Tiber Solutions. Understanding the Current & Future Landscape of BI and Data Storage. Jim Hadley
|
|
- Alisha Shepherd
- 8 years ago
- Views:
Transcription
1 Tiber Solutions Understanding the Current & Future Landscape of BI and Data Storage Jim Hadley
2 Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing / Big Data thought leadership to corporations and government agencies. Deeply skilled in all facets of BI/DW/Big Data solutions star schema, ETL, BI, data visualization, data analytics, data architecture, information architecture, BI agile development methodology, and MDM/governance. Provide hands-on architecture, implementation, and coaching expertise within IT organizations from the CIO to the developers. Partner with business executives to co-invent optimal BI/DW applications to dramatically improve their business. 2
3 Tiber Solutions Customers Amethyst Technologies Amtrak Census Bureau Cognosante Defense Logistics Agency Department of Health and Human Services Department of the Treasury Fannie Mae Federal Depository Insurance Corporation Frontpoint Security Freddie Mac Graduate Management Admission Council Internal Revenue Service Military Health System National Institutes of Health Occupational Safety and Health Administration Office of the Comptroller of the Currency SAP Business Objects Securities and Exchange Commission 3
4 Agenda Business Intelligence Landscape - Concepts/Architectures - BI Tool vs. Data Visualization Tool Comparison Data Storage Landscape - Concepts/Architectures - Product Group Comparison 4
5 Business Intelligence Landscape Data Retrieval Facts Facts Success Factors: Retrieval Speed Ease of Access Data Presentation Success Factors: Visualization Richness and Diversity Delivery Options (e.g., Mobile, Push) 5
6 Business Intelligence Landscape Characteristics Business Intelligence Tools Data Visualization Tools Product Examples Strengths SAP Web Intelligence Cognos MicroStrategy Data Retrieval Dynamic, Complex Ad Hoc Queries Tableau Qliktech Qlikview TIBCO Spotfire Microsoft BI Stack Data Presentation Rich and Diverse Visualizations Limitations Limited Visualizations Limited Ad Hoc Capabilities Primary Use Ad Hoc Query Canned Reports Data Visualization Data Exploration Ad Hoc Query Capabilities Yes No (must be in cube) Leverages Semantic Layer For Data Retrieval Yes Partially Queries Data In Database Real-Time Yes No Requires Persisting Data Set In Cubes or Files No Yes Requires Developer Skills Semantic Layer (Universe) Yes Reports Some Cubes Yes Reports/Dashboards - No SAP Products SAP Web Intelligence SAP Dashboards - Requires Developer SAP Lumira Not nearly as mature SAP Explorer Limited visualizations 6
7 Business Intelligence Tool Architecture Business Terms Semantic Layer (Universe) Business Layer Folders Used to organize objects into logical groups (e.g., Customer Dim, Sales Measures) Objects Business terms are used to represent database columns (e.g., CUST_NM) or SQL formulas (e.g., SUM(REVENUE_AMT)- SUM(COST_AMT)) Technical Layer Connections Database connection parameters Tables/Columns Fact and Dimension tables and columns Joins Predefined joins between fact tables and dimension tables Contexts A group of joins. Each fact table should have a context SQL Facts Facts Assumptions: Data warehouse/data mart exists in which ETL processing has harmonized and combined data from multiple data sources. 7
8 Business Intelligence Tool Architecture Assumptions: Fact tables are at different levels of granularity (detail). 1-to-N fact tables can be queried with common dimensions. Objects Selected by End User Dims - Fiscal Year, Fiscal Quarter, Product Group Measures - Net Sales Amount, Forecast Amount Sales Context Related Tables and Columns Fiscal Year d_date.fiscal_yr Fiscal Quarter d_date.fiscal_qtr Product Group d_product.product_grp Net Sales Amount f_sales.net_sales_amt Forecast Amount f_forecast.forecast_amt Forecast Context Sales Query: SELECT d.fiscal_yr, d.fiscal_qtr, p.product_grp, SUM(f_sales.net_sales_amt) FROM d_date d, d_product p, f_sales f WHERE f.date_key=d.date_key AND f.product_key=p.product_key GROUP BY d.fiscal_yr, fiscal_qtr, p.product_grp Full Outer Join Forecast Query: SELECT d.fiscal_yr, d.fiscal_qtr, p.product_grp, SUM(f_forecast.forecast_amt) FROM d_date d, d_product p, f_sales f WHERE f.date_key=d.date_key AND f.product_key=vp.product_key GROUP BY d.fiscal_yr, d.fiscal_qtr, p.product_grp Facts Facts 8
9 Data Visualization Tool Architecture OLTP DW/DM Nightly SQL Load Nightly SQL Load Data Visualization Experience OLAP/File column names can be renamed to business terms. Easy for end users to drag/drop/ visualize data using multiple visualization styles. Data across cubes can be combined. Data Retrieval Observations: There is an assumption that the data is available, combinable, and clean (without any ETL or DQ). Data can be sourced from any database or file. Most products use OLAP cube technology to improve performance. OLAP cubes can be linked (joined) together, but they must have shared common dimensions and granularity. Data retrieval across OLAP cubes can be difficult. OLAP cubes are refreshed at night. Does not support dynamic ad hoc queries. IT is usually required to set up OLAP cubes on servers. OLAP cubes have practical size limits. Data Presentation Observations: Data visualization products support 100s of visualization styles. Tools are good at recommending visualizations based on data result set. Tools are very interactive. Easy to integrate visualizations together. Business users can successfully use the client tools without IT really. 9
10 Federated BI Architecture Use Case: How many passengers made refundable reservations and never traveled in 2014? Traditional BI/EDW Federated Bi 1. Query 2014 refundable reservation rows 25 million. Batch Real-time 2. Query 2014 travel rows 15 million. Batch Real-time 3. Left outer join the reservation query result set with the travel query result set based on common dimension data travel date, customer information, originating city, destination city, and flight number. Batch Real-time 4. Aggregate the joined result set rows counting all rows where travel information is null. Real-time Real-time Traditional BI/DW Federated BI Semantic Layer (Universe) Semantic Layer (Universe) Federated Architecture Data Warehouse Real-time Batch (Nightly) Reservations Travel ETL Reservations Travel 10
11 Data Storage Concepts/Architectures Columnar Data Storage Compression/Tokenization Parallelization In-Memory Performance Bottleneck: Reading data off of disk. 11
12 Columnar Data Storage Traditional RDBMS Columnar Data Storage SELECT col1, col2, col3 FROM table SELECT col1, col2, col3 FROM table Data is stored row-oriented on disk. All columns are read off of disk even if only a subset of columns are selected. Unselected columns are pruned after disk read. Optimized for row inserts Data is stored column-oriented on disk. Only selected columns are read off of disk. Unselected columns are not read off of disk. Optimized for data retrieval. Results: Less columns to read = Less disk to read = Faster data retrieval speeds Quantitative Results: 3 times faster 12
13 Compression/Tokenization Traditional RDBMS Compressed Databases State State V-List Alabama Alabama Alabama Alabama Alabama Alaska Alaska... Wyoming 10 million rows million rows 1 = Alabama 2 = Alaska 3 = Arizona 4 = Arkansas 5 = California 6 = Colorado 7 = Connecticut = Wyoming 50 bytes Data is stored on disk as it appears to the end user. Columns are byte-bound. Example: 50 bytes x 10 million rows = 500MB to read from disk. 6 bits (0.75 bytes) All distinct values are given a token representation. Tokens are stored on disk and not the actual data values. Columns are not byte-bound. Example: 2 6 = 64 values (50 values required) 6 bits or 0.75 bytes required 0.75 bytes x 10M rows = 7.5MB of disk read Results: Narrower columns = Less disk to read = Faster data retrieval speeds Quantitative Results: 66 times faster Total Quantitative Results: 3 (columnar) x 66 (compression) = 200 times faster 13
14 Parallelization Full-Table Scan Parallelized Full-Table Scan Parallelized Partition Scan Sales Table Sales Partition - 1 Sales Partition million rows Sales Partition - 2 Sales Partition - 3 Sales Partition - 4 Sales Partition - 5 Sales Partition - 6 Sales Partition - 7 Sales Partition Sales Partition Sales Partition Sales Partition Sales Partition Sales Partition Sales Partition - 8 Sales Partition Sales Partition - 9 Sales Partition The entire table is read sequentially. Example: 20 million rows are read sequentially in 200 seconds. Sales Partition - 10 The table s 10 partitions are read in parallel Example: 20 million rows are read in 10 parallel processes (2 million rows each) in 20 seconds. Sales Partition One partition is read (Where Year = 2012) Example: 2 million rows are read by one process (2 million rows) in 20 seconds. Results: Parallel partition reads = Faster data retrieval speeds Quantitative Results: 10 times faster Total Quantitative Results: 3 (columnar) x 66 (compression) x 10 (parallel) = 2,000 times faster Total quantitative results are rarely this significant and are for illustrative purposes only. 14
15 In-Memory In-memory processing is the trump card. However, in-memory processing is not cheap. Using column-oriented data storage and compression/tokenization techniques can significantly allow more data to fit into memory. Don t assume in-memory is the only solution. Example: Perceived Problem: My Honda is too slow Actual Problem: Driver only drives the car in first gear. Solution 1: Buy a Ferrari and drive it in first gear. Solution 2: Keep your Honda and learn how to use a clutch. 15
16 Data Storage Product Group Comparison Characteristics Traditional RDBMS Columnar In-Memory Hadoop Ecosystem Columnar Data Storage No Yes Sometimes No Compression/Tokenization No Yes Sometimes No Parallelization Yes Yes Yes Yes In-Memory No No Yes No Product Examples Oracle IBM DB2 SQL Server Amazon Redshift Vertica HBase EMC GreenPlum IBM DB2 BLU SAP HANA MemSQL HDFS/MapReduce HCatalog Cassandra 16
17 Data Storage Final Thoughts Columnar data storage, compression, parallelization, and in-memory processing ONLY address data retrieval performance. These techniques DO NOT address: - Harmonization of data sources (e.g., VA = Virginia = VIRGINIA, missing DC and Guam) - Data quality issues - Complexity of different data sets (e.g., many-to-many relationships, ratios, timing of data capture, etc.) - End users ability to intuitively and easily access, present, and understand information. 17
18 Questions Jim Hadley, President Phone:
Tiber Solutions. The DNA of a Successful Business Intelligence Effort. Jim Hadley
Tiber Solutions The DNA of a Successful Business Intelligence Effort Jim Hadley Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing thought leadership to corporations and
More informationTiber Solutions. Best Practices in Dashboard Design. Jim Hadley
Tiber Solutions Best Practices in Dashboard Design Jim Hadley Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing thought leadership to corporations and government agencies.
More informationTiber Solutions. The DNA of a Successful Business Intelligence Effort. Jim Hadley
Tiber Solutions The DNA of a Successful Business Intelligence Effort Jim Hadley Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing thought leadership to corporations and
More informationTiber Solutions. Designing Business Intelligence Applications and Dashboards for End-User Needs. Jim Hadley
Tiber Solutions Designing Business Intelligence Applications and Dashboards for End-User Needs Jim Hadley Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing thought leadership
More informationTiber Solutions. Designing and Developing Optimal Dashboard Applications. Jim Hadley
Tiber Solutions Designing and Developing Optimal Dashboard Applications Jim Hadley Tiber Solutions Founded in 2005 to provide Business Intelligence / Data Warehousing thought leadership to corporations
More informationArmanino McKenna LLP Welcomes You To Today s Webinar:
Armanino McKenna LLP Welcomes You To Today s Webinar: Business Intelligence Are You Data Rich & Information Poor? The presentation will begin in a few moments About the Presenter(s) John Horner, Director
More informationThe BIg Picture. Dinsdag 17 september 2013
The BIg Picture Dinsdag 17 september 2013 2 Agenda A short historical overview on BI Current Issues Current trends Future architecture First steps to this architecture 3 MIS/EIS Data Warehouse BI Multidimensional
More informationCost-Effective Business Intelligence with Red Hat and Open Source
Cost-Effective Business Intelligence with Red Hat and Open Source Sherman Wood Director, Business Intelligence, Jaspersoft September 3, 2009 1 Agenda Introductions Quick survey What is BI?: reporting,
More informationExploring the Synergistic Relationships Between BPC, BW and HANA
September 9 11, 2013 Anaheim, California Exploring the Synergistic Relationships Between, BW and HANA Sheldon Edelstein SAP Database and Solution Management Learning Points SAP Business Planning and Consolidation
More informationIn-Memory Data Management for Enterprise Applications
In-Memory Data Management for Enterprise Applications Jens Krueger Senior Researcher and Chair Representative Research Group of Prof. Hasso Plattner Hasso Plattner Institute for Software Engineering University
More informationManagement Consulting Systems Integration Managed Services WHITE PAPER DATA DISCOVERY VS ENTERPRISE BUSINESS INTELLIGENCE
Management Consulting Systems Integration Managed Services WHITE PAPER DATA DISCOVERY VS ENTERPRISE BUSINESS INTELLIGENCE INTRODUCTION Over the past several years a new category of Business Intelligence
More informationA Tour of the Zoo the Hadoop Ecosystem Prafulla Wani
A Tour of the Zoo the Hadoop Ecosystem Prafulla Wani Technical Architect - Big Data Syntel Agenda Welcome to the Zoo! Evolution Timeline Traditional BI/DW Architecture Where Hadoop Fits In 2 Welcome to
More informationTHE DEVELOPER GUIDE TO BUILDING STREAMING DATA APPLICATIONS
THE DEVELOPER GUIDE TO BUILDING STREAMING DATA APPLICATIONS WHITE PAPER Successfully writing Fast Data applications to manage data generated from mobile, smart devices and social interactions, and the
More informationUnderstanding 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 informationSumit Sarkar Real-time BO Universe to Cloud Data Sources Session #
Sumit Sarkar Real-time BO Universe to Cloud Data Sources Session # EXPERIENCE Talk to BI communities across SAP, Oracle, IBM, Microstrategy, Tableau, Tibco and Qlikview. Advocate for BI professionals at
More informationBussiness Intelligence and Data Warehouse. Tomas Bartos CIS 764, Kansas State University
Bussiness Intelligence and Data Warehouse Schedule Bussiness Intelligence (BI) BI tools Oracle vs. Microsoft Data warehouse History Tools Oracle vs. Others Discussion Business Intelligence (BI) Products
More informationSAP HANA SAP s In-Memory Database. Dr. Martin Kittel, SAP HANA Development January 16, 2013
SAP HANA SAP s In-Memory Database Dr. Martin Kittel, SAP HANA Development January 16, 2013 Disclaimer This presentation outlines our general product direction and should not be relied on in making a purchase
More information<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 informationToronto 26 th SAP BI. Leap Forward with SAP
Toronto 26 th SAP BI Leap Forward with SAP Business Intelligence SAP BI 4.0 and SAP BW Operational BI with SAP ERP SAP HANA and BI Operational vs Decision making reporting Verify the evolution of the KPIs,
More informationThe Technology Evaluator s Cheat Sheets. Business Intelligence & Analy:cs
The Technology Evaluator s Cheat Sheets Business Intelligence & Analy:cs Summary So1ware Stacks Full Stacks (DB + ETL Tools + Front- End So1ware) Back- End Stacks (DB and/or ETL Tools Only) Front- End
More information[Analysts: Dr. Carsten Bange, Larissa Seidler, September 2013]
BARC RESEARCH NOTE SAP BusinessObjects Business Intelligence with SAP HANA [Analysts: Dr. Carsten Bange, Larissa Seidler, September 2013] This document is not to be shared, distributed or reproduced in
More informationSAP HANA PLATFORM Top Ten Questions for Choosing In-Memory Databases. Start Here
PLATFORM Top Ten Questions for Choosing In-Memory Databases Start Here PLATFORM Top Ten Questions for Choosing In-Memory Databases. Are my applications accelerated without manual intervention and tuning?.
More informationAn Overview of SAP BW Powered by HANA. Al Weedman
An Overview of SAP BW Powered by HANA Al Weedman About BICP SAP HANA, BOBJ, and BW Implementations The BICP is a focused SAP Business Intelligence consulting services organization focused specifically
More informationAlejandro Vaisman Esteban Zimanyi. Data. Warehouse. Systems. Design and Implementation. ^ Springer
Alejandro Vaisman Esteban Zimanyi Data Warehouse Systems Design and Implementation ^ Springer Contents Part I Fundamental Concepts 1 Introduction 3 1.1 A Historical Overview of Data Warehousing 4 1.2 Spatial
More informationBig Data Architecture & Analytics A comprehensive approach to harness big data architecture and analytics for growth
MAKING BIG DATA COME ALIVE Big Data Architecture & Analytics A comprehensive approach to harness big data architecture and analytics for growth Steve Gonzales, Principal Manager steve.gonzales@thinkbiganalytics.com
More informationNative Connectivity to Big Data Sources in MSTR 10
Native Connectivity to Big Data Sources in MSTR 10 Bring All Relevant Data to Decision Makers Support for More Big Data Sources Optimized Access to Your Entire Big Data Ecosystem as If It Were a Single
More informationEmpowered Self-Service with SAP HANA and SAP Lumira. Dennis Scoville BI Evangelist Business Intelligence & Technology Honeywell Aerospace
Empowered Self-Service with SAP HANA and SAP Lumira Dennis Scoville BI Evangelist Business Intelligence & Technology Honeywell Aerospace Agenda About Honeywell Introduction Self-Service Business Intelligence
More informationBig Data Technologies Compared June 2014
Big Data Technologies Compared June 2014 Agenda What is Big Data Big Data Technology Comparison Summary Other Big Data Technologies Questions 2 What is Big Data by Example The SKA Telescope is a new development
More informationArchitecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing
Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Wayne W. Eckerson Director of Research, TechTarget Founder, BI Leadership Forum Business Analytics
More informationSAP BO 4.1 COURSE CONTENT
Data warehousing/dimensional modeling/ SAP BW 7.0 Concepts 1. OLTP vs. OLAP 2. Types of OLAP 3. Multi Dimensional Modeling Of SAP BW 7.0 4. SAP BW 7.0 Cubes, DSO s,multi Providers, Infosets 5. Business
More informationBig Data & QlikView. Democratizing Big Data Analytics. David Freriks Principal Solution Architect
Big Data & QlikView Democratizing Big Data Analytics David Freriks Principal Solution Architect TDWI Vancouver Agenda What really is Big Data? How do we separate hype from reality? How does that relate
More informationIn-Memory Analytics: A comparison between Oracle TimesTen and Oracle Essbase
In-Memory Analytics: A comparison between Oracle TimesTen and Oracle Essbase Agenda Introduction Why In-Memory? Options for In-Memory in Oracle Products - Times Ten - Essbase Comparison - Essbase Vs Times
More informationOptimizing 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 informationLost in Space? Methodology for a Guided Drill-Through Analysis Out of the Wormhole
Paper BB-01 Lost in Space? Methodology for a Guided Drill-Through Analysis Out of the Wormhole ABSTRACT Stephen Overton, Overton Technologies, LLC, Raleigh, NC Business information can be consumed many
More informationInnovative technology for big data analytics
Technical white paper Innovative technology for big data analytics The HP Vertica Analytics Platform database provides price/performance, scalability, availability, and ease of administration Table of
More informationReal Life Performance of In-Memory Database Systems for BI
D1 Solutions AG a Netcetera Company Real Life Performance of In-Memory Database Systems for BI 10th European TDWI Conference Munich, June 2010 10th European TDWI Conference Munich, June 2010 Authors: Dr.
More informationBI Market Dynamics and Future Directions
Inaugural Keynote Address Business Intelligence Conference Nov 19, 2011, New Delhi BI Market Dynamics and Future Directions Shashikant Brahmankar Head Business Intelligence & Analytics, HCL Content Evolution
More informationQlikView Business Discovery Platform. Algol Consulting Srl
QlikView Business Discovery Platform Algol Consulting Srl Business Discovery Applications Application vs. Platform Application Designed to help people perform an activity Platform Provides infrastructure
More informationSafe Harbor Statement
Safe Harbor Statement "Safe Harbor" Statement: Statements in this presentation relating to Oracle's future plans, expectations, beliefs, intentions and prospects are "forward-looking statements" and are
More informationDriving Peak Performance. 2013 IBM Corporation
Driving Peak Performance 1 Session 2: Driving Peak Performance Abstract We know you want the fastest performance possible for your deployments, and yet that relies on many choices across data storage,
More informationHadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time?
Hadoop and Data Warehouse Friends, Enemies or Profiteers? What about Real Time? Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de Disclaimer! These opinions are my own and do not necessarily
More informationIn-memory computing with SAP HANA
In-memory computing with SAP HANA June 2015 Amit Satoor, SAP @asatoor 2015 SAP SE or an SAP affiliate company. All rights reserved. 1 Hyperconnectivity across people, business, and devices give rise to
More informationApache Kylin Introduction Dec 8, 2014 @ApacheKylin
Apache Kylin Introduction Dec 8, 2014 @ApacheKylin Luke Han Sr. Product Manager lukhan@ebay.com @lukehq Yang Li Architect & Tech Leader yangli9@ebay.com Agenda What s Apache Kylin? Tech Highlights Performance
More informationSQL Server 2012 Performance White Paper
Published: April 2012 Applies to: SQL Server 2012 Copyright The information contained in this document represents the current view of Microsoft Corporation on the issues discussed as of the date of publication.
More informationKey Attributes for Analytics in an IBM i environment
Key Attributes for Analytics in an IBM i environment Companies worldwide invest millions of dollars in operational applications to improve the way they conduct business. While these systems provide significant
More informationIntegrating Apache Spark with an Enterprise Data Warehouse
Integrating Apache Spark with an Enterprise Warehouse Dr. Michael Wurst, IBM Corporation Architect Spark/R/Python base Integration, In-base Analytics Dr. Toni Bollinger, IBM Corporation Senior Software
More informationUnlock 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 informationOracle Database In-Memory The Next Big Thing
Oracle Database In-Memory The Next Big Thing Maria Colgan Master Product Manager #DBIM12c Why is Oracle do this Oracle Database In-Memory Goals Real Time Analytics Accelerate Mixed Workload OLTP No Changes
More informationWhen 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 informationChapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem:
Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Chapter 6 Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:
More information<Insert Picture Here> Enhancing the Performance and Analytic Content of the Data Warehouse Using Oracle OLAP Option
Enhancing the Performance and Analytic Content of the Data Warehouse Using Oracle OLAP Option The following is intended to outline our general product direction. It is intended for
More informationIBM Data Retrieval Technologies: RDBMS, BLU, IBM Netezza, and Hadoop
IBM Data Retrieval Technologies: RDBMS, BLU, IBM Netezza, and Hadoop Frank C. Fillmore, Jr. The Fillmore Group, Inc. Session Code: E13 Wed, May 06, 2015 (02:15 PM - 03:15 PM) Platform: Cross-platform Objectives
More informationIssues in Information Systems Volume 14, Issue 1, pp.331-338, 2013
SQL SERVER TABULAR MODEL: A STEP TOWARDS AGILE BI Stevan Mrdalj, Eastern Michigan University, smrdalj@emich.edu ABSTRACT As data volumes continue to increase the organizations are under constant pressure
More informationIl mondo dei DB Cambia : Tecnologie e opportunita`
Il mondo dei DB Cambia : Tecnologie e opportunita` Giorgio Raico Pre-Sales Consultant Hewlett-Packard Italiana 2011 Hewlett-Packard Development Company, L.P. The information contained herein is subject
More informationData warehousing/dimensional modeling/ SAP BW 7.3 Concepts
Data warehousing/dimensional modeling/ SAP BW 7.3 Concepts 1. OLTP vs. OLAP 2. Types of OLAP 3. Multi Dimensional Modeling Of SAP BW 7.3 4. SAP BW 7.3 Cubes, DSO's,Multi Providers, Infosets 5. Business
More information2015 Ironside Group, Inc. 2
2015 Ironside Group, Inc. 2 Introduction to Ironside What is Cloud, Really? Why Cloud for Data Warehousing? Intro to IBM PureData for Analytics (IPDA) IBM PureData for Analytics on Cloud Intro to IBM dashdb
More informationEnterprise Solutions. Data Warehouse & Business Intelligence Chapter-8
Enterprise Solutions Data Warehouse & Business Intelligence Chapter-8 Learning Objectives Concepts of Data Warehouse Business Intelligence, Analytics & Big Data Tools for DWH & BI Concepts of Data Warehouse
More informationData 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 informationFact Sheet In-Memory Analysis
Fact Sheet In-Memory Analysis 1 Copyright Yellowfin International 2010 Contents In Memory Overview...3 Benefits...3 Agile development & rapid delivery...3 Data types supported by the In-Memory Database...4
More informationSAP and Hortonworks Reference Architecture
SAP and Hortonworks Reference Architecture Hortonworks. We Do Hadoop. June Page 1 2014 Hortonworks Inc. 2011 2014. All Rights Reserved A Modern Data Architecture With SAP DATA SYSTEMS APPLICATIO NS Statistical
More informationPowerPivot Microsoft s Answer to Self-Service Reporting
PowerPivot Microsoft s Answer to Self-Service Reporting Microsoft s Latest Foray in the Business Intelligence Arena COLLABORATIVE WHITEPAPER SERIES In the last quarter of 2010, Microsoft first introduced
More informationORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process
ORACLE OLAP KEY FEATURES AND BENEFITS FAST ANSWERS TO TOUGH QUESTIONS EASILY KEY FEATURES & BENEFITS World class analytic engine Superior query performance Simple SQL access to advanced analytics Enhanced
More informationIBM Cognos 8 Business Intelligence Analysis Discover the factors driving business performance
Data Sheet IBM Cognos 8 Business Intelligence Analysis Discover the factors driving business performance Overview Multidimensional analysis is a powerful means of extracting maximum value from your corporate
More informationSAP Analytics Roadmap for Small and Midsize Companies. Kevin Chan, Director, Solutions Management @ SAP
SAP Analytics Roadmap for Small and Midsize Companies Kevin Chan, Director, Solutions Management @ SAP A WORLD OF ACCELERATING CHANGE An emerging middle class growing to 5B Data doubling every 18 months
More informationBy Makesh Kannaiyan makesh.k@sonata-software.com 8/27/2011 1
Integration between SAP BusinessObjects and Netweaver By Makesh Kannaiyan makesh.k@sonata-software.com 8/27/2011 1 Agenda Evolution of BO Business Intelligence suite Integration Integration after 4.0 release
More informationDevelopment of the Information Analysis System of the Ministry of Finance of Belarus
Development of the Information Analysis System of the Ministry of Finance of Belarus ASFR organizational and technical structure Data Processing (of the ) Local area network (LAN) Local area network (LAN)
More informationData Doesn t Communicate Itself Using Visualization to Tell Better Stories
SAP Brief Analytics SAP Lumira Objectives Data Doesn t Communicate Itself Using Visualization to Tell Better Stories Tap into your data big and small Tap into your data big and small In today s fast-paced
More informationIn-Memory Business Intelligence
In-Memory Business Intelligence Ranwood Paper April 2009 1 CONTENTS 1 Contents... 1-1 2 In-memory BI...... 2-2 3 In-Memory BI solutions and architecture... 3-5 4 Advantages of In-memory BI... 4-10 5 Disadvantages
More informationBig Data Analytics with IBM Cognos BI Dynamic Query IBM Redbooks Solution Guide
Big Data Analytics with IBM Cognos BI Dynamic Query IBM Redbooks Solution Guide IBM Cognos Business Intelligence (BI) helps you make better and smarter business decisions faster. Advanced visualization
More informationBusiness Intelligence, Data warehousing Concept and artifacts
Business Intelligence, Data warehousing Concept and artifacts Data Warehousing is the process of constructing and using the data warehouse. The data warehouse is constructed by integrating the data from
More informationMS 20467: Designing Business Intelligence Solutions with Microsoft SQL Server 2012
MS 20467: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Description: This five-day instructor-led course teaches students how to design and implement a BI infrastructure. The
More informationLeveraging BI Tools & HANA. Tracy Nguyen, North America Analytics COE April 15, 2016
Leveraging BI Tools & HANA Tracy Nguyen, North America Analytics COE April 15, 2016 Legal disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed
More informationUnderstanding Data Warehousing. [by Alex Kriegel]
Understanding Data Warehousing 2008 [by Alex Kriegel] Things to Discuss Who Needs a Data Warehouse? OLTP vs. Data Warehouse Business Intelligence Industrial Landscape Which Data Warehouse: Bill Inmon vs.
More informationIntroducing Oracle Exalytics In-Memory Machine
Introducing Oracle Exalytics In-Memory Machine Jon Ainsworth Director of Business Development Oracle EMEA Business Analytics 1 Copyright 2011, Oracle and/or its affiliates. All rights Agenda Topics Oracle
More informationNews and trends in Data Warehouse Automation, Big Data and BI. Johan Hendrickx & Dirk Vermeiren
News and trends in Data Warehouse Automation, Big Data and BI Johan Hendrickx & Dirk Vermeiren Extreme Agility from Source to Analysis DWH Appliances & DWH Automation Typical Architecture 3 What Business
More informationIST722 Data Warehousing
IST722 Data Warehousing Components of the Data Warehouse Michael A. Fudge, Jr. Recall: Inmon s CIF The CIF is a reference architecture Understanding the Diagram The CIF is a reference architecture CIF
More informationLEARNING SOLUTIONS website milner.com/learning email training@milner.com phone 800 875 5042
Course 20467A: Designing Business Intelligence Solutions with Microsoft SQL Server 2012 Length: 5 Days Published: December 21, 2012 Language(s): English Audience(s): IT Professionals Overview Level: 300
More informationSAP BusinessObjects Business Intelligence 4.1 One Strategy for Enterprise BI. May 2013
SAP BusinessObjects Business Intelligence 4.1 One Strategy for Enterprise BI May 2013 SAP s Strategic Focus on Business Intelligence Core Self-service Mobile Extreme Social Core for innovation Complete
More informationTap into Hadoop and Other No SQL Sources
Tap into Hadoop and Other No SQL Sources Presented by: Trishla Maru What is Big Data really? The Three Vs of Big Data According to Gartner Volume Volume Orders of magnitude bigger than conventional data
More informationDesigning 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 informationSAP BW on HANA : Complete reference guide
SAP BW on HANA : Complete reference guide Applies to: SAP BW 7.4, SAP HANA, BW on HANA, BW 7.3 Summary There have been many architecture level changes in SAP BW 7.4. To enable our customers to understand
More informationBUILDING BLOCKS OF DATAWAREHOUSE. G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT
BUILDING BLOCKS OF DATAWAREHOUSE G.Lakshmi Priya & Razia Sultana.A Assistant Professor/IT 1 Data Warehouse Subject Oriented Organized around major subjects, such as customer, product, sales. Focusing on
More informationNext-Generation Cloud Analytics with Amazon Redshift
Next-Generation Cloud Analytics with Amazon Redshift What s inside Introduction Why Amazon Redshift is Great for Analytics Cloud Data Warehousing Strategies for Relational Databases Analyzing Fast, Transactional
More informationSQL 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 informationCitusDB Architecture for Real-Time Big Data
CitusDB Architecture for Real-Time Big Data CitusDB Highlights Empowers real-time Big Data using PostgreSQL Scales out PostgreSQL to support up to hundreds of terabytes of data Fast parallel processing
More informationCúram 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 informationENHANCING DECISION MAKING
ENHANCING DECISION MAKING through Dashboards and Business Intelligence February 23, 2016 PART ONE: DESIGN Know your audience Contextualize your data Empower your user PART ONE: DESIGN CASE STUDY Tableau
More informationTRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS
9 8 TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS Assist. Prof. Latinka Todoranova Econ Lit C 810 Information technology is a highly dynamic field of research. As part of it, business intelligence
More informationEvaluating NoSQL for Enterprise Applications. Dirk Bartels VP Strategy & Marketing
Evaluating NoSQL for Enterprise Applications Dirk Bartels VP Strategy & Marketing Agenda The Real Time Enterprise The Data Gold Rush Managing The Data Tsunami Analytics and Data Case Studies Where to go
More informationSAP BUSINESS OBJECTS BO BI 4.1 amron
0 Training Details Course Duration: 65 hours Training + Assignments + Actual Project Based Case Studies Training Materials: All attendees will receive, Assignment after each module, Video recording of
More informationWorkshop Schedule 2015 4 th Quarter
Workshop Schedule 2015 4 th Quarter October: 10/6 PDS DASH 10/15 Understanding the IBM Cognos 7 Toolset 10/20 Understanding the IBM Cognos 10 Web Toolset 10/29 PDS Producer November: 11/5 PDS DASH Advanced
More informationTableau Visual Intelligence Platform Rapid Fire Analytics for Everyone Everywhere
Tableau Visual Intelligence Platform Rapid Fire Analytics for Everyone Everywhere Agenda 1. Introductions & Objectives 2. Tableau Overview 3. Tableau Products 4. Tableau Architecture 5. Why Tableau? 6.
More informationOBIEE 11g Data Modeling Best Practices
OBIEE 11g Data Modeling Best Practices Mark Rittman, Director, Rittman Mead Oracle Open World 2010, San Francisco, September 2010 Introductions Mark Rittman, Co-Founder of Rittman Mead Oracle ACE Director,
More informationUnderstanding and Evaluating the BI Platform by Cindi Howson
Understanding and Evaluating the BI Platform by Cindi Howson All rights reserved. Reproduction in whole or part prohibited except by written permission. Product and company names mentioned herein may be
More informationTE's Analytics on Hadoop and SAP HANA Using SAP Vora
TE's Analytics on Hadoop and SAP HANA Using SAP Vora Naveen Narra Senior Manager TE Connectivity Santha Kumar Rajendran Enterprise Data Architect TE Balaji Krishna - Director, SAP HANA Product Mgmt. -
More informationSurvey of use of Data Warehousing and Business Intelligence at Australasian Universities 2008
Data Warehousing Survey results (Jan ) Australasian Association for Institutional Research (AAIR) Data Warehouse Special Interest Group (SIG) Survey of use of Data Warehousing and Business Intelligence
More informationBig Data Buzzwords From A to Z. By Rick Whiting, CRN 4:00 PM ET Wed. Nov. 28, 2012
Big Data Buzzwords From A to Z By Rick Whiting, CRN 4:00 PM ET Wed. Nov. 28, 2012 Big Data Buzzwords Big data is one of the, well, biggest trends in IT today, and it has spawned a whole new generation
More informationPresented by: Jose Chinchilla, MCITP
Presented by: Jose Chinchilla, MCITP Jose Chinchilla MCITP: Database Administrator, SQL Server 2008 MCITP: Business Intelligence SQL Server 2008 Customers & Partners Current Positions: President, Agile
More informationBIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE. Prepared by:
BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE Cerulium Corporation has provided quality education and consulting expertise for over six years. We offer customized solutions to
More information