ETL-EXTRACT, TRANSFORM & LOAD TESTING

Size: px
Start display at page:

Download "ETL-EXTRACT, TRANSFORM & LOAD TESTING"

Transcription

1 ETL-EXTRACT, TRANSFORM & LOAD TESTING Rajesh Popli Manager (Quality), Nagarro Software Pvt. Ltd., Gurgaon, INDIA ABSTRACT Data is most important part in any organization. Data is backbone of any report and reports are the baseline on which all the vital management decisions are taken. Most of the companies are constructing their data warehouse to store and monitor real time data as well as historical data. Many organizations have distributed departments with different applications running on distributedtechnology.etl process came into picture in order to make a flawless integration between different data sources from different departments.etl tool will work as an integrator, extracting data from different sources; transforming it in preferred format based on the business transformation rules and loading it in cohesive DB known are Data Warehouse. This paper deals with ETL testing and its architecture. 1.INTRODUCTION Extract, Transform and Load(ETL) is a process to extract data, mostly from different types of systems, transform it into a structure that s more appropriate for reporting and analysis and finally load it into the database. Extract from Source-In this step we extract data from different internal and external sources, structured and/or unstructured. Plain queries are sent to the source systems, using native connections, message queuing, ODBC or OLE-DB middleware. The data will be put in a Staging Area (SA), usually with the same structure as the source. Transform the data-once the data is available in the Staging Area, it is all on one platform and one database. So we can easily join and union tables, filter and sort the data using specific attributes, pivot to another structure and make business calculations. In this step of the ETL process, we can check on data quality and cleans the data if necessary. After having all the data prepared, we can choose to implement slowly changing dimensions. In that case we want to keep track in our analysis and reports when attributes changes over time, for example a customer moves from one region to another. We need to set the validation at time of data transformation like Avoid the NULL values in the table, validate the data type as using Tiny instead of integer etc. Load into the data warehouse-finally, data is loaded into a data warehouse, usually into fact and dimension tables. From there the data can be combined, aggregated and loaded into data-marts or cubes* and stores in OLAP server. Any report generation, analysis is done on these. Cubes have pre aggregated values which make report generation faster. Multi dimensional data is logically represented by Cubes in data warehousing. The dimension and the data are represented by the edge and the body of the cube respectively. A cube typically includes the aggregations that are needed for business intelligence queries. 2.ETL ARCHITECTURE ETL also covers data profiling, data quality control, Data monitoring and cleansing, real-time and on demand data integration in a service oriented architecture (SOA), and metadata management. Data profiling and data quality control: Profiling the data, will give direct insight in the data quality of the source systems. It can display how many rows have missing or invalid values, or what the distribution is of the values in a specific column. Based on this knowledge, one can specify business rules in order to cleanse the data, or keep really bad data out of the data warehouse. By doing data profiling before designing your ETL process, you will be able to design a system that is robust and has a clear structure. Data Cleansing: It is a process of changing inconsistency and inaccuracies. Removing unwanted data and transforming inconsistent data into consistent format. Meta data management & ETL: Metadata is defined as data that describes other data. Metadata can be divided into two main types: structural and descriptive. Structural metadata describes the design structure and their specifications. This type of metadata describes the containers of data within a database. Descriptive metadata describes instances of application data. This is the type of metadata that is traditionally spoken of and described as data about the data.

2 A third type is sometime identified called Administrative metadata. Administrative metadata provides information that helps to manage other information, such as when and how a resource was created, file types and other technical information. Figure 1: ETL Architecture Metadata makes it easier to retrieve, use, or manage information resources by providing users with information that adds context to the data they re working with. Metadata can describe information at any level of aggregation, including collections, single resources, or component part of a single resource. Metadata can be embedded into a digital object or can be stored separately. Web pages contain metadata called metatags. Therefore, metadata is information about how data is extracted, and how it may be transformed. It is also about indexing and creating pointers into data. Database design is all about defining metadata schemas. Meta data can be stored either internally, in the same file as the data, or externally, in a separate area. If the data is stored internally, the metadata is together with the data, making it more easily accessible to view or change. However, this method creates high redundancy. If metadata is stored externally, the searches can become more efficient. There is no redundancy but getting to this metadata may be a little more technical. All the metadata is stored in a data dictionary or a system catalog. The data dictionary is most typically an external document that is created in a spreadsheet type

3 of document that stores the conceptual design ideas for the database schema. The data dictionary also contains the general format that the data, and in effect the metadata, should be. Metadata is an essential aspect to database design, it allows for increased processing power, due to the fact that it can help create pointers and indexes. Information about all the data that is processed, from sources to targets by transformations, is often put into a metadata repository; a database containing all the metadata. The entire ETL process can be managed with metadata management, for example one can query how a specific target attribute is built-up in the ETL process, called data lineage. Or, you want to know what the impact of a change will be, for example the size of the order identifier (id) is changed, and in which ETL steps this attribute plays a role. 3. CHALLENGES IN ETL PROCESS ETL processes can involve considerable complexity, and significant operational problems can occur with improperly designed ETL systems. The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifications. This leads to an amendment of validation rules explicitly and implicitly implemented in the ETL process. Data warehouses are typically assembled from a variety of data sources with different formats and purposes. As such, ETL is a key process to bring all the data together in a standard, homogeneous environment. Design analysts should establish the scalability of an ETL system across the lifetime of its usage. This includes understanding the volumes of data that must be processed within service level agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data warehouses with tens of terabytes of data. Increasing volumes of data may require designs that can scale from daily batch to multiple-day micro batch to integration with message queues or real-time change-data capture for continuous transformation and update. 4. PERFORMANCE ETL vendors benchmark their record-systems at multiple TB (terabytes) per hour (or ~1 GB per second) using powerful servers with multiple CPUs, multiple hard drives, multiple gigabit-network connections, and lots of memory. The fastest ETL record is currently held by Syncsort, Vertica and HP at 5.4TB in under an hour, which is more than twice as fast as the earlier record held by Microsoft and Unisys. In real life, the slowest part of an ETL process usually occurs in the database load phase. Databases may perform slowly because they have to take care of concurrency, integrity maintenance, and indices. Thus, for better performance, it may make sense to employ: Direct Path Extract method or bulk unload whenever is possible (instead of querying the database) to reduce the load on source system while getting high speed extract Most of the transformation processing outside of the database Bulk load operations whenever possible. 5. ETL TESTING ETL Testing is used to test the entire ETL process using a tool or at table level with the help of test cases and Rules Mapping document. ETL or Data warehouse testing is categorized into four different engagements irrespective of technology or ETL tools used: New Data Warehouse Testing New DW is built and verified from scratch. Data input is taken from customer requirements and different data sources and new data warehouse is build and verified with the help of ETL tools. Migration Testing In this type of project customer will have an existing DW and ETL performing the job but they are looking to bag new tool in order to improve efficiency. Change Request In this type of project new data is added from different sources to an existing DW. Also, there might be a condition where customer needs to change their existing business rule or they might integrate the new rule. Report Testing Report are the end result of any Data Warehouse and the basic propose for which DW is build. Report must be tested by validating layout, data in the report and calculation.

4 6. ETL TESTING TECHNIQUES Verify that data is transformed correctly according to various business requirements and rules. Make sure that all projected data is loaded into the data warehouse without any data loss and truncation. Make sure that ETL application appropriately rejects, replaces with default values and reports invalid data. Make sure that data is loaded in data warehouse within prescribed and expected time frames to confirm improved performance and scalability. Verify that for rejected data proper error logs are generated with all details Verify NULL value fields Verify that duplicate data is not loaded Verify data integrity Verify that the counts in the source and target are matching Verify that the data is matching between source and target Verify that the transformed data is as per expectation Verify that the data is incrementally getting updated Verify that the foreign - primary key relations are preserved during the ETL Verify if there are any duplicates in the loaded data 7. ETL TESTING CHALLENGES Incompatible and duplicate data Loss of data during ETL process Unavailability of inclusive test bed Testers have no privileges to execute ETL jobs by their own Volume and complexity of data is very huge Fault in business process and procedures Trouble acquiring and building test data Missing business flow information 8. ETL Bugs User interface bugs/cosmetic bugs Related to GUI of application Navigation, spelling mistakes, font style, font size, colors, alignment. BVA Related bug Minimum and maximum values ECP Related bug Valid and invalid type Input/output bugs Valid values not accepted Invalid values accepted Calculation bugs Mathematical errors Final output is wrong Load condition bugs Does not allows multiple users Does not allows customer expected load Race condition bugs System crash & hang System cannot run client plat forms Version control bugs No logo matching No version information available This occurs usually in regression testing H/W bugs Device is not responding to the application

5 Source bugs Mistakes in help documents 9. CONCLUSION ETL stands for Extract Transformation and Load, It collect the different source data from Heterogeneous System (DB), Transform the data into Data warehouse (Target). The methodology primarily aims at identifying and mitigating existing data defects and general errors occurring prior to processing of data for analytics and reporting. REFERENCES: 1. Kimball, The Data Warehouse Lifecycle Toolkit, p Golfarelli /Rizzi, Data Warehouse Design, p The DW ETL toolkit, R. Kimball, J. Caserta, Wiley, 2004 Extraction-Transformation-Lo The DW ETL toolkit, R. Kimball, J. Caserta, Wiley, 2004 The DW ETL toolkit, R. Kimball, J. Caserta, Wiley, 2004 Pedersen, Aalborg University, /Teaching/DWML06/DWML06.html

Data Warehouse and Business Intelligence Testing: Challenges, Best Practices & the Solution

Data Warehouse and Business Intelligence Testing: Challenges, Best Practices & the Solution Warehouse and Business Intelligence : Challenges, Best Practices & the Solution Prepared by datagaps http://www.datagaps.com http://www.youtube.com/datagaps http://www.twitter.com/datagaps Contact contact@datagaps.com

More information

IST722 Data Warehousing

IST722 Data Warehousing IST722 Data Warehousing Introducing ETL Michael A. Fudge, Jr. Recall: Kimball Lifecycle Objective: Define and explain the ETL components and subsystems What is ETL? ETL: 4 Major Operations 1. Extract the

More information

SAS BI Course Content; Introduction to DWH / BI Concepts

SAS BI Course Content; Introduction to DWH / BI Concepts 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

More information

OLAP (Online Analytical Processing) G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

OLAP (Online Analytical Processing) G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT OLAP (Online Analytical Processing) G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT OVERVIEW INTRODUCTION OLAP CUBE HISTORY OF OLAP OLAP OPERATIONS DATAWAREHOUSE DATAWAREHOUSE ARCHITECHTURE DIFFERENCE

More information

Chapter 5. Learning Objectives. DW Development and ETL

Chapter 5. Learning Objectives. DW Development and ETL 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)

More information

MDM and Data Warehousing Complement Each Other

MDM and Data Warehousing Complement Each Other 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

More information

High-Volume Data Warehousing in Centerprise. Product Datasheet

High-Volume Data Warehousing in Centerprise. Product Datasheet 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

More information

JOURNAL OF OBJECT TECHNOLOGY

JOURNAL OF OBJECT TECHNOLOGY 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,

More information

Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition

Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition Migrating a Discoverer System to Oracle Business Intelligence Enterprise Edition Milena Gerova President Bulgarian Oracle User Group mgerova@technologica.com Who am I Project Manager in TechnoLogica Ltd

More information

THE DATA WAREHOUSE ETL TOOLKIT CDT803 Three Days

THE DATA WAREHOUSE ETL TOOLKIT CDT803 Three Days Three Days Prerequisites Students should have at least some experience with any relational database management system. Who Should Attend This course is targeted at technical staff, team leaders and project

More information

The Data Warehouse ETL Toolkit

The Data Warehouse ETL Toolkit 2008 AGI-Information Management Consultants May be used for personal purporses only or by libraries associated to dandelon.com network. The Data Warehouse ETL Toolkit Practical Techniques for Extracting,

More information

Data Integration and ETL with Oracle Warehouse Builder

Data Integration and ETL with Oracle Warehouse Builder Oracle University Contact Us: 1.800.529.0165 Data Integration and ETL with Oracle Warehouse Builder Duration: 5 Days What you will learn This Data Integration and ETL with Oracle Warehouse Builder training

More information

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING 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 asistithod@gmail.com

More information

Building a Data Warehouse

Building a Data Warehouse Building a Data Warehouse With Examples in SQL Server EiD Vincent Rainardi BROCHSCHULE LIECHTENSTEIN Bibliothek Apress Contents About the Author. ; xiij Preface xv ^CHAPTER 1 Introduction to Data Warehousing

More information

SAP Data Services 4.X. An Enterprise Information management Solution

SAP Data Services 4.X. An Enterprise Information management Solution SAP Data Services 4.X An Enterprise Information management Solution Table of Contents I. SAP Data Services 4.X... 3 Highlights Training Objectives Audience Pre Requisites Keys to Success Certification

More information

Jet Data Manager 2012 User Guide

Jet Data Manager 2012 User Guide Jet Data Manager 2012 User Guide Welcome This documentation provides descriptions of the concepts and features of the Jet Data Manager and how to use with them. With the Jet Data Manager you can transform

More information

Establish and maintain Center of Excellence (CoE) around Data Architecture

Establish and maintain Center of Excellence (CoE) around Data Architecture Senior BI Data Architect - Bensenville, IL The Company s Information Management Team is comprised of highly technical resources with diverse backgrounds in data warehouse development & support, business

More information

POLAR IT SERVICES. Business Intelligence Project Methodology

POLAR IT SERVICES. Business Intelligence Project Methodology POLAR IT SERVICES Business Intelligence Project Methodology Table of Contents 1. Overview... 2 2. Visualize... 3 3. Planning and Architecture... 4 3.1 Define Requirements... 4 3.1.1 Define Attributes...

More information

Data Integration and ETL with Oracle Warehouse Builder

Data Integration and ETL with Oracle Warehouse Builder Oracle University Contact Us: +27 (0)11 319-4111 Data Integration and ETL with Oracle Warehouse Builder Duration: 5 Days What you will learn Participants learn to load data by executing the mappings or

More information

Practical meta data solutions for the large data warehouse

Practical meta data solutions for the large data warehouse 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

More information

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya Chapter 6 Basics of Data Integration Fundamentals of Business Analytics Learning Objectives and Learning Outcomes Learning Objectives 1. Concepts of data integration 2. Needs and advantages of using data

More information

Introduction to Datawarehousing

Introduction to Datawarehousing 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

More information

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA 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,

More information

B.Sc (Computer Science) Database Management Systems UNIT-V

B.Sc (Computer Science) Database Management Systems UNIT-V 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

More information

BUSINESSOBJECTS DATA INTEGRATOR

BUSINESSOBJECTS DATA INTEGRATOR 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

More information

Integrate and Deliver Trusted Data and Enable Deep Insights

Integrate and Deliver Trusted Data and Enable Deep Insights 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

More information

SimCorp Solution Guide

SimCorp Solution Guide SimCorp Solution Guide Data Warehouse Manager For all your reporting and analytics tasks, you need a central data repository regardless of source. SimCorp s Data Warehouse Manager gives you a comprehensive,

More information

SQL Server 2012 Business Intelligence Boot Camp

SQL Server 2012 Business Intelligence Boot Camp 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

More information

Dimensional Modeling for Data Warehouse

Dimensional Modeling for Data Warehouse Modeling for Data Warehouse Umashanker Sharma, Anjana Gosain GGS, Indraprastha University, Delhi Abstract Many surveys indicate that a significant percentage of DWs fail to meet business objectives or

More information

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc.

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc. 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

More information

ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION

ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION EXECUTIVE SUMMARY Oracle business intelligence solutions are complete, open, and integrated. Key components of Oracle business intelligence

More information

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER SIX DATA Business Intelligence 2011 The McGraw-Hill Companies, All Rights Reserved 2 CHAPTER OVERVIEW SECTION 6.1 Data, Information, Databases The Business Benefits of High-Quality Information

More information

A Comprehensive Approach to Master Data Management Testing

A Comprehensive Approach to Master Data Management Testing A Comprehensive Approach to Master Data Management Testing Abstract Testing plays an important role in the SDLC of any Software Product. Testing is vital in Data Warehousing Projects because of the criticality

More information

ETL Process in Data Warehouse. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT

ETL Process in Data Warehouse. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT ETL Process in Data Warehouse G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT Outline ETL Extraction Transformation Loading ETL Overview Extraction Transformation Loading ETL To get data out of

More information

BUILDING OLAP TOOLS OVER LARGE DATABASES

BUILDING OLAP TOOLS OVER LARGE DATABASES BUILDING OLAP TOOLS OVER LARGE DATABASES Rui Oliveira, Jorge Bernardino ISEC Instituto Superior de Engenharia de Coimbra, Polytechnic Institute of Coimbra Quinta da Nora, Rua Pedro Nunes, P-3030-199 Coimbra,

More information

Microsoft Data Warehouse in Depth

Microsoft Data Warehouse in Depth 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

More information

An Introduction to Data Warehousing. An organization manages information in two dominant forms: operational systems of

An Introduction to Data Warehousing. An organization manages information in two dominant forms: operational systems of An Introduction to Data Warehousing An organization manages information in two dominant forms: operational systems of record and data warehouses. Operational systems are designed to support online transaction

More information

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram 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

More information

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28

www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 28 Data Warehousing - Essential Element To Support Decision- Making Process In Industries Ashima Bhasin 1, Mr Manoj Kumar 2 1 Computer Science Engineering Department, 2 Associate Professor, CSE Abstract SGT

More information

Oracle BI EE Implementation on Netezza. Prepared by SureShot Strategies, Inc.

Oracle BI EE Implementation on Netezza. Prepared by SureShot Strategies, Inc. 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

More information

Deriving Business Intelligence from Unstructured Data

Deriving Business Intelligence from Unstructured Data International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 9 (2013), pp. 971-976 International Research Publications House http://www. irphouse.com /ijict.htm Deriving

More information

Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach

Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach 2006 ISMA Conference 1 Sizing Logical Data in a Data Warehouse A Consistent and Auditable Approach Priya Lobo CFPS Satyam Computer Services Ltd. 69, Railway Parallel Road, Kumarapark West, Bangalore 560020,

More information

Oracle Warehouse Builder 10g

Oracle Warehouse Builder 10g Oracle Warehouse Builder 10g Architectural White paper February 2004 Table of contents INTRODUCTION... 3 OVERVIEW... 4 THE DESIGN COMPONENT... 4 THE RUNTIME COMPONENT... 5 THE DESIGN ARCHITECTURE... 6

More information

Data Warehousing and Data Mining

Data Warehousing and Data Mining Data Warehousing and Data Mining Part I: Data Warehousing Gao Cong gaocong@cs.aau.dk Slides adapted from Man Lung Yiu and Torben Bach Pedersen Course Structure Business intelligence: Extract knowledge

More information

Whitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE

Whitepaper. Data Warehouse/BI Testing Offering YOUR SUCCESS IS OUR FOCUS. Published on: January 2009 Author: BIBA PRACTICE YOUR SUCCESS IS OUR FOCUS Whitepaper Published on: January 2009 Author: BIBA PRACTICE 2009 Hexaware Technologies. All rights reserved. Table of Contents 1. 2. Data Warehouse - Typical pain points 3. Hexaware

More information

Module 3: Understanding Analysis Services Architecture

Module 3: Understanding Analysis Services Architecture Overview Module 3: Understanding Architecture Microsoft Data Warehousing Overview Components Metadata Repository Cube Options Architecture Office 2000 OLAP Components Microsoft Data Warehousing Overview

More information

Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes

Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes Five Steps to Integrate SalesForce.com with 3 rd -Party Systems and Avoid Most Common Mistakes This white paper will help you learn how to integrate your SalesForce.com data with 3 rd -party on-demand,

More information

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES MUHAMMAD KHALEEL (0912125) SZABIST KARACHI CAMPUS Abstract. Data warehouse and online analytical processing (OLAP) both are core component for decision

More information

Optimizing the Performance of the Oracle BI Applications using Oracle Datawarehousing Features and Oracle DAC 10.1.3.4.1

Optimizing the Performance of the Oracle BI Applications using Oracle Datawarehousing Features and Oracle DAC 10.1.3.4.1 Optimizing the Performance of the Oracle BI Applications using Oracle Datawarehousing Features and Oracle DAC 10.1.3.4.1 Mark Rittman, Director, Rittman Mead Consulting for Collaborate 09, Florida, USA,

More information

East Asia Network Sdn Bhd

East Asia Network Sdn Bhd 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

More information

Data Integration and ETL with Oracle Warehouse Builder NEW

Data Integration and ETL with Oracle Warehouse Builder NEW Oracle University Appelez-nous: +33 (0) 1 57 60 20 81 Data Integration and ETL with Oracle Warehouse Builder NEW Durée: 5 Jours Description In this 5-day hands-on course, students explore the concepts,

More information

Building Views and Charts in Requests Introduction to Answers views and charts Creating and editing charts Performing common view tasks

Building Views and Charts in Requests Introduction to Answers views and charts Creating and editing charts Performing common view tasks Oracle Business Intelligence Enterprise Edition (OBIEE) Training: Working with Oracle Business Intelligence Answers Introduction to Oracle BI Answers Working with requests in Oracle BI Answers Using advanced

More information

Data warehouse Architectures and processes

Data warehouse Architectures and processes Database and data mining group, Data warehouse Architectures and processes DATA WAREHOUSE: ARCHITECTURES AND PROCESSES - 1 Database and data mining group, Data warehouse architectures Separation between

More information

www.ducenit.com Analance Data Integration Technical Whitepaper

www.ducenit.com Analance Data Integration Technical Whitepaper Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring

More information

Agile Data Warehousing with SQL Server 2012

Agile Data Warehousing with SQL Server 2012 Agile Data Warehousing with SQL Server 2012 Davide Mauri SolidQ Global Sponsors: sp_help Davide Mauri Microsoft SQL Server MVP Works with SQL Server from 6.5 Works on BI from 2003 Specialized in Data Solution

More information

Chapter 3 - Data Replication and Materialized Integration

Chapter 3 - Data Replication and Materialized Integration Prof. Dr.-Ing. Stefan Deßloch AG Heterogene Informationssysteme Geb. 36, Raum 329 Tel. 0631/205 3275 dessloch@informatik.uni-kl.de Chapter 3 - Data Replication and Materialized Integration Motivation Replication:

More information

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 2. What is a Data warehouse a. A database application

More information

Business Intelligence for SUPRA. WHITE PAPER Cincom In-depth Analysis and Review

Business Intelligence for SUPRA. WHITE PAPER Cincom In-depth Analysis and Review Business Intelligence for A Technical Overview WHITE PAPER Cincom In-depth Analysis and Review SIMPLIFICATION THROUGH INNOVATION Business Intelligence for A Technical Overview Table of Contents Complete

More information

Technology in Action. Alan Evans Kendall Martin Mary Anne Poatsy. Eleventh Edition. Copyright 2015 Pearson Education, Inc.

Technology in Action. Alan Evans Kendall Martin Mary Anne Poatsy. Eleventh Edition. Copyright 2015 Pearson Education, Inc. Copyright 2015 Pearson Education, Inc. Technology in Action Alan Evans Kendall Martin Mary Anne Poatsy Eleventh Edition Copyright 2015 Pearson Education, Inc. Technology in Action Chapter 9 Behind the

More information

Data warehouse and Business Intelligence Collateral

Data warehouse and Business Intelligence Collateral 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

More information

Implementing a Data Warehouse with Microsoft SQL Server 2012 MOC 10777

Implementing a Data Warehouse with Microsoft SQL Server 2012 MOC 10777 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

More information

Business Benefits From Microsoft SQL Server Business Intelligence Solutions How Can Business Intelligence Help You? PTR Associates Limited

Business Benefits From Microsoft SQL Server Business Intelligence Solutions How Can Business Intelligence Help You? PTR Associates Limited Business Benefits From Microsoft SQL Server Business Intelligence Solutions How Can Business Intelligence Help You? www.ptr.co.uk Business Benefits From Microsoft SQL Server Business Intelligence (September

More information

Data Warehousing. Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de. Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1

Data Warehousing. Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de. Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1 Jens Teubner Data Warehousing Winter 2015/16 1 Data Warehousing Jens Teubner, TU Dortmund jens.teubner@cs.tu-dortmund.de Winter 2015/16 Jens Teubner Data Warehousing Winter 2015/16 13 Part II Overview

More information

University Data Warehouse Design Issues: A Case Study

University Data Warehouse Design Issues: A Case Study Session 2358 University Data Warehouse Design Issues: A Case Study Melissa C. Lin Chief Information Office, University of Florida Abstract A discussion of the design and modeling issues associated with

More information

When to consider OLAP?

When to consider OLAP? When to consider OLAP? Author: Prakash Kewalramani Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 03/10/08 Email: erg@evaltech.com Abstract: Do you need an OLAP

More information

Unlock your data for fast insights: dimensionless modeling with in-memory column store. By Vadim Orlov

Unlock your data for fast insights: dimensionless modeling with in-memory column store. By Vadim Orlov Unlock your data for fast insights: dimensionless modeling with in-memory column store By Vadim Orlov I. DIMENSIONAL MODEL Dimensional modeling (also known as star or snowflake schema) was pioneered by

More information

Data Testing on Business Intelligence & Data Warehouse Projects

Data Testing on Business Intelligence & Data Warehouse Projects Data Testing on Business Intelligence & Data Warehouse Projects Karen N. Johnson 1 Construct of a Data Warehouse A brief look at core components of a warehouse. From the left, these three boxes represent

More information

ETL Overview. Extract, Transform, Load (ETL) Refreshment Workflow. The ETL Process. General ETL issues. MS Integration Services

ETL Overview. Extract, Transform, Load (ETL) Refreshment Workflow. The ETL Process. General ETL issues. MS Integration Services ETL Overview Extract, Transform, Load (ETL) General ETL issues ETL/DW refreshment process Building dimensions Building fact tables Extract Transformations/cleansing Load MS Integration Services Original

More information

Data Warehouse: Introduction

Data Warehouse: Introduction Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of Base and Mining Group of base and data mining group,

More information

Course Outline. Module 1: Introduction to Data Warehousing

Course Outline. Module 1: Introduction to 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

More information

Data Warehouse Testing

Data Warehouse Testing 1 Data Warehouse Testing White Paper Authored for: 13th International Conference, QAI Author 1: Prasuna Potteti Date: 11-Sep-2011 Email: ppotteti@deloitte.com Deloitte Consulting India Private Limited

More information

Vendor: Brio Software Product: Brio Performance Suite

Vendor: Brio Software Product: Brio Performance Suite 1 Ability to access the database platforms desired (text, spreadsheet, Oracle, Sybase and other databases, OLAP engines.) yes yes Brio is recognized for it Universal database access. Any source that is

More information

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage

Moving Large Data at a Blinding Speed for Critical Business Intelligence. A competitive advantage Moving Large Data at a Blinding Speed for Critical Business Intelligence A competitive advantage Intelligent Data In Real Time How do you detect and stop a Money Laundering transaction just about to take

More information

Real-time Data Replication

Real-time Data Replication Real-time Data Replication from Oracle to other databases using DataCurrents WHITEPAPER Contents Data Replication Concepts... 2 Real time Data Replication... 3 Heterogeneous Data Replication... 4 Different

More information

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica

More information

BUSINESSOBJECTS DATA INTEGRATOR

BUSINESSOBJECTS DATA INTEGRATOR 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

More information

White Paper February 2010. IBM InfoSphere DataStage Performance and Scalability Benchmark Whitepaper Data Warehousing Scenario

White Paper February 2010. IBM InfoSphere DataStage Performance and Scalability Benchmark Whitepaper Data Warehousing Scenario White Paper February 2010 IBM InfoSphere DataStage Performance and Scalability Benchmark Whitepaper Data Warehousing Scenario 2 Contents 5 Overview of InfoSphere DataStage 7 Benchmark Scenario Main Workload

More information

Oracle Data Integrator for OWB Developers. Mark Rittman, Rittman Mead Consulting

Oracle Data Integrator for OWB Developers. Mark Rittman, Rittman Mead Consulting Oracle Data Integrator for OWB Developers Mark Rittman, Rittman Mead Consulting http://www.rittmanmead.com Who Am I? Oracle BI&W Architecture & Development Specialist The Rittman of Rittman Mead Consulting

More information

COURSE 20463C: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER

COURSE 20463C: IMPLEMENTING A DATA WAREHOUSE WITH MICROSOFT SQL SERVER 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

More information

Understanding the Value of In-Memory in the IT Landscape

Understanding the Value of In-Memory in the IT Landscape February 2012 Understing the Value of In-Memory in Sponsored by QlikView Contents The Many Faces of In-Memory 1 The Meaning of In-Memory 2 The Data Analysis Value Chain Your Goals 3 Mapping Vendors to

More information

Conventional BI Solutions Are No Longer Sufficient

Conventional BI Solutions Are No Longer Sufficient Exceeding Standards LOGO Mind Offers Quick Integration and Dynamic Reporting and Analysis! Provided by an avant-garde technology in its field, Logo Mind will carry your business one step ahead and offer

More information

Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities

Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities Vendor briefing Business Intelligence and Analytics Platforms Gartner 15 capabilities April, 2013 gaddsoftware.com Table of content 1. Introduction... 3 2. Vendor briefings questions and answers... 3 2.1.

More information

Extending Hyperion BI with the Oracle BI Server

<Insert Picture Here> Extending Hyperion BI with the Oracle BI Server Extending Hyperion BI with the Oracle BI Server Mark Ostroff Sr. BI Solutions Consultant Agenda Hyperion BI versus Hyperion BI with OBI Server Benefits of using Hyperion BI with the

More information

Implementing a Data Warehouse with Microsoft SQL Server 2012

Implementing a Data Warehouse with Microsoft SQL Server 2012 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

More information

Rocky Mountain Technology Ventures. Exploring the Intricacies and Processes Involving the Extraction, Transformation and Loading of Data

Rocky Mountain Technology Ventures. Exploring the Intricacies and Processes Involving the Extraction, Transformation and Loading of Data Rocky Mountain Technology Ventures Exploring the Intricacies and Processes Involving the Extraction, Transformation and Loading of Data 3/25/2006 Introduction As data warehousing, OLAP architectures, Decision

More information

Rational Reporting. Module 3: IBM Rational Insight and IBM Cognos Data Manager

Rational Reporting. Module 3: IBM Rational Insight and IBM Cognos Data Manager 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

More information

Developing Business Intelligence and Data Visualization Applications with Web Maps

Developing Business Intelligence and Data Visualization Applications with Web Maps Developing Business Intelligence and Data Visualization Applications with Web Maps Introduction Business Intelligence (BI) means different things to different organizations and users. BI often refers to

More information

Data Warehouse Overview. Srini Rengarajan

Data Warehouse Overview. Srini Rengarajan Data Warehouse Overview Srini Rengarajan Please mute Your cell! Agenda Data Warehouse Architecture Approaches to build a Data Warehouse Top Down Approach Bottom Up Approach Best Practices Case Example

More information

www.sryas.com Analance Data Integration Technical Whitepaper

www.sryas.com Analance Data Integration Technical Whitepaper Analance Data Integration Technical Whitepaper Executive Summary Business Intelligence is a thriving discipline in the marvelous era of computing in which we live. It s the process of analyzing and exploring

More information

DATA WAREHOUSING AND OLAP TECHNOLOGY

DATA WAREHOUSING AND OLAP TECHNOLOGY DATA WAREHOUSING AND OLAP TECHNOLOGY Manya Sethi MCA Final Year Amity University, Uttar Pradesh Under Guidance of Ms. Shruti Nagpal Abstract DATA WAREHOUSING and Online Analytical Processing (OLAP) are

More information

uncommon thinking ORACLE BUSINESS INTELLIGENCE ENTERPRISE EDITION ONSITE TRAINING OUTLINES

uncommon thinking ORACLE BUSINESS INTELLIGENCE ENTERPRISE EDITION ONSITE TRAINING OUTLINES OBIEE 11G: CREATE ANALYSIS AND DASHBOARDS: 11.1.1.7 DURATION: 4 DAYS Course Description: This course provides step-by-step instructions for creating analyses and dashboards, which compose business intelligence

More information

Data Warehouse Architecture

Data Warehouse Architecture Anwendungssoftwares a -Warehouse-, -Mining- und OLAP-Technologien Warehouse Architecture Overview Warehouse Architecture Sources and Quality Mart Federated Information Systems Operational Store Metadata

More information

AV-005: Administering and Implementing a Data Warehouse with SQL Server 2014

AV-005: Administering and Implementing a Data Warehouse with SQL Server 2014 AV-005: Administering and Implementing a Data Warehouse with SQL Server 2014 Career Details Duration 105 hours Prerequisites This career requires that you meet the following prerequisites: Working knowledge

More information

Course Outline: Course: Implementing a Data Warehouse with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning

Course Outline: Course: Implementing a Data Warehouse with Microsoft SQL Server 2012 Learning Method: Instructor-led Classroom Learning 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

More information

Designing a Dimensional Model

Designing a Dimensional Model Designing a Dimensional Model Erik Veerman Atlanta MDF member SQL Server MVP, Microsoft MCT Mentor, Solid Quality Learning Definitions Data Warehousing A subject-oriented, integrated, time-variant, and

More information

Cúram Business Intelligence Reporting Developer Guide

Cúram Business Intelligence Reporting Developer Guide IBM Cúram Social Program Management Cúram Business Intelligence Reporting Developer Guide Version 6.0.5 IBM Cúram Social Program Management Cúram Business Intelligence Reporting Developer Guide Version

More information

Data Warehouse design

Data Warehouse design Data Warehouse design Design of Enterprise Systems University of Pavia 21/11/2013-1- Data Warehouse design DATA PRESENTATION - 2- BI Reporting Success Factors BI platform success factors include: Performance

More information

Lection 3-4 WAREHOUSING

Lection 3-4 WAREHOUSING 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

More information

Implementing a Data Warehouse with Microsoft SQL Server 2012

Implementing a Data Warehouse with Microsoft SQL Server 2012 Course Code: M10777 Vendor: Microsoft Course Overview Duration: 5 RRP: 1,935 Implementing a Data Warehouse with Microsoft SQL Server 2012 Overview This 5-day instructor-led course describes how to implement

More information

CS2032 Data warehousing and Data Mining Unit II Page 1

CS2032 Data warehousing and Data Mining Unit II Page 1 UNIT II BUSINESS ANALYSIS Reporting Query tools and Applications The data warehouse is accessed using an end-user query and reporting tool from Business Objects. Business Objects provides several tools

More information