SAP NetWeaver BW Performance on IBM i: Comparing SAP BW Aggregates, IBM i DB2 MQTs and SAP BW Accelerator

Size: px
Start display at page:

Download "SAP NetWeaver BW Performance on IBM i: Comparing SAP BW Aggregates, IBM i DB2 MQTs and SAP BW Accelerator"

Transcription

1 SAP NetWeaver BW Performance on IBM i: Comparing SAP BW Aggregates, IBM i DB2 MQTs and SAP BW Accelerator By Susan Bestgen IBM i OS Development, SAP on i

2 Introduction The purpose of this paper is to demonstrate the capabilities of three solutions to optimize and increase performance of SAP NetWeaver BW TM performance on IBM i TM, namely: SAP BW aggregates, DB2 for IBM i TM Materialized Query Tables (MQTs) and the SAP BW Accelerator (BWA). Using a workload similar to the SAP MXL benchmark, all three solutions were implemented, tested and performance results compared. First, an overview of the hardware landscape is provided. The workload definition is then defined, along with brief descriptions of the three performance enhancing implementations. The results section reports the performance of each solution in both a query-only test and query+delta data load test. When determining the best solution for a given installation, these results can help identify tradeoffs between performance, implementation effort and cost. Landscape Definition The central system in the hardware landscape was an IBM Power 6 Model 595 running V7.1 with DB2 for i as the integrated database. SAP NetWeaver 7.0 was installed in a central instance configuration and the workload data was loaded. The initial goal of the test was to hold memory constant in all test environments at 64GB, but additional memory was needed to achieve desired performance for some of the tests. To simulate concurrent users, a workload driver system (a single partition on an IBM Power 5 Model 595) running Linux was loaded with the SAP benchmark driver toolkit. The BW Accelerator was powered by four IBM HS22 Blades using IBM s General Parallel File System (GPFS). The BW Accelerator was attached to the central configuration with a dedicated gigabit ethernet line. Figure 1 illustrates the landscape definition. All three test environments were configured and active at the same time. To test independently, each could be switched on or off for query use via tools shipped with the respective product. Page 2 of 15

3 BW Accelerator 4 HS22 Blades, each with (2 x) 2.93 GHz processors 48 GB total memory per blade (192 GB cluster total) 713 GB shared storage 64 bit Linux IBM GPFS SAP NetWeaver BW 7.0 Dedicated GB ethernet Central configuration IBM POWER 6 Model GHz IBM i V7.1 with DB2 for i SAP NetWeaver BW node partition 64GB / 96GB main memory 300 million rows across 10 infocubes 48 drives / 3 TB Workload Driver IBM POWER 5 Model 595 running the SAP benchmark driver Figure 1. Landscape Definition Workload Definition The key workload metric was SAP query navigation steps per hour (qns/hr 1 ). The workload data configuration consisted of a data warehouse multiprovider comprised of ten infocubes. Each cube s fact table contains one year s worth of data, which equals 30,000,000 rows. Two test points were measured: 1. Query Only: Simultaneous end users concurrently running a series of 11 queries of varying complexity. 2. Query + Data Load: The same query load with all simulated end users active for at least one hour. In addition, 3 data loads scheduled at the beginning, 20 minutes and 40 minutes into the high use interval (the time all workload users are signed on and actively running the query suite) extended the static operational data with delta data. Each delta load added 10,000 rows per cube. 1 This is defined as the throughput value of the workload. A query is a combination of characteristics and key figures (InfoObjects) for the analysis of the data of an InfoProvider. The query data can be displayed in different views. A view change through a user interaction is considered to be a navigation step. Page 3 of 15

4 SAP s benchmark workload toolkit was used to drive the workload from a Linux partition on the workload driver system. The goal was to maximize qns/hr while driving the CPU of the central install to 90% or greater. When running the delta loads, the goal was to complete each delta load in less than 20 minutes. A sample query from the workload follows: SELECT "D3". "/B49/S_DIVISION" AS "S 022","DU". "/B49/S_BASE_UOM" AS "S 020","DU". "/B49/S_STAT_CURR" AS "S 031", SUM ( "F". "/B49/S_CRMEM_CST" ) AS "Z 043", SUM ( "F". "/B49/S_CRMEM_QTY" ) AS "Z 044", SUM ( "F". "/B49/S_CRMEM_VAL" ) AS "Z 045", SUM ( "F". "/B49/S_INCORDCST" ) AS "Z 046", SUM ( "F". "/B49/S_INCORDQTY" ) AS "Z 047", SUM ( "F". "/B49/S_INCORDVAL" ) AS "Z 048", SUM ( "F". "/B49/S_INVCD_CST" ) AS "Z 049", SUM ( "F". "/B49/S_INVCD_QTY" ) AS "Z 050", SUM ( "F". "/B49/S_INVCD_VAL" ) AS "Z 051", SUM ( "F". "/B49/S_OPORDQTYB" ) AS "Z 052", SUM ( "F". "/B49/S_OPORDVALS" ) AS "Z 053", SUM ( "F". "/B49/S_ORD_ITEMS" ) AS "Z 054", SUM ( "F". "/B49/S_RTNSCST" ) AS "Z 055", SUM ( "F". "/B49/S_RTNSQTY" ) AS "Z 056", SUM ( "F". "/B49/S_RTNSVAL" ) AS "Z 057", SUM ( "F". "/B49/S_RTNS_ITEM" ) AS "Z 058", COUNT( * ) AS "Z 059" FROM "/B49/EBENCH05" "F" JOIN "/B49/DBENCH05U" "DU" ON "F". "KEY_BENCH05U" = "DU". "DIMID" JOIN "/B49/DBENCH051" "D1" ON "F". "KEY_BENCH051" = "D1". "DIMID" JOIN "/B49/XCUSTOMER" "X2" ON "D1". "/B49/S_SOLD_TO" = "X2". "SID" JOIN "/B49/DBENCH05T" "DT" ON "F". "KEY_BENCH05T" = "DT". "DIMID" JOIN "/B49/DBENCH05P" "DP" ON "F". "KEY_BENCH05P" = "DP". "DIMID" JOIN "/B49/DBENCH053" "D3" ON "F". "KEY_BENCH053" = "D3". "DIMID" JOIN "/B49/SSALESORG" "S1" ON "D3". "/B49/S_SALESORG" = "S1". "SID" WHERE ( ( ( ( "S1". "/B49/S_SALESORG" = 'B310' ) ) AND ( ( "X2". "/B49/S_COUNTRY" = 13 ) ) AND ( ( "DT". "SID_0CALMONTH" BETWEEN AND ) ) AND ( ( "DP". "SID_0CHNGID" = 0 ) ) AND ( ( "DT". "SID_0FISCVARNT" = 5 ) ) AND ( ( "DP". "SID_0RECORDTP" = 0 ) ) AND ( ( "DP". "SID_0REQUID" <= 2267 ) ) ) ) AND "X2". "OBJVERS" = 'A' GROUP BY "D3". "/B49/S_DIVISION","DU". "/B49/S_BASE_UOM","DU". "/B49/S_STAT_CURR" OPTIMIZE FOR ALL ROWS ; Page 4 of 15

5 All queries in the workload were similar to the sample, varying in the number of files joined and complexity of the selection criteria. This mimics an ad hoc drill down situation where a user starts from gathering data warehouse information and then narrows in on data specifics. Test Environments Performance data was collected running the workload in four environments: baseline, SAP Aggregates, DB2 for i Materialized Query Tables, and Business Warehouse Accelerator. Hardware configuration remained the same for all environments except where explicitly noted. The only additional optimizations performed were to provide indexes to support each scenario (where necessary) and modifications to the delta load process chains unique to each test environment. Baseline The baseline test consisted of a basic install of the benchmark environment and benchmark driver toolkit. Sample runs were performed which enabled the DB2 for i query optimizer to suggest indexes to improve query performance on the complex joins. These additional indexes over the infocube fact tables were created, and the baseline run was collected to establish query only performance in a nonperformance optimized setting. This data point helped quantify the improvements shown in the query phase of the subsequent test scenarios. SAP Aggregates An SAP aggregate is a materialized, aggregated view of the data in an InfoCube. With an aggregate, the dataset of an InfoCube is saved redundantly and persistently in a consolidated form into the database. Aggregates make it possible to improve the query performance in a similar way to database indexes or database summary tables. SAP NetWeaver will automatically detect the presence of an aggregate, compare it to a query, and transform the query to use the aggregate if it is a match for the query (consistent with a subset of the join and selection criteria). The transformed query is then passed along to the database for completion. The screenshot in Figure 2 shows a sample aggregate definition over characteristics Country, Division, Sales Organization, and Calendar Year/Month for InfoCube 01. Multiple aggregates were defined over each infocube to improve performance of this workload. In some cases, additional or more detailed aggregates could have been defined to improve query only performance. However, the maintenance of Page 5 of 15

6 these aggregates during the query + data load test was found to be too excessive 2 to achieve desired results in terms of response time and overall throughput. Figure 2. Aggregate Maintenance Screenshot (SAP transaction code rsa1 -> Maintain Aggregates) DB2 for i Materialized Query Tables (MQTs) MQTs are DB2 tables that contain the results of a query along with the query s definition. The MQT can be substituted for one or more base tables in the query by the DB2 query optimizer. A matching MQT implies the MQT definition contains all or a subset of the selection criteria specified in the query and it therefore holds the applicable data needed by the query. Materialized query tables have the potential to greatly improve response time for complex queries by storing precomputed results for complex tasks such as joins, aggregation and selection. The MQT definition that parallels the SAP aggregate in the previous section would be defined as follows: CREATE TABLE MQT_SAMPLE AS ( SELECT "X2". "/B49/S_COUNTRY" AS COUNTRY, "S1". "/B49/S_SALESORG" AS SALESORG, "D3". "/B49/S_DIVISION" AS DIVISION, SUM ( "F". "/B49/S_CRMEM_CST" ) AS CRMEM_CST, SUM ( "F". "/B49/S_CRMEM_QTY" ) AS CRMEM_QTY, 2 A maximum amount of time was desired during the data load phase. Since the total data load time includes the time needed refresh all aggregates to reflect the newly loaded data, the number and complexity of aggregates defined had to be balanced with the overall data load maximum time window. Page 6 of 15

7 SUM ( "F". "/B49/S_CRMEM_VAL" ) AS CRMEM_VAL, SUM ( "F". "/B49/S_INCORDCST" ) AS INCORDCST, SUM ( "F". "/B49/S_INCORDQTY" ) AS INCORDQTY, SUM ( "F". "/B49/S_INCORDVAL" ) AS INCORDVAL, SUM ( "F". "/B49/S_INVCD_CST" ) AS INVCD_CST, SUM ( "F". "/B49/S_INVCD_QTY" ) AS INVCD_QTY, SUM ( "F". "/B49/S_INVCD_VAL" ) AS INVCD_VAL, SUM ( "F". "/B49/S_OPORDQTYB" ) AS OPORDQTYB, SUM ( "F". "/B49/S_OPORDVALS" ) AS OPORDVALS, SUM ( "F". "/B49/S_ORD_ITEMS" ) AS ORD_ITEMS, SUM ( "F". "/B49/S_RTNSCST" ) AS RTNSCST, SUM ( "F". "/B49/S_RTNSQTY" ) AS RTNSQTY, SUM ( "F". "/B49/S_RTNSVAL" ) AS RTNSVAL, SUM ( "F". "/B49/S_RTNS_ITEM" ) AS RTNS_ITEM, COUNT( * ) AS RECCOUNT FROM "/B49/EBENCH01" "F" JOIN "/B49/DBENCH011" "D1" ON "F". "KEY_BENCH011" = "D1". "DIMID" JOIN "/B49/DBENCH01T" "DT" ON "F". "KEY_BENCH01T" = "DT". "DIMID" JOIN "/B49/DBENCH013" "D3" ON "F". "KEY_BENCH013" = "D3". "DIMID" JOIN "/B49/SSALESORG" "S1" ON "D3". "/B49/S_SALESORG" = "S1". "SID" JOIN "/B49/XCUSTOMER" "X2" ON "D1". "/B49/S_SOLD_TO" = "X2". "SID" GROUP BY "X2". "/B49/S_COUNTRY", "S1". "/B49/S_SALESORG", "D3". "/B49/S_DIVISION" ) DATA INITIALLY IMMEDIATE REFRESH DEFERRED MAINTAINED BY USER ENABLE QUERY OPTIMIZATION ; Like aggregates, multiple MQTs for each InfoCube were defined to satisfy query requirements for maximizing performance in both the query only and query + data load tests. Business Warehouse Accelerator (BW Acclerator) The SAP NetWeaver BW Accelerator is an appliance a hardware and software bundle which serves to improve the performance of Business Warehouse search and analysis functions. Using SAP NetWeaver 7.0 as a base, the TREX search and classification engine builds up the BW Accelerator with BW Accelerator indexes. These compressed structures represent replicated BW star schema data and, once created, are used transparently by SAP NetWeaver. The BW Accelerator was comprised of four IBM HS22 blades. IBM s General Parallel File System (GPFS) was used to provide high performance access to a shared storage pool across all BW Accelerator blades. Once the appliance was attached and configured, the BW indexes were created from the Data Warehouse Workbench Modeling menu for each cube in the target MultiProvider. Figure 3 shows the modeling menu where Maintaining BI Acclerator Indexes can be selected, while Figure 4 shows the detail for the BIA Index Maintenance Wizard. Once built, the BWA indexes can be toggled on or off for query use: Page 7 of 15

8 Figure 3. Data Warehouse Modeling BW Accelerator Maintenance screenshot (SAP transaction code rsa1) Figure 4. BIA Index Maintenance Wizard Screenshot (SAP transaction code rsa1 -> Maintain BI Accelerator Index) Page 8 of 15

9 Results Two separate data points were measured: query only, and query concurrent with three delta loads (query + data load) timed to start at 20 minute intervals. High CPU utilization (>90%) on the central box was desired. The key metrics measured were: Overall SAP qns/hr (query navigation steps per hour) Overall SAP average response time (in seconds) Individual query average response times for the eleven data warehouse queries CPU on the central installation IBM i server The defined pre-test performance goals were: Collect metrics on the four defined scenarios for query only Collect metrics on the three performance enhancing tests for query plus delta data load No or minimal changes to the system configuration between tests Query Only Results: The table and graph in Figures 5 and 6 summarize the workload results running queries only. As expected, large performance and throughput gains were achieved with all three environments as compared to the baseline results. There are several key points to note in comparing these results: 1. Both the baseline and aggregates tests required additional memory to drive results close to or above 90% CPU utilization. These scenarios could have benefitted from even more memory and/or additional DASD arms to reduce the I/O demands of performing the complex joins. 2. Higher response times on the baseline test reflect the additional time required to perform the join, selection and grouping on the workload queries. The impact of stored summarization with aggregates, MQTs and BWA is evident in both the response time and overall capacity to drive higher results. 3. With the 96GB of memory, the aggregates query-only number could have been driven slightly higher. However, 250 concurrent users was the maximum that could be supported during the query + data load test. Thus, 250 concurrent users were reported for the query-only results to maintain consistency of comparisons across both tests. 4. The MQT results represent a fully utilized system that maximizes CPU, I/O and workload performance in the query + data load test. 5. The BWA appliance could have supported many more users and achieved much higher results. The performance was gated by the capacity of the IBM i server used for the SAP NetWeaver central installation. In addition, more blades can be configured to the BWA to further increase workload capacity Page 9 of 15

10 Test Concurrent users Memory (GB) SAP qns/hr SAP avg rsp time CPU on i box Baseline a Aggregates a MQTs BWA b (a) Additional memory required to achieve response time and CPU utilization goals in range with MQTs and BWA (b) Results gated by driving capacity of the central configuration server Fi gure 5. Query Only Overall Results Query Only Average Response Times (in seconds) Aggregates MQTs BWA Baseline Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Figure 6. Query only average response times per query. NOTE: Baseline response times of 9.8 (Q2) and 8.7 (Q7). Graph scaled to 3 seconds to better differentiate faster response times across most of the queries. Query Plus Delta Data Load Results: The delta data load phase was to run at the same level of concurrent SAP users as the query only workload. The test was lengthened to run for at least one hour. Once all users were running concurrently, the high use interval phase of the test began. Delta loads were started at the 0 minute mark, at the 20 minute mark, and at the 40 minute mark of this interval. The goal was to confirm a Page 10 of 15

11 minimal impact on average response time and overall SAP qns/hr while completing each delta load in 20 minutes or less. Observations to note on the comparisons in Figures 7 and 8: 1. Despite the additional memory for the SAP aggregates, overall performance was still significantly impacted by I/O constraints. Additional disk arms and/or memory would have benefitted this workload but was not available with the test hardware. Excessive delta load times reflect the I/O burden of performing the complex end user queries concurrently with refreshes of the SAP aggregates, which are required during the delta loads. It was not possible to push CPU above 90% due to the I/O constraints. 2. MQTs and BWA were comparable in the ability to drive CPU and maintain response time while staying within the 20 minute delta load window time at the same memory footprint. 3. BWA s increase in overall throughput is a reflection of the query and load activity offloaded to the appliance, thus freeing up the central installation IBM i server to achieve increased capacity. 4. The BWA could have supported much more work, as shown by Figure 9. CPU load on the BWA did not exceed 25% on any blade during the delta load test and work was evenly distributed across the four blades. Test Concurrent Memory SAP qns/hr SAP avg rsp CPU on i Delta load 1 Delta load 2 Delta load 3 users (GB) time box time time time Aggregates % 36:28 41:13 32:09 MQTs % 18:17 10:58 11:57 BWA a % 13:37 9:48 8:52 (a) Results gated by driving capacity of the central configuration server Figure 7. Query + Data Load Overall Results Page 11 of 15

12 Query + Data Load Average Response Times (in seconds) Aggregates MQTs BWA Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Figure 8. Query + Data Load average response times per query The TREX workload administration toolset provides a set of monitoring tools for the BWA. In Figure 9, the chart graphs both CPU and memory used for each of the 4 blades in the BWA implementation. The timeframe from 20:15-21:45 represents the system utilization during the query + data load workload. CPU averaged around 20% on each blade. Both CPU and memory were evenly utilized across the blades with no tuning required to achieve this balance. Page 12 of 15

13 Figure 9. TREX Administration Services summary of CPU and Memory on BWA Summary Each of the workload configurations demonstrated their potential performance benefits over a basic SAP NetWeaver Business Warehouse installation. The pros and cons in Figure 10 can be weighed to determine the best fit for a given environment: Test Pros Cons Aggregates Capability shipped without charge in SAP NetWeaver NetWeaver controlled aggregate maintenance Significant performance gains over baseline maintenance overhead Some analysis required to define an optimal set of aggregates that balances query performance vs. aggregate Least potential for performance improvement of the three tested implementations Page 13 of 15

14 MQTs Capability shipped without charge in DB2 for i Performance gains better than SAP aggregates with smaller memory footprint BWA Highest performance gains of the three configurations with much more capacity available to achieve higher results (could push the 4 blades higher or add additional blades) BWA indexes the easiest to define no analysis required BWA indexes automatically maintained Figure 10. Summary of Configuration Pros and Cons Analysis and SQL experience required to define an optimal set of MQTs (service offerings available) System maintained MQTs not yet available on DB2 for i (user must define and implement maintenance strategy again, service offerings available) Appliance cost Additional knowledge required to install and maintain (service offerings available) A generalized view of the cost /skills / performance comparison is shown in Figure 11. While each solution may require acquisition cost, additional DASD and/or memory costs for MQTs and aggregates are incurred only if the system did not already have sufficient capacity. All software needed to implement these solutions is bundled with SAP NetWeaver in the case of aggregates and bundled with DB2 for i in the case of MQTs. The BWA has a higher acquisition cost due to the separate appliance. However, once configured and implemented, BWA has the least amount of ongoing skill to maintain and the highest potential for benefit. The aggregate and MQT solutions both require analysis and implementation skills on a periodic basis to monitor and maintain the highest levels of performance gains. The skill level is higher for MQTs due to the need for a maintenance strategy for keeping the table(s) up to date. Appliance Memory and DASD DASD Acquistion cost Skill to implement Benefit Aggregate MQT BWA Figure 11. Approximated Comparison of Cost / Skills / Performance Benefit The techniques described here can also be applied to your data sets to help your business drive even more performance out of your SAP Business Warehouse solution running on IBM i. Any one or a combination of performance optimizing enhancements may be the best solution based on price, performance needs, and skills. IBM offers expert consulting to help you analyze your data, and apply the techniques that will best leverage the capabilities of your IBM i platform. If you would like help Page 14 of 15

15 applying these techniques to your Business Warehouse solution, contact Frank Kriss with IBM Systems Lab Services and Training to discuss an SAP on IBM i BW Performance Assessment. For more information about the tests represented in this document, contact Susan Bestgen (sbestgen@us.ibm.com). Special Notices Performance is based on measurements using a benchmark similar to a standard SAP benchmark in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon considerations such as network configuration, size of database, the I/O configuration, the storage configuration, and the actual workload. Therefore, no assurance can be given that an individual will achieve throughput or performance improvements equivalent to the ratios stated here. Page 15 of 15

Performance Tuning BI on SAP NetWeaver Using DB2 for i5/os and i5 Navigator

Performance Tuning BI on SAP NetWeaver Using DB2 for i5/os and i5 Navigator Performance Tuning BI on SAP NetWeaver Using DB2 for i5/os and i5 Navigator Susan Bestgen System i ERP SAP team System i TM with DB2 for i5/os TM leads the industry in SAP BI-D and SAP BW benchmark certifications

More information

HANA Performance. Efficient Speed and Scale-out for Real-time BI

HANA Performance. Efficient Speed and Scale-out for Real-time BI HANA Performance Efficient Speed and Scale-out for Real-time BI 1 HANA Performance: Efficient Speed and Scale-out for Real-time BI Introduction SAP HANA enables organizations to optimize their business

More information

Know How Network: SAP BW Performance Monitoring with BW Statistics

Know How Network: SAP BW Performance Monitoring with BW Statistics Know How Network: SAP BW Performance Monitoring with BW Statistics Ron Silberstein Platinum Consultant- Business Intelligence Netweaver RIG US SAP Labs, LLC Agenda 2 BW Statistics Overview Monitoring with

More information

Tools, tips, and strategies to optimize BEx query performance for SAP HANA

Tools, tips, and strategies to optimize BEx query performance for SAP HANA Tools, tips, and strategies to optimize BEx query performance for SAP HANA Pravin Gupta TekLink International Produced by Wellesley Information Services, LLC, publisher of SAPinsider. 2016 Wellesley Information

More information

Susanne Hess, Stefanie Lenz, Jochen Scheibler. Sales and Distribution Controlling with SAP. NetWeaver BI. Bonn Boston

Susanne Hess, Stefanie Lenz, Jochen Scheibler. Sales and Distribution Controlling with SAP. NetWeaver BI. Bonn Boston Susanne Hess, Stefanie Lenz, Jochen Scheibler Sales and Distribution Controlling with SAP NetWeaver BI Bonn Boston Contents Acknowledgments... 9 1 Introduction... 11 1.1 Goals and Basic Principles... 11

More information

Extending the Reach of LSA++ Using New SAP BW 7.40 Artifacts Pravin Gupta, TekLink International Inc. Bhanu Gupta, Molex SESSION CODE: BI2241

Extending the Reach of LSA++ Using New SAP BW 7.40 Artifacts Pravin Gupta, TekLink International Inc. Bhanu Gupta, Molex SESSION CODE: BI2241 Extending the Reach of LSA++ Using New SAP BW 7.40 Artifacts Pravin Gupta, TekLink International Inc. Bhanu Gupta, Molex SESSION CODE: BI2241 Agenda What is Enterprise Data Warehousing (EDW)? Introduction

More information

1) In the Metadata Repository:

1) In the Metadata Repository: 1) In the Metadata Repository: - Objects delivered with BI Content can be activated - You can find the medatada for all delivered and activated objects and their links to other objects - BI Web Applications

More information

How can a Reference Query Be used?

How can a Reference Query Be used? How can a Reference Query Applies to SAP NetWeaver Business Warehouse 7.30 (BW7.30) SP05 with SAP NetWeaver Business Warehouse Accelerator 7.20 (BWA7.20) or HANA 1.0 running as a database for SAP NetWeaver

More information

Performance Optimization

Performance Optimization Joe Darlak and Jesper Christensen SAP BW: Administration and Performance Optimization Galileo Press Bonn Boston Foreword 13 Preface 15 PART I Initial System Setup 1.1 Infrastructure Architecture 22 1.1.1

More information

Lori Vanourek Product Management SAP NetWeaver / BI. Mike Eacrett SAP NetWeaver RIG - BI

Lori Vanourek Product Management SAP NetWeaver / BI. Mike Eacrett SAP NetWeaver RIG - BI Lori Vanourek Product Management SAP NetWeaver BI Mike Eacrett SAP NetWeaver RIG - BI Content Overview Query Performance OLAP Cache Pre-Calculation Load Performance Performance Tuning OLTP Systems Application

More information

This is a simple tutorial that covers the basics of SAP Business Intelligence and how to handle its various other components.

This is a simple tutorial that covers the basics of SAP Business Intelligence and how to handle its various other components. About the Tutorial SAP Business Warehouse (BW) integrates data from different sources, transforms and consolidates the data, does data cleansing, and storing of data as well. It also includes data modeling,

More information

This is a simple tutorial that covers the basics of SAP Business Intelligence and how to handle its various other components.

This is a simple tutorial that covers the basics of SAP Business Intelligence and how to handle its various other components. About the Tutorial SAP Business Warehouse (BW) integrates data from different sources, transforms and consolidates the data, does data cleansing, and storing of data as well. It also includes data modeling,

More information

Using SAP NetWeaver Business Intelligence in the universe design tool SAP BusinessObjects Business Intelligence platform 4.1

Using SAP NetWeaver Business Intelligence in the universe design tool SAP BusinessObjects Business Intelligence platform 4.1 Using SAP NetWeaver Business Intelligence in the universe design tool SAP BusinessObjects Business Intelligence platform 4.1 Copyright 2013 SAP AG or an SAP affiliate company. All rights reserved. No part

More information

Overview of Reporting in the Business Information Warehouse

Overview of Reporting in the Business Information Warehouse Overview of Reporting in the Business Information Warehouse Contents What Is the Business Information Warehouse?...2 Business Information Warehouse Architecture: An Overview...2 Business Information Warehouse

More information

Built for Speed: Comparing Panoply and Amazon Redshift Rendering Performance Utilizing Tableau Visualizations

Built for Speed: Comparing Panoply and Amazon Redshift Rendering Performance Utilizing Tableau Visualizations Built for Speed: Comparing Panoply and Amazon Redshift Rendering Performance Utilizing Tableau Visualizations Table of contents Faster Visualizations from Data Warehouses 3 The Plan 4 The Criteria 4 Learning

More information

This download file shows detailed view for all updates from BW 7.5 SP00 to SP05 released from SAP help portal.

This download file shows detailed view for all updates from BW 7.5 SP00 to SP05 released from SAP help portal. This download file shows detailed view for all updates from BW 7.5 SP00 to SP05 released from SAP help portal. (1) InfoObject (New) As of BW backend version 7.5 SPS00, it is possible to model InfoObjects

More information

Evolution of Database Systems

Evolution of Database Systems Evolution of Database Systems Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Intelligent Decision Support Systems Master studies, second

More information

Performance Tuning with the OLAP Cache

Performance Tuning with the OLAP Cache How to Performance Tuning with the OLAP Cache BUSINESS INFORMATION WAREHOUSE Applicable Releases: 3.x Release date: September 2004 SAP (SAP America, Inc. and SAP AG) assumes no responsibility for errors

More information

Using EMC FAST with SAP on EMC Unified Storage

Using EMC FAST with SAP on EMC Unified Storage Using EMC FAST with SAP on EMC Unified Storage Applied Technology Abstract This white paper examines the performance considerations of placing SAP applications on FAST-enabled EMC unified storage. It also

More information

Using Query Extract to Export Data from Business warehouse, With Pros and Cons Analyzed

Using Query Extract to Export Data from Business warehouse, With Pros and Cons Analyzed Using Query Extract to Export Data from Business warehouse, With Pros and Cons Analyzed Applies to: SAP BW 3.X & BI 7.0. For more information, visit the Business Intelligence homepage. Summary This article

More information

C_HANAIMP142

C_HANAIMP142 C_HANAIMP142 Passing Score: 800 Time Limit: 4 min Exam A QUESTION 1 Where does SAP recommend you create calculated measures? A. In a column view B. In a business layer C. In an attribute view D. In an

More information

Best Practices. Deploying Optim Performance Manager in large scale environments. IBM Optim Performance Manager Extended Edition V4.1.0.

Best Practices. Deploying Optim Performance Manager in large scale environments. IBM Optim Performance Manager Extended Edition V4.1.0. IBM Optim Performance Manager Extended Edition V4.1.0.1 Best Practices Deploying Optim Performance Manager in large scale environments Ute Baumbach (bmb@de.ibm.com) Optim Performance Manager Development

More information

C_TBI30_74

C_TBI30_74 C_TBI30_74 Passing Score: 800 Time Limit: 0 min Exam A QUESTION 1 Where can you save workbooks created with SAP BusinessObjects Analysis, edition for Microsoft Office? (Choose two) A. In an Analysis iview

More information

TBW60. BW: Operations and Performance COURSE OUTLINE. Course Version: 10 Course Duration: 5 Day(s)

TBW60. BW: Operations and Performance COURSE OUTLINE. Course Version: 10 Course Duration: 5 Day(s) TBW60 BW: Operations and Performance. COURSE OUTLINE Course Version: 10 Course Duration: 5 Day(s) SAP Copyrights and Trademarks 2014 SAP SE. All rights reserved. No part of this publication may be reproduced

More information

Service Description. IBM DB2 on Cloud. 1. Cloud Service. 1.1 IBM DB2 on Cloud Standard Small. 1.2 IBM DB2 on Cloud Standard Medium

Service Description. IBM DB2 on Cloud. 1. Cloud Service. 1.1 IBM DB2 on Cloud Standard Small. 1.2 IBM DB2 on Cloud Standard Medium Service Description IBM DB2 on Cloud This Service Description describes the Cloud Service IBM provides to Client. Client means the company and its authorized users and recipients of the Cloud Service.

More information

1 Dulcian, Inc., 2001 All rights reserved. Oracle9i Data Warehouse Review. Agenda

1 Dulcian, Inc., 2001 All rights reserved. Oracle9i Data Warehouse Review. Agenda Agenda Oracle9i Warehouse Review Dulcian, Inc. Oracle9i Server OLAP Server Analytical SQL Mining ETL Infrastructure 9i Warehouse Builder Oracle 9i Server Overview E-Business Intelligence Platform 9i Server:

More information

QLIKVIEW SCALABILITY BENCHMARK WHITE PAPER

QLIKVIEW SCALABILITY BENCHMARK WHITE PAPER QLIKVIEW SCALABILITY BENCHMARK WHITE PAPER Measuring Business Intelligence Throughput on a Single Server QlikView Scalability Center Technical White Paper December 2012 qlikview.com QLIKVIEW THROUGHPUT

More information

Foreword 7. Acknowledgments 9. 1 Evolution and overview The evolution of SAP HANA The evolution of BW 17

Foreword 7. Acknowledgments 9. 1 Evolution and overview The evolution of SAP HANA The evolution of BW 17 Table of Contents Foreword 7 Acknowledgments 9 1 Evolution and overview 11 1.1 The evolution of SAP HANA 11 1.2 The evolution of BW 17 2 Preparing for the conversion to SAP HANA 37 2.1 Sizing 37 2.2 Migration

More information

BW310. BW - Enterprise Data Warehousing COURSE OUTLINE. Course Version: 10 Course Duration: 5 Day(s)

BW310. BW - Enterprise Data Warehousing COURSE OUTLINE. Course Version: 10 Course Duration: 5 Day(s) BW310 BW - Enterprise Data Warehousing. COURSE OUTLINE Course Version: 10 Course Duration: 5 Day(s) SAP Copyrights and Trademarks 2015 SAP SE. All rights reserved. No part of this publication may be reproduced

More information

SAP BW Archiving with Nearline Storage at Esprit

SAP BW Archiving with Nearline Storage at Esprit SAP BW Archiving with Nearline Storage at Esprit Claudia Ottilige, Esprit Europe GmbH Dr. Michael Hahne, Hahne Consulting GmbH 27. Februar 2013 Agenda Company Esprit Initial situation NLS Best Practices

More information

Exadata Implementation Strategy

Exadata Implementation Strategy Exadata Implementation Strategy BY UMAIR MANSOOB 1 Who Am I Work as Senior Principle Engineer for an Oracle Partner Oracle Certified Administrator from Oracle 7 12c Exadata Certified Implementation Specialist

More information

Cognos Dynamic Cubes

Cognos Dynamic Cubes Cognos Dynamic Cubes Amit Desai Cognos Support Engineer Open Mic Facilitator Reena Nagrale Cognos Support Engineer Presenter Gracy Mendonca Cognos Support Engineer Technical Panel Member Shashwat Dhyani

More information

Reduce Costs & Increase Oracle Database OLTP Workload Service Levels:

Reduce Costs & Increase Oracle Database OLTP Workload Service Levels: Reduce Costs & Increase Oracle Database OLTP Workload Service Levels: PowerEdge 2950 Consolidation to PowerEdge 11th Generation A Dell Technical White Paper Dell Database Solutions Engineering Balamurugan

More information

IBM Terms of Use SaaS Specific Offering Terms. IBM DB2 on Cloud. 1. IBM SaaS. 2. Charge Metrics

IBM Terms of Use SaaS Specific Offering Terms. IBM DB2 on Cloud. 1. IBM SaaS. 2. Charge Metrics IBM Terms of Use SaaS Specific Offering Terms IBM DB2 on Cloud The Terms of Use ( ToU ) is composed of this IBM Terms of Use - SaaS Specific Offering Terms ( SaaS Specific Offering Terms ) and a document

More information

SAP HANA Scalability. SAP HANA Development Team

SAP HANA Scalability. SAP HANA Development Team SAP HANA Scalability Design for scalability is a core SAP HANA principle. This paper explores the principles of SAP HANA s scalability, and its support for the increasing demands of data-intensive workloads.

More information

QLogic TrueScale InfiniBand and Teraflop Simulations

QLogic TrueScale InfiniBand and Teraflop Simulations WHITE Paper QLogic TrueScale InfiniBand and Teraflop Simulations For ANSYS Mechanical v12 High Performance Interconnect for ANSYS Computer Aided Engineering Solutions Executive Summary Today s challenging

More information

SAP HANA. Jake Klein/ SVP SAP HANA June, 2013

SAP HANA. Jake Klein/ SVP SAP HANA June, 2013 SAP HANA Jake Klein/ SVP SAP HANA June, 2013 SAP 3 YEARS AGO Middleware BI / Analytics Core ERP + Suite 2013 WHERE ARE WE NOW? Cloud Mobile Applications SAP HANA Analytics D&T Changed Reality Disruptive

More information

#mstrworld. Analyzing Multiple Data Sources with Multisource Data Federation and In-Memory Data Blending. Presented by: Trishla Maru.

#mstrworld. Analyzing Multiple Data Sources with Multisource Data Federation and In-Memory Data Blending. Presented by: Trishla Maru. Analyzing Multiple Data Sources with Multisource Data Federation and In-Memory Data Blending Presented by: Trishla Maru Agenda Overview MultiSource Data Federation Use Cases Design Considerations Data

More information

Data Warehouse Tuning. Without SQL Modification

Data Warehouse Tuning. Without SQL Modification Data Warehouse Tuning Without SQL Modification Agenda About Me Tuning Objectives Data Access Profile Data Access Analysis Performance Baseline Potential Model Changes Model Change Testing Testing Results

More information

SAP NetWeaver BW 7.3 Practical Guide

SAP NetWeaver BW 7.3 Practical Guide Amol Palekar, Bharat Patel, and Shreekant Shiralkar SAP NetWeaver BW 7.3 Practical Guide Bonn Boston Contents at a Glance 1 The Business Scenario: ABCD Corp.... 23 2 Overview of SAP NetWeaver BW... 31

More information

SugarCRM on IBM i Performance and Scalability TECHNICAL WHITE PAPER

SugarCRM on IBM i Performance and Scalability TECHNICAL WHITE PAPER SugarCRM on IBM i Performance and Scalability TECHNICAL WHITE PAPER Contents INTRODUCTION...2 SYSTEM ARCHITECTURE...2 SCALABILITY OVERVIEW...3 PERFORMANCE TUNING...4 CONCLUSION...4 APPENDIX A DATA SIZES...5

More information

IBM DB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs

IBM DB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs IBM DB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs Day(s): 5 Course Code: CL442G Overview Learn how to tune for optimum the IBM DB2 9 for Linux, UNIX, and Windows relational

More information

Course Contents: 1 Business Objects Online Training

Course Contents: 1 Business Objects Online Training IQ Online training facility offers Business Objects online training by trainers who have expert knowledge in the Business Objects and proven record of training hundreds of students Our Business Objects

More information

IBM s Data Warehouse Appliance Offerings

IBM s Data Warehouse Appliance Offerings IBM s Data Warehouse Appliance Offerings RChaitanya IBM India Software Labs Agenda 1 IBM Smart Analytics System (D5600) System Overview Technical Architecture Software / Hardware stack details 2 Netezza

More information

Entscheidungsgrundlagen mit Proof-of-Concept SAP HANA

Entscheidungsgrundlagen mit Proof-of-Concept SAP HANA Place photo here Entscheidungsgrundlagen mit Proof-of-Concept SAP HANA Gerd Rieger Panalpina Management AG 1 Agenda Company Profile SAP CC at Panalpina Current Situation and Motivation Reporting Environment

More information

Root Cause Analysis for SAP HANA. June, 2015

Root Cause Analysis for SAP HANA. June, 2015 Root Cause Analysis for SAP HANA June, 2015 Process behind Application Operations Monitor Notify Analyze Optimize Proactive real-time monitoring Reactive handling of critical events Lower mean time to

More information

Industry Leading BI Performance With System i and DB2 for i5/os Using BI on SAP NetWeaver. Susan Bestgen System i ERP SAP team

Industry Leading BI Performance With System i and DB2 for i5/os Using BI on SAP NetWeaver. Susan Bestgen System i ERP SAP team Industry Leading BI Performance With System i and DB2 for i5/os Using BI on SAP NetWeaver Susan Bestgen System i ERP SAP team SAP product offerings have been available on the IBM System i TM platform since

More information

OLAP Introduction and Overview

OLAP Introduction and Overview 1 CHAPTER 1 OLAP Introduction and Overview What Is OLAP? 1 Data Storage and Access 1 Benefits of OLAP 2 What Is a Cube? 2 Understanding the Cube Structure 3 What Is SAS OLAP Server? 3 About Cube Metadata

More information

DB Partitioning & Compression

DB Partitioning & Compression Applies to: SAP BI 7, SQL Server 2005. For more information, visit the Business Intelligence homepage. Summary The purpose of this document is to outline a strategy for DB partitioning and compression

More information

Preface 7. 1 Data warehousing and database technologies 9

Preface 7. 1 Data warehousing and database technologies 9 TABLE OF CONTENTS Table of Contents Preface 7 1 Data warehousing and database technologies 9 1.1 Starflake schema vs. snowflake schema 11 1.2 Relational databases and SAP HANA 12 1.3 SAP BW on SAP HANA

More information

Separating Fact from Fiction: SAP HANA and Oracle Benchmarking Claims

Separating Fact from Fiction: SAP HANA and Oracle Benchmarking Claims SAP HANA Separating Fact from Fiction: SAP HANA and Oracle Benchmarking Claims Table of Contents 3 Understanding SAP Standard Application Benchmarks and the Benchmark Process 6 Questions to Ask When Assessing

More information

Microsoft SQL Server 2012 Fast Track Reference Configuration Using PowerEdge R720 and EqualLogic PS6110XV Arrays

Microsoft SQL Server 2012 Fast Track Reference Configuration Using PowerEdge R720 and EqualLogic PS6110XV Arrays Microsoft SQL Server 2012 Fast Track Reference Configuration Using PowerEdge R720 and EqualLogic PS6110XV Arrays This whitepaper describes Dell Microsoft SQL Server Fast Track reference architecture configurations

More information

Oracle 1Z0-515 Exam Questions & Answers

Oracle 1Z0-515 Exam Questions & Answers Oracle 1Z0-515 Exam Questions & Answers Number: 1Z0-515 Passing Score: 800 Time Limit: 120 min File Version: 38.7 http://www.gratisexam.com/ Oracle 1Z0-515 Exam Questions & Answers Exam Name: Data Warehousing

More information

Validating the NetApp Virtual Storage Tier in the Oracle Database Environment to Achieve Next-Generation Converged Infrastructures

Validating the NetApp Virtual Storage Tier in the Oracle Database Environment to Achieve Next-Generation Converged Infrastructures Technical Report Validating the NetApp Virtual Storage Tier in the Oracle Database Environment to Achieve Next-Generation Converged Infrastructures Tomohiro Iwamoto, Supported by Field Center of Innovation,

More information

Satisfy the Business Using Db2 Web Query

Satisfy the Business Using Db2 Web Query Satisfy the Business Using Db2 Web Query Rob Bestgen Db2 for i Lab Services bestgen@us.ibm.com Blog: db2webqueryi.blogspot.com qu2@us.ibm.com Db2 Web Query for i From Report Modernization to Business Intelligence

More information

Lenovo Database Configuration for Microsoft SQL Server TB

Lenovo Database Configuration for Microsoft SQL Server TB Database Lenovo Database Configuration for Microsoft SQL Server 2016 22TB Data Warehouse Fast Track Solution Data Warehouse problem and a solution The rapid growth of technology means that the amount of

More information

Reconcile Data Between SAP Source Systems and SAP BW BUSINESS INFORMATION WAREHOUSE

Reconcile Data Between SAP Source Systems and SAP BW BUSINESS INFORMATION WAREHOUSE How to Reconcile Data Between SAP Source Systems and SAP BW BUSINESS INFORMATION WAREHOUSE ASAP How to Paper Applicable Releases: BW 3.0B, 3.1C, 3.2, 3.3 June 2005 SAP (SAP America, Inc. and SAP AG) assumes

More information

Lenovo Database Configuration

Lenovo Database Configuration Lenovo Database Configuration for Microsoft SQL Server Standard Edition DWFT 9TB Reduce time to value with pretested hardware configurations Data Warehouse problem and a solution The rapid growth of technology

More information

Analysis Process Designer (APD) Step by Step Business Intelligence

Analysis Process Designer (APD) Step by Step Business Intelligence Analysis Process Designer (APD) Step by Step Business Intelligence Applies to: This article applies to SAP_BW 350 and SAP_BW 700 with highest support package SAPKW70017. For more information, visit the

More information

CIS 601 Graduate Seminar. Dr. Sunnie S. Chung Dhruv Patel ( ) Kalpesh Sharma ( )

CIS 601 Graduate Seminar. Dr. Sunnie S. Chung Dhruv Patel ( ) Kalpesh Sharma ( ) Guide: CIS 601 Graduate Seminar Presented By: Dr. Sunnie S. Chung Dhruv Patel (2652790) Kalpesh Sharma (2660576) Introduction Background Parallel Data Warehouse (PDW) Hive MongoDB Client-side Shared SQL

More information

Performance Tuning in SAP BI 7.0

Performance Tuning in SAP BI 7.0 Applies to: SAP Net Weaver BW. For more information, visit the EDW homepage. Summary Detailed description of performance tuning at the back end level and front end level with example Author: Adlin Sundararaj

More information

This tutorial will help computer science graduates to understand the basic-to-advanced concepts related to data warehousing.

This tutorial will help computer science graduates to understand the basic-to-advanced concepts related to data warehousing. About the Tutorial A data warehouse is constructed by integrating data from multiple heterogeneous sources. It supports analytical reporting, structured and/or ad hoc queries and decision making. This

More information

Information Management course

Information Management course Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 05(b) : 23/10/2012 Data Mining: Concepts and Techniques (3 rd ed.) Chapter

More information

20 technical tips and tricks to speed SAP NetWeaver Business Intelligence query, report, and dashboard performance Dr. Bjarne Berg

20 technical tips and tricks to speed SAP NetWeaver Business Intelligence query, report, and dashboard performance Dr. Bjarne Berg 20 technical tips and tricks to speed SAP NetWeaver Business Intelligence query, report, and dashboard performance Dr. Bjarne Berg 2009 Wellesley Information Services. All rights reserved. 2 What We ll

More information

Segregating Data Within Databases for Performance Prepared by Bill Hulsizer

Segregating Data Within Databases for Performance Prepared by Bill Hulsizer Segregating Data Within Databases for Performance Prepared by Bill Hulsizer When designing databases, segregating data within tables is usually important and sometimes very important. The higher the volume

More information

Accelerate Applications Using EqualLogic Arrays with directcache

Accelerate Applications Using EqualLogic Arrays with directcache Accelerate Applications Using EqualLogic Arrays with directcache Abstract This paper demonstrates how combining Fusion iomemory products with directcache software in host servers significantly improves

More information

... IBM Power Systems with IBM i single core server tuning guide for JD Edwards EnterpriseOne

... IBM Power Systems with IBM i single core server tuning guide for JD Edwards EnterpriseOne IBM Power Systems with IBM i single core server tuning guide for JD Edwards EnterpriseOne........ Diane Webster IBM Oracle International Competency Center January 2012 Copyright IBM Corporation, 2012.

More information

Syllabus. Syllabus. Motivation Decision Support. Syllabus

Syllabus. Syllabus. Motivation Decision Support. Syllabus Presentation: Sophia Discussion: Tianyu Metadata Requirements and Conclusion 3 4 Decision Support Decision Making: Everyday, Everywhere Decision Support System: a class of computerized information systems

More information

Realtests.C_TBW45_70.80 Questions

Realtests.C_TBW45_70.80 Questions Realtests.C_TBW45_70.80 Questions Number: C_TBW45_70 Passing Score: 800 Time Limit: 120 min File Version: 4.6 http://www.gratisexam.com/ C_TBW45_70 SAP Certified Application Associate- Business Intelligence

More information

Appliances and DW Architecture. John O Brien President and Executive Architect Zukeran Technologies 1

Appliances and DW Architecture. John O Brien President and Executive Architect Zukeran Technologies 1 Appliances and DW Architecture John O Brien President and Executive Architect Zukeran Technologies 1 OBJECTIVES To define an appliance Understand critical components of a DW appliance Learn how DW appliances

More information

Data Warehousing (1)

Data Warehousing (1) ICS 421 Spring 2010 Data Warehousing (1) Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa 3/18/2010 Lipyeow Lim -- University of Hawaii at Manoa 1 Motivation

More information

SAP Business Warehouse powered by SAP HANA

SAP Business Warehouse powered by SAP HANA SAP Business Warehouse powered by SAP HANA Jürgen Hagedorn, Vice President, Head of PM for SAP HANA Europe & APJ, SAP SAP HANA Council July 30, 2013 Mumbai, India SAP Business Warehouse Widely Adopted

More information

BI, Big Data, Mission Critical. Eduardo Rivadeneira Specialist Sales Manager

BI, Big Data, Mission Critical. Eduardo Rivadeneira Specialist Sales Manager BI, Big Data, Mission Critical Eduardo Rivadeneira Specialist Sales Manager Required 9s & Protection Blazing-Fast Performance Enhanced Security & Compliance Rapid Data Exploration & Visualization Managed

More information

SAP Business Information Warehouse Functions in Detail. Version 4.0 SAP BW 3.5 November 2004

SAP Business Information Warehouse Functions in Detail. Version 4.0 SAP BW 3.5 November 2004 Functions in Detail Version 4.0 SAP BW 3.5 November 2004 This Document Version Date of Last Change Release Status Version 1.0 30.09.2002 SAP BW 3.0B Version 2.0 October 2003 SAP BW 3.1Content Version 3.0

More information

Innovations in Business Solutions. SAP Analytics, Data Modeling and Reporting Course

Innovations in Business Solutions. SAP Analytics, Data Modeling and Reporting Course SAP Analytics, Data Modeling and Reporting Course Introduction: This course is design to cover SAP Analytics, Data Modeling and Reporting course content. After completion of this course students can go

More information

Oracle Exadata: The World s Fastest Database Machine

Oracle Exadata: The World s Fastest Database Machine 10 th of November Sheraton Hotel, Sofia Oracle Exadata: The World s Fastest Database Machine Daniela Milanova Oracle Sales Consultant Oracle Exadata Database Machine One architecture for Data Warehousing

More information

FIVE BEST PRACTICES FOR ENSURING A SUCCESSFUL SQL SERVER MIGRATION

FIVE BEST PRACTICES FOR ENSURING A SUCCESSFUL SQL SERVER MIGRATION FIVE BEST PRACTICES FOR ENSURING A SUCCESSFUL SQL SERVER MIGRATION The process of planning and executing SQL Server migrations can be complex and risk-prone. This is a case where the right approach and

More information

EDWH Architecture for Global Data Loading Strategy

EDWH Architecture for Global Data Loading Strategy EDWH Architecture for Global Data Loading Strategy Applies to: System Architecture for EDWH having multiple regions across globe. SAP NetWeaver 2004s BI 7.0 version. For more information, visit the Business

More information

Sync Services. Server Planning Guide. On-Premises

Sync Services. Server Planning Guide. On-Premises Kony MobileFabric Sync Services Server Planning Guide On-Premises Release 6.5 Document Relevance and Accuracy This document is considered relevant to the Release stated on this title page and the document

More information

Storwize/IBM Technical Validation Report Performance Verification

Storwize/IBM Technical Validation Report Performance Verification Storwize/IBM Technical Validation Report Performance Verification Storwize appliances, deployed on IBM hardware, compress data in real-time as it is passed to the storage system. Storwize has placed special

More information

Teradata Aggregate Designer

Teradata Aggregate Designer Data Warehousing Teradata Aggregate Designer By: Sam Tawfik Product Marketing Manager Teradata Corporation Table of Contents Executive Summary 2 Introduction 3 Problem Statement 3 Implications of MOLAP

More information

BI (Business Intelligence)

BI (Business Intelligence) BI (Business Intelligence) Computer: Computer is an electronic device, which takes input, processed it and gives the accurate result as output. Hardware: which we can see and touch. Software: it is a set

More information

Reading Sample. Introduction to SAP BW on SAP HANA SAP HANA Architecture. Contents. Index. The Authors. Implementing SAP BW on SAP HANA

Reading Sample. Introduction to SAP BW on SAP HANA SAP HANA Architecture. Contents. Index. The Authors. Implementing SAP BW on SAP HANA First-hand knowledge. Reading Sample Before diving into the SAP BW on SAP HANA migration process, it s important to understand the type of architecture that SAP HANA brings to the table. Here is an overview

More information

SQL Server Analysis Services

SQL Server Analysis Services DataBase and Data Mining Group of DataBase and Data Mining Group of Database and data mining group, SQL Server 2005 Analysis Services SQL Server 2005 Analysis Services - 1 Analysis Services Database and

More information

IBM Db2 Analytics Accelerator Version 7.1

IBM Db2 Analytics Accelerator Version 7.1 IBM Db2 Analytics Accelerator Version 7.1 Delivering new flexible, integrated deployment options Overview Ute Baumbach (bmb@de.ibm.com) 1 IBM Z Analytics Keep your data in place a different approach to

More information

Service Description. IBM DB2 on Cloud. 1. Cloud Service. 1.1 IBM DB2 on Cloud Standard Small. 1.2 IBM DB2 on Cloud Standard Medium

Service Description. IBM DB2 on Cloud. 1. Cloud Service. 1.1 IBM DB2 on Cloud Standard Small. 1.2 IBM DB2 on Cloud Standard Medium Service Description IBM DB2 on Cloud This Service Description describes the Cloud Service IBM provides to Client. Client means the company and its authorized users and recipients of the Cloud Service.

More information

OBT Global presents. SAP Business Information Warehouse. -an overview -

OBT Global presents. SAP Business Information Warehouse. -an overview - OBT Global presents. SAP Business Information Warehouse -an overview - Contents General Overview Architecture Overview Reporting Overview 6/19/2009 2 General Overview 6/19/2009 3 BW Defined BW is SAP's

More information

Welcome to the Learning Objekt Operational Analytics with Operational Data Providers. After the explanations of the entire ODP Architecture and the

Welcome to the Learning Objekt Operational Analytics with Operational Data Providers. After the explanations of the entire ODP Architecture and the Welcome to the Learning Objekt Operational Analytics with Operational Data Providers. After the explanations of the entire ODP Architecture and the specifics of the TransientProvider layer this unit will

More information

... IBM Advanced Technical Skills IBM Oracle International Competency Center September 2013

... IBM Advanced Technical Skills IBM Oracle International Competency Center September 2013 Performance benefits of IBM Power Systems and IBM FlashSystem for JD Edwards EnterpriseOne IBM Power 780 server with AIX and IBM FlashSystem 820 flash storage improves batch performance in a client proof

More information

Microsoft SQL Server Training Course Catalogue. Learning Solutions

Microsoft SQL Server Training Course Catalogue. Learning Solutions Training Course Catalogue Learning Solutions Querying SQL Server 2000 with Transact-SQL Course No: MS2071 Two days Instructor-led-Classroom 2000 The goal of this course is to provide students with the

More information

Evolving To The Big Data Warehouse

Evolving To The Big Data Warehouse Evolving To The Big Data Warehouse Kevin Lancaster 1 Copyright Director, 2012, Oracle and/or its Engineered affiliates. All rights Insert Systems, Information Protection Policy Oracle Classification from

More information

SAP HANA SAP HANA Introduction Description:

SAP HANA SAP HANA Introduction Description: SAP HANA SAP HANA Introduction Description: SAP HANA is a flexible, data-source-agnostic appliance that enables customers to analyze large volumes of SAP ERP data in real-time, avoiding the need to materialize

More information

Optimizing and Modeling SAP Business Analytics for SAP HANA. Iver van de Zand, Business Analytics

Optimizing and Modeling SAP Business Analytics for SAP HANA. Iver van de Zand, Business Analytics Optimizing and Modeling SAP Business Analytics for SAP HANA Iver van de Zand, Business Analytics Early data warehouse projects LIMITATIONS ISSUES RAISED Data driven by acquisition, not architecture Too

More information

What are Specifics Concerning the Creation of New Master Data?

What are Specifics Concerning the Creation of New Master Data? What are Specifics Concerning the Creation of New Master Data? Applies to SAP NetWeaver Business Warehouse 7.30 (BW7.30) SP05 with SAP NetWeaver Business Warehouse Accelerator 7.20 (BWA7.20) or HANA 1.0

More information

Create Cube From Star Schema Grouping Framework Manager

Create Cube From Star Schema Grouping Framework Manager Create Cube From Star Schema Grouping Framework Manager Create star schema groupings to provide authors with logical groupings of query Connect to an OLAP data source (cube) in a Framework Manager project

More information

Designing dashboards for performance. Reference deck

Designing dashboards for performance. Reference deck Designing dashboards for performance Reference deck Basic principles 1. Everything in moderation 2. If it isn t fast in database, it won t be fast in Tableau 3. If it isn t fast in desktop, it won t be

More information

Data Warehousing and Decision Support. Introduction. Three Complementary Trends. [R&G] Chapter 23, Part A

Data Warehousing and Decision Support. Introduction. Three Complementary Trends. [R&G] Chapter 23, Part A Data Warehousing and Decision Support [R&G] Chapter 23, Part A CS 432 1 Introduction Increasingly, organizations are analyzing current and historical data to identify useful patterns and support business

More information

Benefits of Automatic Data Tiering in OLTP Database Environments with Dell EqualLogic Hybrid Arrays

Benefits of Automatic Data Tiering in OLTP Database Environments with Dell EqualLogic Hybrid Arrays TECHNICAL REPORT: Performance Study Benefits of Automatic Data Tiering in OLTP Database Environments with Dell EqualLogic Hybrid Arrays ABSTRACT The Dell EqualLogic hybrid arrays PS6010XVS and PS6000XVS

More information

CSE 544 Principles of Database Management Systems. Alvin Cheung Fall 2015 Lecture 8 - Data Warehousing and Column Stores

CSE 544 Principles of Database Management Systems. Alvin Cheung Fall 2015 Lecture 8 - Data Warehousing and Column Stores CSE 544 Principles of Database Management Systems Alvin Cheung Fall 2015 Lecture 8 - Data Warehousing and Column Stores Announcements Shumo office hours change See website for details HW2 due next Thurs

More information

Coca-Cola Bottling Co. Consolidated utilizes SAP technical upgrade project to migrate from Oracle to IBM DB2

Coca-Cola Bottling Co. Consolidated utilizes SAP technical upgrade project to migrate from Oracle to IBM DB2 Coca-Cola Bottling Co. Consolidated utilizes SAP technical upgrade project to migrate from Oracle to IBM DB2 About this paper This technical brief describes the migration of an SAP R/3 Enterprise (version

More information