1 Chapter 6 Basics of Data Integration Fundamentals of Business Analytics
2 Learning Objectives and Learning Outcomes Learning Objectives 1. Concepts of data integration 2. Needs and advantages of using data integration 3. Introduction to common data integration approaches 4. Metadata types and sources 5. Introduction to data quality 6. Data profiling concepts and applications Learning Outcomes (a) To realize the importance of metadata (b) To understand data quality (c) To be able to perform scrubbing/cleaning of data (d) To be able to apply de-duplication (e) To be able to enhance the quality of data
3 Session Plan Lecture time : 90 minutes Q/A : 15 minutes
4 Agenda BI the process What is Data Integration? Challenges in Data Integration Technologies in Data Integration ETL: Extract, Transform, Load Various stages in ETL Need for Data Integration Advantages of using Data Integration Common approaches to Data Integration Metadata and its types Data Quality and Data Profiling concepts
5 BI The Process Data Integration Data Analysis Reporting
6 What Is Data Integration? Process of coherent merging of data from various data sources and presenting a cohesive/consolidated view to the user Involves combining data residing at different sources and providing users with a unified view of the data. Significant in a variety of situations; both commercial (e.g., two similar companies trying to merge their database) Scientific (e.g., combining research results from different bioinformatics research repositories)
7 Answer a Quick Question According to your understanding What are the problems faced in Data Integration?
8 Challenges in Data Integration Development challenges Translation of relational database to object-oriented applications Consistent and inconsistent metadata Handling redundant and missing data Normalization of data from different sources Technological challenges Various formats of data Structured and unstructured data Huge volumes of data Organizational challenges Unavailability of data Manual integration risk, failure
9 Technologies in Data Integration Integration is divided into two main approaches: Schema integration reconciles schema elements Instance integration matches tuples and attribute values The technologies that are used for data integration include: Data interchange Object Brokering Modeling techniques Entity-Relational Modeling Dimensional Modeling
10 Various Stages in ETL Cycle initiation Build reference data Data Mapping Extract (actual data) Validate Transform (clear, apply business rules) Data Staging Stage (load into staging tables) Audit reports (success/failure log) Publish (load into target tables) Archive Clean up
11 Various Stages in ETL DATA MAPPING DATA STAGING VALIDATE STAGE REFERENCE EXTRACT TRANSFORM ARCHIVE PUBLISH AUDIT REPORTS
12 Extract, Transform and Load What is ETL? Extract, transform, and load (ETL) in database usage (and especially in data warehousing) involves: Extracting data from different sources Transforming it to fit operational needs (which can include quality levels) Loading it into the end target (database or data warehouse) Allows to create efficient and consistent databases While ETL can be referred in the context of a data warehouse, the term ETL is in fact referred to as a process that loads any database. Usually ETL implementations store an audit trail on positive and negative process runs.
13 Data Mapping The process of creating data element mapping between two distinct data models It is used as the first step towards a wide variety of data integration tasks The various method of data mapping are Hand-coded, graphical manual Graphical tools that allow a user to draw lines from fields in one set of data to fields in another Data-driven mapping Evaluating actual data values in two data sources using heuristics and statistics to automatically discover complex mappings Semantic mapping A metadata registry can be consulted to look up data element synonyms If the destination column does not match the source column, the mappings will be made if these data elements are listed as synonyms in the metadata registry Only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between columns
14 Data Staging A data staging area is an intermediate storage area between the sources of information and the Data Warehouse (DW) or Data Mart (DM) A staging area can be used for any of the following purposes: Gather data from different sources at different times Load information from the operational database Find changes against current DW/DM values. Data cleansing Pre-calculate aggregates.
15 Data Extraction Extraction is the operation of extracting data from the source system for further use in a data warehouse environment. This the first step in the ETL process. Designing this process means making decisions about the following main aspects: Which extraction method would I choose? How do I provide the extracted data for further processing?
16 Data Extraction (cont ) The data has to be extracted both logically and physically. The logical extraction method Full extraction Incremental extraction The physical extraction method Online extraction Offline extraction
17 Data Transformation It is the most complex and, in terms of production the most costly part of ETL process. They can range from simple data conversion to extreme data scrubbing techniques. From an architectural perspective, transformations can be performed in two ways. Multistage data transformation Pipelined data transformation
18 Data Transformation LOAD INTO STAGIN G TABLE STAGE_01 TABLE VALIDATE CUSTOMER KEYS STAGE_02 TABLE CONVERT SOURCE KEY STAGE_03 TABLE INSERT INTO WAREHOUSE TARGET TABLE TO WAREHOUSE TABLE KEYS MULTISTAGE TRANSFORMATION
19 Data Transformation EXTERNAL TABLE VALIDATE CUSTOMER KEYS CONVERT SOURCE KEYS TO WAREHOUSE KEYS INSERT INTO WAREHOUSE TABLE TARGET TABLE PIPELINED TRANSFORMATION
20 Data Loading The load phase loads the data into the end target, usually the data warehouse (DW). Depending on the requirements of the organization, this process varies widely. The timing and scope to replace or append into the DW are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the DW.
21 Answer a Quick Question According to your understanding What is the need for Data Integration in corporate world?
22 Need for Data Integration It is done for providing data in a specific view as requested by users, applications, etc. What it means? DB2 The bigger the organization gets, the more data there is and the more data needs integration. SQL Unified view of data Increases with the need for data sharing. Oracle
23 Advantages of Using Data Integration Of benefit to decision-makers, who have access to important information from past studies What it means? DB2 Reduces cost, overlaps and redundancies; reduces exposure to risks Helps to monitor key variables like trends and consumer behaviour, etc. SQL Oracle Unified view of data
24 Common Approaches to Data Integration
25 Data Integration Approaches There are currently various methods for performing data integration. The most popular ones are: Federated databases Memory-mapped data structure Data warehousing
26 Data Integration Approaches Federated database (virtual database): Type of meta-database management system which transparently integrates multiple autonomous databases into a single federated database The constituent databases are interconnected via a computer network, geographically decentralized. The federated databases is the fully integrated, logical composite of all constituent databases in a federated database management system. Memory-mapped data structure: Useful when needed to do in-memory data manipulation and data structure is large. It s mainly used in the dot net platform and is always performed with C# or using VB.NET It s is a much faster way of accessing the data than using Memory Stream.
27 Data Integration Approaches Data Warehousing The various primary concepts used in data warehousing would be: ETL (Extract Transform Load) Component-based (Data Mart) Dimensional Models and Schemas Metadata driven
28 Answer a Quick Question According to your understanding What are the advantages and limitations of Data Warehouse?
29 Data Warehouse Advantage and Limitations ADVANTAGES Integration at the lowest level, eliminating need for integration queries. Runtime schematic cleaning is not needed performed at the data staging environment Independent of original data source Query optimization is possible. LIMITATIONS Process would take a considerable amount of time and effort Requires an understanding of the domain More scalable when accompanied with a metadata repository increased load. Tightly coupled architecture
30 Metadata and Its Types
31 Metadata and Its Types WHAT Business HOW Process TYPE Technical WHO, WHEN Application Data definitions, Metrics definitions, Subject models, Data models, Business rules, Data rules, Data owners/stewards, etc. Source/target maps, Transformation rules, data cleansing rules, extract audit trail, transform audit trail, load audit trail, data quality audit, etc. Data locations, Data formats, Technical names, Data sizes, Data types, indexing, data structures, etc. Data access history: Who is accessing? Frequency of access? When accessed? How accessed?, etc.
32 Data Quality and Data Profiling
33 Building Blocks of Data Quality Management Analyze, Improve and Control This methodology is used to encompass people, processes and technology. This is achieved through five methodological building blocks, namely: Profiling Quality Integration Enrichment Monitoring
34 Data Profiling Beginning the data improvement efforts by knowing where to begin. Data profiling (sometimes called data discovery or data quality analysis) helps to gain a clear perspective on the current integrity of data. It helps: Discover the quality, characteristics and potential problems Reduce the time and resources in finding problematic data Gain more control on the maintenance and management of data Catalog and analyze metadata The various steps in profiling include Metadata analysis Outline detection Data validation Pattern analysis Relationship discovery Statistical analysis Business rule validation
35 Data Profiling (cont ) Metadata profiling Typical type of metadata profiling are Domain: Conformation of data in column to the defined value or range Type: Alphabetic or numeric Pattern: The proper pattern Frequency counts Interdependencies: Within a table: Between tables: Data profiling analysis Column profiling Dependency profiling Redundancy profiling
36 Answer a Quick Question According to your understanding What is data quality and why it is important?
37 Data Quality Correcting, standardizing and validating the information Creating business rules to correct, standardize and validate your data. High-quality data is essential to successful business operations.
38 Data Quality (cont ) Data quality helps you to: Plan and prioritize data Parse data Standardize, correct and normalize data Verify and validate data accuracy Apply business rules Standardize and Transform Data The three components that ensure the quality and integrity of the data: Data rationalization Data standardization Data transformation
39 Answer a Quick Question What do you think are the major causes of bad data quality?
40 Causes of Bad Data Quality DURING PROCESS OF EXTRACTION Initial Conversion of Data Consolidation of System Manual Data Entry Batch Feeds Real Time Interfaces Effect of Bad Quality DATA DECAY DURING LOADING AND ARCHIVING Changes Not Captured System Upgrades Use of New Data Loss of Expertise Automation Process DURING DATA TRANSFORMATIONS Processing Data Data Scrubbing Data Purging
41 Data Quality in Data Integration Building a unified view of the database from the information. An effective data integration strategy can lower costs and improve productivity by ensuring the consistency, accuracy and reliability of data. Data integration enables to: Match, link and consolidate multiple data sources Gain access to the right data sources at the right time Deliver high-quality information Increase the quality of information
42 Data Quality in Data Integration Understand Corporate Information Anywhere in the Enterprise Data integration involves combining processes and technology to ensure an effective use of the data can be made. Data integration can include: Data movement Data linking and matching Data house holding
43 Popular ETL Tools
44 ETL Tools ETL process can be create using programming language. Open source ETL framework tools Clover.ETL Enhydra Octopus Pentaho Data Integration (also known as Kettle ) Talend Open Studio Popular ETL Tools Ab Initio Business Objects Data Integrator Informatica SQL Server 2005/08 Integration services
45 Summary please Ask a few participants of the learning program to summarize the lecture.
Data Integration and ETL Process Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, second
Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There
Anwendungssoftwares a -Warehouse-, -Mining- und OLAP-Technologien Warehouse Architecture Overview Warehouse Architecture Sources and Quality Mart Federated Information Systems Operational Store Metadata
White Paper Thirsting for Insight? Quench It With 5 Data Management for Analytics Best Practices. Contents Data Management: Why It s So Essential... 1 The Basics of Data Preparation... 1 1: Simplify Access
Page 1 of 8 ABOUT THIS COURSE This 5 day course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL Server
Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram Cognizant Technology Solutions, Newbury Park, CA Clinical Data Repository (CDR) Drug development lifecycle consumes a lot of time, money
JOURNAL OF OBJECT TECHNOLOGY Online at www.jot.fm. Published by ETH Zurich, Chair of Software Engineering JOT, 2008 Vol. 7, No. 8, November-December 2008 What s Your Information Agenda? Mahesh H. Dodani,
Course Outline: Course: Implementing a Data with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning Duration: 5.00 Day(s)/ 40 hrs Overview: This 5-day instructor-led course describes
META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING Ramesh Babu Palepu 1, Dr K V Sambasiva Rao 2 Dept of IT, Amrita Sai Institute of Science & Technology 1 MVR College of Engineering 2 firstname.lastname@example.org
Implementing a Data Warehouse with Microsoft SQL Server 2012 MOC 10777 Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing
DataFlux Data Management Studio DataFlux Data Management Studio provides the key for true business and IT collaboration a single interface for data management tasks. A Single Point of Control for Enterprise
Chapter 5 DW Development and ETL Learning Objectives Explain data integration and the extraction, transformation, and load (ETL) processes Basic DW development methodologies Describe real-time (active)
This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse 2014, implement ETL with SQL Server Integration Services, and
Submitted to: Service Definition Document for BI / MI Data Services Table of Contents 1. Introduction... 3 2. Data Quality Management... 4 3. Master Data Management... 4 3.1 MDM Implementation Methodology...
Lincoln Land Community College Capital City Training Center 130 West Mason Springfield, IL 62702 217-782-7436 www.llcc.edu/cctc Implement a Data Warehouse with Microsoft SQL Server 20463C; 5 days Course
Implementing a Data Warehouse with Microsoft SQL Server 2012 Module 1: Introduction to Data Warehousing Describe data warehouse concepts and architecture considerations Considerations for a Data Warehouse
Chapter 5 Foundations of Business Intelligence: Databases and Information Management 5.1 Copyright 2011 Pearson Education, Inc. Student Learning Objectives How does a relational database organize data,
Page 1 of 7 Overview This course describes how to implement a data warehouse platform to support a BI solution. Students will learn how to create a data warehouse with Microsoft SQL 2014, implement ETL
Data Integration with Talend Open Studio Robert A. Nisbet, Ph.D. Most college courses in statistical analysis and data mining are focus on the mathematical techniques for analyzing data structures, rather
CSPP 53017: Data Warehousing Winter 2013 Lecture 6 Svetlozar Nestorov Class News Homework 4 is online Due by Tuesday, Feb 26. Second 15 minute in-class quiz today at 6:30pm Open book/notes Last 15 minute
Course 20463C: Implementing a Data Warehouse with Microsoft SQL Server Length : 5 Days Audience(s) : IT Professionals Level : 300 Technology : Microsoft SQL Server 2014 Delivery Method : Instructor-led
Integration of Legacy Data (SLIMS) and Laboratory Information Management System (LIMS) through Development of a Data Warehouse Presenter N. Chikobi 2011.06.29 Structure of the presentation Background Preliminary
PRODUCTS BUSINESSOBJECTS DATA INTEGRATOR IT Benefits Correlate and integrate data from any source Efficiently design a bulletproof data integration process Accelerate time to market Move data in real time
A Scalable Data Transformation Framework using the Hadoop Ecosystem Raj Nair Director Data Platform Kiru Pakkirisamy CTO AGENDA About Penton and Serendio Inc Data Processing at Penton PoC Use Case Functional
Implementing a Data Warehouse with Microsoft SQL Server 2012 (70-463) Course Description Data warehousing is a solution organizations use to centralize business data for reporting and analysis. This five-day
Three Fundamental Techniques To Maximize the Value of Your Enterprise Data Prepared for Talend by: David Loshin Knowledge Integrity, Inc. October, 2010 2010 Knowledge Integrity, Inc. 1 Introduction Organizations
High-Volume Data Warehousing in Centerprise Product Datasheet Table of Contents Overview 3 Data Complexity 3 Data Quality 3 Speed and Scalability 3 Centerprise Data Warehouse Features 4 ETL in a Unified
Oracle9i Data Warehouse Review Robert F. Edwards Dulcian, Inc. Agenda Oracle9i Server OLAP Server Analytical SQL Data Mining ETL Warehouse Builder 3i Oracle 9i Server Overview 9i Server = Data Warehouse
COMMONWEALTH OF PENNSYLVANIA DEPARTMENT OF PUBLIC WELFARE INFORMATION TECHNOLOGY STANDARD Name Of Standard: Data Warehouse Standards Domain: Enterprise Knowledge Management Number: Category: STD-EKMS001
PRODUCTS BUSINESSOBJECTS DATA INTEGRATOR IT Benefits Correlate and integrate data from any source Efficiently design a bulletproof data integration process Improve data quality Move data in real time and
Data Integration and ETL Process Krzysztof Dembczyński Institute of Computing Science Laboratory of Intelligent Decision Support Systems Politechnika Poznańska (Poznań University of Technology) Software
Course Code: M20463 Vendor: Microsoft Course Overview Duration: 5 RRP: 2,025 Implementing a Data Warehouse with Microsoft SQL Server Overview This course describes how to implement a data warehouse platform
Lection 3-4 DATA WAREHOUSING Learning Objectives Understand d the basic definitions iti and concepts of data warehouses Understand data warehousing architectures Describe the processes used in developing
Implementing a Data Warehouse with Microsoft SQL Server MOC 20463 Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing
COURSE OUTLINE MOC 20463: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER MODULE 1: INTRODUCTION TO DATA WAREHOUSING This module provides an introduction to the key components of a data warehousing
Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components of a data warehousing solution and the highlevel considerations you must take into account
A McKnight Associates, Inc. White Paper: Effective Data Warehouse Organizational Roles and Responsibilities Numerous roles and responsibilities will need to be acceded to in order to make data warehouse
Data Management Solutions Horizon Software Solution s Data Management Solutions provide organisations with confidence in control of their data as they change systems and implement new solutions. Data is
THINK ORACLE BI. THINK KPI. THINK ORACLE BI. THINK KPI. MIGRATING FROM BUSINESS OBJECTS TO OBIEE KPI Partners is a world-class consulting firm focused 100% on Oracle s Business Intelligence technologies.
SQL Server 2012 Business Intelligence Boot Camp Length: 5 Days Technology: Microsoft SQL Server 2012 Delivery Method: Instructor-led (classroom) About this Course Data warehousing is a solution organizations
DIPARTIMENTO DI INGEGNERIA INFORMATICA AUTOMATICA E GESTIONALE ANTONIO RUBERTI Master of Science in Engineering in Computer Science (MSE-CS) Seminars in Software and Services for the Information Society
SAP Technical Brief SAP s for Enterprise Information Management SAP Data Services Objectives Integrate and Deliver Trusted Data and Enable Deep Insights Provide a wide-ranging view of enterprise information
Marco Lehmann Technical Sales Professional Integrating Netezza into your existing IT landscape 2011 IBM Corporation Agenda How to integrate your existing data into Netezza appliance? 4 Steps for creating
HYPERION MASTER DATA MANAGEMENT SOLUTIONS FOR IT POINT-AND-SYNC MASTER DATA MANAGEMENT 04.2005 Hyperion s new master data management solution provides a centralized, transparent process for managing critical
www.ijcsi.org 102 A Knowledge Management Framework Using Business Intelligence Solutions Marwa Gadu 1 and Prof. Dr. Nashaat El-Khameesy 2 1 Computer and Information Systems Department, Sadat Academy For
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012 Course Details Course Outline Module 1: Introduction to Data Warehousing This module provides an introduction to the key components
Global Data Integration with Autonomous Mobile Agents White Paper June 2002 Contents Executive Summary... 1 The Business Problem... 2 The Global IDs Solution... 5 Global IDs Technology... 8 Company Overview...
COMMONWEALTH OF PENNSYLVANIA DEPARTMENT OF HUMAN SERVICES INFORMATION TECHNOLOGY STANDARD Name Of Standard: Data Warehouse Standards Domain: Enterprise Knowledge Management Date Issued: 8/30/2002 Number:
Data warehouse and Business Intelligence Collateral Page 1 of 12 DATA WAREHOUSE AND BUSINESS INTELLIGENCE COLLATERAL Brains for the corporate brawn: In the current scenario of the business world, the competition
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012 OVERVIEW About this Course Data warehousing is a solution organizations use to centralize business data for reporting and analysis.
Page 1 of 10 Quick Links Home Worldwide Search Microsoft.com for: Go : Home Product Information How to Buy Editions Learning Downloads Support Partners Technologies Solutions Community Previous Versions
IT4E Schedule 13939 Gold Circle Omaha NE 68144 402-431-5432 Course Number 10777 For Sales Chris Reynolds 402-963-4465 email@example.com www.it4e.com For Sales Kathy Hall 402-963-4466 firstname.lastname@example.org Course
Course 20463:Implementing a Data Warehouse with Microsoft SQL Server Type:Course Audience(s):IT Professionals Technology:Microsoft SQL Server Level:300 This Revision:C Delivery method: Instructor-led (classroom)
Agile Business Intelligence Data Lake Architecture TABLE OF CONTENTS Introduction... 2 Data Lake Architecture... 2 Step 1 Extract From Source Data... 5 Step 2 Register And Catalogue Data Sets... 5 Step
Course 10777A: Implementing a Data Warehouse with Microsoft SQL Server 2012 Length: Audience(s): 5 Days Level: 200 IT Professionals Technology: Microsoft SQL Server 2012 Type: Delivery Method: Course Instructor-led
Proceedings of the Postgraduate Annual Research Seminar 2005 68 A Model-based Software Architecture for XML and Metadata Integration in Warehouse Systems Abstract Wan Mohd Haffiz Mohd Nasir, Shamsul Sahibuddin
The need for data integration tools exists in every company, small to large. Whether it is extracting data that exists in spreadsheets, packaged applications, databases, sensor networks or social media
CÔNG TY CỔ PHẦN TRƯỜNG CNTT TÂN ĐỨC TAN DUC INFORMATION TECHNOLOGY SCHOOL JSC LEARN MORE WITH LESS! Course 20463 Implementing a Data Warehouse with Microsoft SQL Server Length: 5 Days Audience: IT Professionals
CÔNG TY CỔ PHẦN TRƯỜNG CNTT TÂN ĐỨC TAN DUC INFORMATION TECHNOLOGY SCHOOL JSC LEARN MORE WITH LESS! Course 10777: Beta: Implementing a Data Warehouse with Microsoft SQL Server 2012 Length: 5 Days Audience:
Oracle Data Integrator Overview and Demo Maria Mundrova ETL Developer/DW Consultant BGOUG Spring Conference, 2009 Presentation Agenda Oracle Data Integrator What is ODI? ELT Approach Architecture Oracle
Data Warehousing Systems: Foundations and Architectures Il-Yeol Song Drexel University, http://www.ischool.drexel.edu/faculty/song/ SYNONYMS None DEFINITION A data warehouse (DW) is an integrated repository
Course 10777 : Implementing a Data Warehouse with Microsoft SQL Server 2012 Page 1 of 8 Implementing a Data Warehouse with Microsoft SQL Server 2012 Course 10777: 4 days; Instructor-Led Introduction Data
Applied Business Intelligence Iakovos Motakis, Ph.D. Director, DW & Decision Support Systems Intrasoft SA Agenda Business Drivers and Perspectives Technology & Analytical Applications Trends Challenges
K N I G H T S B R I D G E Practical meta data solutions for the large data warehouse PERFORMANCE that empowers August 21, 2002 ACS Boston National Meeting Chemical Information Division www.knightsbridge.com
Friction-free self-service BI solutions for everyone Scalable analytics on a modern architecture Apps and data source extensions with APIs Future white label, embed or integrate Power BI Deploy Intelligent
The Role of the BI Competency Center in Maximizing Organizational Performance Gloria J. Miller Dr. Andreas Eckert MaxMetrics GmbH October 16, 2008 Topics The Role of the BI Competency Center Responsibilites
Rational Reporting Module 3: IBM Rational Insight and IBM Cognos Data Manager 1 Copyright IBM Corporation 2012 What s next? Module 1: RRDI and IBM Rational Insight Introduction Module 2: IBM Rational Insight
Framework for Data warehouse architectural components Author: Jim Wendt Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 04/08/11 Email: email@example.com Abstract:
Data Virtualization for Agile Business Intelligence Systems and Virtual MDM To View This Presentation as a Video Click Here Agenda Data Virtualization New Capabilities New Challenges in Data Integration
SAS BI Course Content; Introduction to DWH / BI Concepts SAS Web Report Studio 4.2 SAS EG 4.2 SAS Information Delivery Portal 4.2 SAS Data Integration Studio 4.2 SAS BI Dashboard 4.2 SAS Management Console
Microsoft Data Warehouse in Depth 1 P a g e Duration What s new Why attend Who should attend Course format and prerequisites 4 days The course materials have been refreshed to align with the second edition
The following is an excerpt from a draft chapter of a new enterprise architecture text book that is currently under development entitled Enterprise Architecture: Principles and Practice by Brian Cameron
OLAP and OLTP AMIT KUMAR BINDAL Associate Professor Databases Databases are developed on the IDEA that DATA is one of the critical materials of the Information Age Information, which is created by data,
September, 21 2012 TRANSFORMING YOUR BUSINESS PROCESS INTO DATA MODEL Prasad Duvvuri AST Corporation Agenda First Step Analysis Data Modeling End Solution Wrap Up FIRST STEP It Starts With.. What is the
Course: Analyzing, Designing, and Implementing a Data Warehouse with Microsoft SQL Server 2014 Elements of this syllabus may be change to cater to the participants background & knowledge. This course describes
Oracle BI EE Implementation on Netezza Prepared by SureShot Strategies, Inc. The goal of this paper is to give an insight to Netezza architecture and implementation experience to strategize Oracle BI EE
1 What s New with Informatica Data Services & PowerCenter Data Virtualization Edition Kevin Brady, Integration Team Lead Bonneville Power Wei Zheng, Product Management Informatica Ash Parikh, Product Marketing
Best Practices for Deploying Managed Self-Service Analytics and Why Tableau and QlikView Fall Short Vijay Anand, Director, Product Marketing Agenda 1. Managed self-service» The need of managed self-service»
Winter 2009 Knowledge-Based Systems IS430 Data Warehousing Lesson 6 Mostafa Z. Ali firstname.lastname@example.org Lecture 2: Slide 1 Learning Objectives Understand the basic definitions and concepts of data warehouses
Practical Considerations for Real-Time Business Intelligence Donovan Schneider Yahoo! September 11, 2006 Outline Business Intelligence (BI) Background Real-Time Business Intelligence Examples Two Requirements
Paper SAS034-2014 What's New in SAS Data Management Nancy Rausch, SAS Institute Inc., Cary, NC; Mike Frost, SAS Institute Inc., Cary, NC, Mike Ames, SAS Institute Inc., Cary ABSTRACT The latest releases
Automated Data Ingestion Bernhard Disselhoff Enterprise Sales Engineer Agenda Pentaho Overview Templated dynamic ETL workflows Pentaho Data Integration (PDI) Use Cases Pentaho Overview Overview What we
1 B.Sc (Computer Science) Database Management Systems UNIT-V Business Intelligence? Business intelligence is a term used to describe a comprehensive cohesive and integrated set of tools and process used
Your consent to our cookies if you continue to use this website.