendra Gupta has worked as a database analyst for HCL Technologies in Noida, India since 2013. He has experience managing over 200 Oracle databases, performing tasks such as monitoring, maintenance, cloning, and tuning. He also has experience working as an EMAT technology analyst for a GE project in Calgary, Canada, where he classified data, performed data analysis and reporting, and maintained the database. Jitendra holds certifications in Oracle DBA, Lean Six Sigma Yellow Belt, and was trained in embedded systems and industrial training in Hindustan Aeronautics Limited.
This document discusses data warehousing and OLAP (online analytical processing) technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data to support management decision making. It describes how data warehouses use a multi-dimensional data model with facts and dimensions to organize historical data from multiple sources for analysis. Common data warehouse architectures like star schemas and snowflake schemas are also summarized.
Framework for Real Time Analytics
This document discusses frameworks for real time analytics. It begins with an introduction that describes real time analytics as having low latency (sub-second response times) and high availability requirements, compared to batch analytics which have slower response times. The document then covers challenges of real time analytics like unpredictable and rapidly changing data sources and requirements. It provides examples of companies like MongoDB and Crittercism that enable real time analytics through flexible data models and powerful querying. Overall, the document advocates for using technologies like MongoDB to enable real time analysis of large, diverse and changing datasets.
The document discusses business intelligence systems (BIS). It defines BIS as using applications and technologies to collect, store, analyze and provide access to information to improve business processes and decision making. BIS benefits businesses by improving management and operations, enabling fraud detection and predicting the future. It is created using procured data and information, and combines skills, processes, technologies and practices to provide business insights for better decisions. The purpose of a BIS is to help executives and managers make more informed decisions. It facilitates the decision making process and efficient communication within a business.
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Analysing data analytics use cases to understand big data platformdataeaze systems
Get big picture of data platform architecture by knowing its purpose and problem it solves.
These slides take top down approach, starting with basic purpose of data platform ie. to serve analytics use cases. These slides categorise use cases and analyses their expectation from data platform.
This document provides an overview of key concepts in data warehousing including:
1. The need for data warehousing to consolidate data from multiple sources and support decision making.
2. Common data warehouse architectures like the two-tier architecture and data marts.
3. The extract, transform, load (ETL) process used to reconcile data and populate the data warehouse.
1) A data warehouse is a collection of data from multiple sources used to enable informed decision making. It contains data, metadata, dimensions, facts and aggregates.
2) The typical processes in a data warehouse are extract and load, data cleaning and transformation, user queries, and data archiving.
3) The key components that manage these processes are the load manager, warehouse manager and query manager. The load manager extracts, loads and does simple transformations on the data. The warehouse manager performs more complex transformations, integrity checks and generates summaries. The query manager directs user queries to the appropriate data.
Reconciling your Enterprise Data Warehouse to Source SystemsMethod360
Implementing and an enterprise BI system is a significant organization investment. Too many times the expected benefit of that investment isn’t realized due to inconsistent data between the organization’s operational and BI systems.
This webcast will explain several options to enable your organization to leverage its investment by providing options to reconcile the data from source operational systems to BI.
The document outlines the key concepts in systems analysis and design including:
1) It defines systems, analysis, and design and describes the role of the systems analyst in performing analysis and design to improve existing systems.
2) It describes the principal phases of the systems development life cycle including preliminary investigation, analysis, design, development, implementation, and ongoing maintenance.
3) It provides an overview of various tools used in systems analysis and design like entity relationship diagrams, data flow diagrams, documentation, and prototypes.
Data flow in Extraction of ETL data warehousingDr. Dipti Patil
The document discusses data flow processes in data warehousing including extraction, cleaning, conforming, and delivery.
Extraction involves reading data from source systems, connecting to data sources, scheduling data retrieval, capturing changed data, and dumping extracted data to disk. Cleaning ensures proper data types and structure and enforces data rules. Conforming loads dimensions, facts, and aggregations and handles delayed data. Delivery includes scheduling, job execution, recovery, and quality checks.
The document also discusses logical data mapping, which provides the foundation for metadata. It involves planning ETL processes, identifying data sources, and designing fact and dimension tables based on business rules and requirements. Components of a logical data map include table names, column names
SYSTEM DESIGN by Neeraj Bhandari (Surkhet Nepal)Neeraj Bhandari
The document outlines the system design process, which specifies how a system will meet the information needs of users as defined in system analysis. The system design consists of both logical and physical design activities. The physical design relates to input/output processes, while the architectural design emphasizes the system structure and behavior. The logical design abstractly represents data flows and inputs/outputs, often using models and diagrams. The system specification is the final output of design and specifies the hardware, software, database, user interface, and personnel requirements needed.
The complexities of meeting individual and program service goals require a systematic and comprehensive service delivery approach at both the organizational and front-line worker levels. This workshop will provide a clear definition of a bi-level service delivery system, its purpose, structure, and components. The necessity of partnerships at all levels, and how they are developed, will be emphasized.
O documento descreve um relatório de campo realizado por estudantes de engenharia civil sobre um levantamento altimétrico utilizando o método de nivelamento geométrico. O relatório apresenta a introdução, objetivos, materiais e localização do trabalho de campo, além de detalhar os procedimentos realizados para medir as cotas dos pontos ao longo de um perfil longitudinal através de leituras com nível topográfico.
Right-Sizing your SQL Server Virtual Machineheraflux
This document discusses "right-sizing" a SQL Server virtual machine (VM) by properly allocating CPU, memory, and storage resources. It explains that one size does not fit all workloads and inappropriate allocations can hurt performance. The presenter recommends profiling systems by collecting metrics from all stack components, analyzing workloads, and adjusting VM configurations based on the data. Regular reviews are also advised as workloads change. A new free beta tool is announced that will automate estimating the right-sized resource assignment for a SQL Server VM.
The document provides an overview of data warehousing and decision support systems. It discusses how data warehouses evolved from databases used for transaction processing to integrated databases designed for analysis and decision making. Key points include:
- Data warehouses store historical data from multiple sources to support analysis and decision making.
- They address limitations of transactional databases that are optimized for real-time queries rather than complex analysis.
- Effective data warehousing requires resolving data conflicts, documenting assumptions, and learning from mistakes in the implementation process.
Framework for Real time Analytics
Real time analytics provide insights very quickly by analyzing data with low latency (sub-second response times) and high availability. Real time analytics use technologies like MongoDB while batch analytics use Hadoop. Real time analytics applications include predictive modeling, user behavior analysis, and fraud detection. Traditional BI systems are not well suited for real time analytics due to rigid schemas, slow querying, and inability to handle high volumes and varieties of data. MongoDB allows for real time analytics by flexibly handling structured and unstructured data, scaling horizontally, and analyzing data in-place without lengthy batch processes.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
The document presents information on data warehousing. It defines a data warehouse as a repository for integrating enterprise data for analysis and decision making. It describes the key components, including operational data sources, an operational data store, and end-user access tools. It also outlines the processes of extracting, cleaning, transforming, loading and accessing the data, as well as common management tools. Data marts are discussed as focused subsets of a data warehouse tailored for a specific department.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
This document discusses various tools used for business analysis including reporting tools, managed query tools, executive information system tools, OLAP tools, data mining tools, and application development tools. It provides details on specific tools like Cognos Impromptu, Cactus, and FOCUS Fusion.
This document discusses different areas and methods of data processing. It covers two main areas: business data processing which involves large volumes of input/output data and limited calculations, and scientific data processing which involves limited input data but many calculations. The key data processing operations are recording, verifying, duplicating, classifying, calculating, summarizing, reporting, merging, storing, retrieving, and feedback. The main methods of processing data are batch processing, online processing, real-time processing, and distributed processing.
Jitendra Gupta has worked as a database analyst for HCL Technologies in Noida, India since 2013. He has experience managing over 200 Oracle databases, performing tasks such as monitoring, maintenance, cloning, and tuning. He also has experience working as an EMAT technology analyst for a GE project in Calgary, Canada, where he classified data, performed data analysis and reporting, and maintained the database. Jitendra holds certifications in Oracle DBA, Lean Six Sigma Yellow Belt, and was trained in embedded systems and industrial training in Hindustan Aeronautics Limited.
This document discusses data warehousing and OLAP (online analytical processing) technology. It defines a data warehouse as a subject-oriented, integrated, time-variant, and nonvolatile collection of data to support management decision making. It describes how data warehouses use a multi-dimensional data model with facts and dimensions to organize historical data from multiple sources for analysis. Common data warehouse architectures like star schemas and snowflake schemas are also summarized.
Framework for Real Time Analytics
This document discusses frameworks for real time analytics. It begins with an introduction that describes real time analytics as having low latency (sub-second response times) and high availability requirements, compared to batch analytics which have slower response times. The document then covers challenges of real time analytics like unpredictable and rapidly changing data sources and requirements. It provides examples of companies like MongoDB and Crittercism that enable real time analytics through flexible data models and powerful querying. Overall, the document advocates for using technologies like MongoDB to enable real time analysis of large, diverse and changing datasets.
The document discusses business intelligence systems (BIS). It defines BIS as using applications and technologies to collect, store, analyze and provide access to information to improve business processes and decision making. BIS benefits businesses by improving management and operations, enabling fraud detection and predicting the future. It is created using procured data and information, and combines skills, processes, technologies and practices to provide business insights for better decisions. The purpose of a BIS is to help executives and managers make more informed decisions. It facilitates the decision making process and efficient communication within a business.
This document provides an overview of data warehousing concepts including dimensional modeling, online analytical processing (OLAP), and indexing techniques. It discusses the evolution of data warehousing, definitions of data warehouses, architectures, and common applications. Dimensional modeling concepts such as star schemas, snowflake schemas, and slowly changing dimensions are explained. The presentation concludes with references for further reading.
Analysing data analytics use cases to understand big data platformdataeaze systems
Get big picture of data platform architecture by knowing its purpose and problem it solves.
These slides take top down approach, starting with basic purpose of data platform ie. to serve analytics use cases. These slides categorise use cases and analyses their expectation from data platform.
This document provides an overview of key concepts in data warehousing including:
1. The need for data warehousing to consolidate data from multiple sources and support decision making.
2. Common data warehouse architectures like the two-tier architecture and data marts.
3. The extract, transform, load (ETL) process used to reconcile data and populate the data warehouse.
1) A data warehouse is a collection of data from multiple sources used to enable informed decision making. It contains data, metadata, dimensions, facts and aggregates.
2) The typical processes in a data warehouse are extract and load, data cleaning and transformation, user queries, and data archiving.
3) The key components that manage these processes are the load manager, warehouse manager and query manager. The load manager extracts, loads and does simple transformations on the data. The warehouse manager performs more complex transformations, integrity checks and generates summaries. The query manager directs user queries to the appropriate data.
Reconciling your Enterprise Data Warehouse to Source SystemsMethod360
Implementing and an enterprise BI system is a significant organization investment. Too many times the expected benefit of that investment isn’t realized due to inconsistent data between the organization’s operational and BI systems.
This webcast will explain several options to enable your organization to leverage its investment by providing options to reconcile the data from source operational systems to BI.
The document outlines the key concepts in systems analysis and design including:
1) It defines systems, analysis, and design and describes the role of the systems analyst in performing analysis and design to improve existing systems.
2) It describes the principal phases of the systems development life cycle including preliminary investigation, analysis, design, development, implementation, and ongoing maintenance.
3) It provides an overview of various tools used in systems analysis and design like entity relationship diagrams, data flow diagrams, documentation, and prototypes.
Data flow in Extraction of ETL data warehousingDr. Dipti Patil
The document discusses data flow processes in data warehousing including extraction, cleaning, conforming, and delivery.
Extraction involves reading data from source systems, connecting to data sources, scheduling data retrieval, capturing changed data, and dumping extracted data to disk. Cleaning ensures proper data types and structure and enforces data rules. Conforming loads dimensions, facts, and aggregations and handles delayed data. Delivery includes scheduling, job execution, recovery, and quality checks.
The document also discusses logical data mapping, which provides the foundation for metadata. It involves planning ETL processes, identifying data sources, and designing fact and dimension tables based on business rules and requirements. Components of a logical data map include table names, column names
SYSTEM DESIGN by Neeraj Bhandari (Surkhet Nepal)Neeraj Bhandari
The document outlines the system design process, which specifies how a system will meet the information needs of users as defined in system analysis. The system design consists of both logical and physical design activities. The physical design relates to input/output processes, while the architectural design emphasizes the system structure and behavior. The logical design abstractly represents data flows and inputs/outputs, often using models and diagrams. The system specification is the final output of design and specifies the hardware, software, database, user interface, and personnel requirements needed.
The complexities of meeting individual and program service goals require a systematic and comprehensive service delivery approach at both the organizational and front-line worker levels. This workshop will provide a clear definition of a bi-level service delivery system, its purpose, structure, and components. The necessity of partnerships at all levels, and how they are developed, will be emphasized.
O documento descreve um relatório de campo realizado por estudantes de engenharia civil sobre um levantamento altimétrico utilizando o método de nivelamento geométrico. O relatório apresenta a introdução, objetivos, materiais e localização do trabalho de campo, além de detalhar os procedimentos realizados para medir as cotas dos pontos ao longo de um perfil longitudinal através de leituras com nível topográfico.
Right-Sizing your SQL Server Virtual Machineheraflux
This document discusses "right-sizing" a SQL Server virtual machine (VM) by properly allocating CPU, memory, and storage resources. It explains that one size does not fit all workloads and inappropriate allocations can hurt performance. The presenter recommends profiling systems by collecting metrics from all stack components, analyzing workloads, and adjusting VM configurations based on the data. Regular reviews are also advised as workloads change. A new free beta tool is announced that will automate estimating the right-sized resource assignment for a SQL Server VM.
Capacity planning is central to long-term organizational success and involves both long and short-term plans. There are different types of capacity including production, design, effective, and maximum capacities. Effective capacity is most impacted by factors related to facilities, products, processes, human resources, operations, and external forces. When determining capacity needs, organizations must consider economies and diseconomies of scale and develop alternatives such as building flexibility, differentiating product maturity, taking a holistic view, and smoothing requirements over time.
Hardware planning & sizing for sql serverDavide Mauri
This document provides an overview of hardware planning and sizing considerations for SQL Server. It discusses that performance is the typical requirement for relational database management systems. While high performance is expected, typical server hardware configurations often result in unbalanced systems that are not optimized. The document advocates for balanced systems with no single bottleneck. It provides guidance on evaluating CPU, memory, I/O capabilities and storage to ensure a system can handle peak resource consumption. Baseline testing is recommended to compare hardware performance.
The document discusses capacity planning, which involves determining the production capacity needed by an organization to meet changing demand. It covers determining current and future capacity needs, identifying options to modify capacity, and addressing imbalances between demand and capacity. Short-term adjustments and long-term responses are discussed. Models like present value analysis, aggregate planning, and decision trees can be useful for capacity planning. Economies of scale and concepts like efficiency and utilization are also summarized.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Database Development Process: A core aspect of software engineering is the subdivision of the development process into a series of phases, or steps, each of which focuses on one part of the development.
The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
This document provides information about getting fully solved assignments from an assignment help service. It includes contact information for the service via email or phone call, and provides an example of an assignment question from the subject of Business Intelligence and Tools. The assignment question asks students to define and explain similarity measures and methods for determining similarity between objects, as well as the differences between OLTP and OLAP systems. It then provides multiple additional questions on topics related to data extraction techniques, aspects of a business intelligence strategy, content management systems, end user segmentation, basic reporting and querying, and OLAP.
The document proposes a business intelligence (BI) system for ABC University using a data warehouse. It will follow the BI application release concept with 10 steps. The data warehouse will use a snowflake schema and Oracle for ETL and data mining. Informatica PowerCenter Express Enterprise was selected as the ETL tool. Oracle Data Miner will be used for data mining and provides a GUI and algorithms. The new system aims to provide a unified view of the university's data to help it stay competitive.
This document provides information about a student details management system (SDMS) software project created by a student. It includes an introduction describing the purpose of automating a student information system. It also includes sections on the objectives, theoretical background of databases, MySQL and Python, problem definition and analysis, and system design including database and code details. The overall aim is to develop a program with a graphical user interface to allow users to view and update student information stored in a centralized database.
The document describes the development of an employee management system. It discusses analyzing the data needed for the system and designing relational database tables to store employee information. This includes tables for employee details, work history, time records, salary, contacts, and holidays. The document also covers using C# and Microsoft Access to build the graphical user interface and connect it to the backend database. Functions are implemented to retrieve, add, update and delete employee records from the database.
This document describes the development of an employee management system. It discusses:
1) The programming tools used - Microsoft Access for the database and C# with .NET Framework for the application. Access allows constructing relational databases while C# provides an object-oriented interface.
2) The database design, which includes 6 tables - one main employee table and 5 child tables for additional employee details like work history, time records, and contact information. The tables are related through primary and foreign keys.
3) The development process, which first analyzed user needs, designed the database structure, then constructed the graphical user interface in the application to interact with the database according to its structure.
The document discusses Enterprise Resource Planning (ERP) systems. It describes the ERP architecture as using a client-server model with a relational database to store and process data. The ERP lifecycle involves definition, construction, implementation, and operation phases. Core ERP components manage accounting, production, human resources and other internal functions, while extended components provide external capabilities like CRM, SCM, and e-business. Proper implementation requires screening software, evaluating packages, analyzing process gaps, reengineering workflows, training staff, testing, and post-implementation support.
The document discusses the database development life cycle (DBLC), which follows a similar process to the systems development life cycle (SDLC). The DBLC involves gathering requirements, database analysis, design, implementation, testing and evaluation, and maintenance. It describes each stage in detail, including conceptual, logical, and physical data modeling during the design stage. The goal is to systematically plan and develop a database to meet requirements while ensuring completeness, integrity, flexibility, and usability.
Business Intelligence and Multidimensional DatabaseRussel Chowdhury
It was an honor that my employer assigned me to study with Business Intelligence that follows SQL Server Analysis
Services. Hence I started and prepared a presentation as a startup guide for a new learner.
* Thanks to all the contributions gathered here to prepare the doc.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example assignment for the subject of Business Intelligence & Tools. The assignment contains 6 multiple part questions covering topics like similarity measures, OLAP vs OLTP, data extraction techniques, BI strategy implementation, content management systems, and how a footwear company could implement and make best use of business intelligence solutions. Students are encouraged to contact the assignment help service by email or call for assistance with their assignments.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example of an assignment question from the subject of Business Intelligence & Tools. The assignment question covers topics like similarity measures, data extraction techniques, OLAP vs OLTP, content management systems, and how to plan and implement a business intelligence solution for a footwear company. Students are encouraged to email their assignment needs to the provided address or call in an emergency.
Data Warehouses & Deployment By Ankita dubeyAnkita Dubey
This document contains the notes about data warehouses and life cycle for data warehouse deployment project. This can be useful for students or working professionals to gain the basic knowledge about Data warehouses.
This document discusses business analytics and next-generation business intelligence tools. It describes how business analytics is used to gain insights from data to inform business decisions and optimize processes. It also explains that successful business analytics depends on data quality, skilled analysts, and organizational commitment to data-driven decision making. The document then profiles the capabilities of next-generation BI tools, including their support for top-down reporting, bottom-up analysis, self-service capabilities, and their ability to provide insights quickly through in-memory processing and interactive visualizations.
The document discusses the system development life cycle (SDLC), which includes various phases for developing and maintaining systems. The key phases are: system investigation, feasibility study, system analysis, system design, coding, testing, implementation, and maintenance. The feasibility study phase evaluates the technical, operational, economic, motivational, and schedule feasibility of a proposed system. The system analysis phase involves studying user requirements and the current system. System design then specifies how the new system will meet requirements through elements like data design, user interface design, and process design. This produces specifications for the system.