Rapid need for more data makes it challenging to keep the data growth under control. Learn how to shrink SAP BW by half and keep data growth under control in the long term.
Shrink your DB and increase SAP BW performanceDataVard
The document discusses how to optimize data management in SAP systems through analyzing data usage and volume, identifying hot and cold data, implementing nearline storage and housekeeping automation to reduce database size by 40-50% while improving performance, as demonstrated by a case study where a company reduced database size by 43% and improved data load time by 64%.
The document describes DataVard's BW Fitness Test and HeatMap products which provide analysis and recommendations to optimize SAP BW systems. The BW Fitness Test analyzes key performance indicators, system usage, and data distribution. It benchmarks the system against others. The HeatMap visualizes query usage and runtimes to identify performance optimization opportunities. Both tools help with data management, testing, compliance, and preparing for upgrades like SAP HANA.
How to decrease the database size with automated housekeepingDataVard
Our experience shows that temporary and system data contributes towards more than 30% of the size of their SAP databases. This was one of the driving forces behind the development of our housekeeping solution ERNA, which helps in a smart way to keep temporary and system data growth under control. This webinar presents the most typical problems with SAP housekeeping and the solution towards its optimisation.
From our experience, performance is the most critical issue in SAP BW. Thus, those are answered with new technologies such as BWA or SAP HANA. Right data management enables you to bring your BW to the top form even before you decide to invest in new technologies, especially with nearline storage and housekeeping.
This proven approach has helped many global companies reduce the size of their SAP BW. Helping to reduce TCO in their SAP landscape and increase the performance of their SAP BW operations.
DataVard SAPPHIRE Presentation - Canary Code (TM)Mike Nelson
Gregor Stoeckler and Dirk Biehler of Datavard present CanaryCodeTM, a system monitoring solution specialized for SAP landscapes. CanaryCodeTM monitors over 300 key performance indicators for security, availability, and performance across SAP applications like ECC, BW, CRM and HANA. It provides real-time alerts and a knowledge base to help resolve issues. Datavard is an experienced SAP optimization partner with customers ranging from small to large enterprises.
The BW application log contains error and status information stored in BAL* tables like BALDAT, which can grow very large over time and slow down read and write activities; it is recommended to schedule regular deletion of unneeded log entries using transaction SLG1 to prevent performance issues from large log tables.
DataVard is a company that helps customers manage their SAP data landscapes. They offer solutions to classify data, automate housekeeping, integrate Hadoop, and manage data across different storage tiers based on value. Their approach involves profiling data usage, classifying data based on heat maps, and implementing simple rules to manage data in a lean way through automation and central governance. This helps customers reduce storage costs by moving less used data to cheaper tiers while maintaining usability and compliance.
Shrink your DB and increase SAP BW performanceDataVard
The document discusses how to optimize data management in SAP systems through analyzing data usage and volume, identifying hot and cold data, implementing nearline storage and housekeeping automation to reduce database size by 40-50% while improving performance, as demonstrated by a case study where a company reduced database size by 43% and improved data load time by 64%.
The document describes DataVard's BW Fitness Test and HeatMap products which provide analysis and recommendations to optimize SAP BW systems. The BW Fitness Test analyzes key performance indicators, system usage, and data distribution. It benchmarks the system against others. The HeatMap visualizes query usage and runtimes to identify performance optimization opportunities. Both tools help with data management, testing, compliance, and preparing for upgrades like SAP HANA.
How to decrease the database size with automated housekeepingDataVard
Our experience shows that temporary and system data contributes towards more than 30% of the size of their SAP databases. This was one of the driving forces behind the development of our housekeeping solution ERNA, which helps in a smart way to keep temporary and system data growth under control. This webinar presents the most typical problems with SAP housekeeping and the solution towards its optimisation.
From our experience, performance is the most critical issue in SAP BW. Thus, those are answered with new technologies such as BWA or SAP HANA. Right data management enables you to bring your BW to the top form even before you decide to invest in new technologies, especially with nearline storage and housekeeping.
This proven approach has helped many global companies reduce the size of their SAP BW. Helping to reduce TCO in their SAP landscape and increase the performance of their SAP BW operations.
DataVard SAPPHIRE Presentation - Canary Code (TM)Mike Nelson
Gregor Stoeckler and Dirk Biehler of Datavard present CanaryCodeTM, a system monitoring solution specialized for SAP landscapes. CanaryCodeTM monitors over 300 key performance indicators for security, availability, and performance across SAP applications like ECC, BW, CRM and HANA. It provides real-time alerts and a knowledge base to help resolve issues. Datavard is an experienced SAP optimization partner with customers ranging from small to large enterprises.
The BW application log contains error and status information stored in BAL* tables like BALDAT, which can grow very large over time and slow down read and write activities; it is recommended to schedule regular deletion of unneeded log entries using transaction SLG1 to prevent performance issues from large log tables.
DataVard is a company that helps customers manage their SAP data landscapes. They offer solutions to classify data, automate housekeeping, integrate Hadoop, and manage data across different storage tiers based on value. Their approach involves profiling data usage, classifying data based on heat maps, and implementing simple rules to manage data in a lean way through automation and central governance. This helps customers reduce storage costs by moving less used data to cheaper tiers while maintaining usability and compliance.
This document provides an overview of sizing SAP HANA, including initial sizing for new SAP HANA systems (greenfield) and sizing for migrating existing SAP systems to HANA (migration). It discusses using the HANA Quick Sizer for greenfield sizing and SAP Note 1872170 reporting for migration sizing. Memory is the primary driver for HANA sizing while CPU, disk, and network are also factors. Data aging and analytical workloads further impact sizing. The reporting provides estimated memory requirements and savings from HANA optimizations. It concludes with resources for additional sizing documentation, tools, and contact information.
The document discusses sizing SAP S/4HANA systems using the Quick Sizer tool. It provides an overview of the Quick Sizer tool and the SAP HANA sizing approach, which focuses on memory as the leading factor. It also discusses greenfield sizing for SAP S/4HANA projects using the HANA version of Quick Sizer, including the option for data aging to reduce memory requirements. The document notes recent additions to Quick Sizer like what-if analysis for data aging retention times and sizing principles for SAP Fiori frontend servers.
The document discusses 4 secrets for maintaining fitness in an SAP BW system. It shows that most data in a typical BW system is not report data but rather temporary, master, or cube data. It identifies 4 categories of BW fitness to focus on: system robustness, data quality, query performance, and information lifecycle management. It provides tips in each category, such as regularly checking for unused IDs, building indexes and aggregates to improve query performance, and using nearline storage to archive cold data and reduce data volumes over time.
Rabobank banks on DSM for regulation complianceEPI-USE Labs
Rabobank faces strict regulations from the Dutch Banking Authority including the requirement to scramble all data outside the production environment. They invested in the Data Sync Manager (DSM) suite of products from EPI-USE Labs, specifically Client Sync and the Data Secure 3 solution for their SAP ECC and SRM environments. This enabled them to dramatically reduce their Runbook to only four pages, thanks to automation built into DSM to simplify the data refresh and scrambling process. Rabobank now has a clear method to create test data in their non-production environments, while complying with the Banking Authority’s regulations.
SAP HANA is an in-memory database and platform that allows for real-time analytics on large datasets. It utilizes columnar storage, massive parallelization across cores and servers, and in-memory computing to enable interactive queries and analysis of big data without the latency of disk access. SAP HANA provides a single system for both transaction processing and analytics, combining structured and unstructured data on a scalable platform.
Implementation involves deploying new SAP software components into an organization using the ASAP methodology. Key activities include sizing, installation, configuration, customizing, training, and go-live. Sizing determines hardware requirements and is influenced by business needs and technical factors. Implementation can fail due to vague requirements, lack of skills, scope changes, or growth. SAP software includes installation masters, kernels, exports, and GUIs installed based on the operating system, database, and architecture.
sap hana|sap hana database| Introduction to sap hanaJames L. Lee
SAP HANA, sap hana implementation scenarios, sap hana deployment scenarios, SAP HANA Implementations, sap hana implementation and modeling, sap hana implementation cost, sap hana implementation partners, Applications based on SAP HANA, SAP HANA Databases.
Introduction to HANA in-memory from SAPugur candan
This document provides an introduction to SAP HANA, an in-memory database that allows for real-time processing of massive quantities of data. It discusses SAP HANA's vision of enabling a real-time enterprise by removing constraints like data latency. The roadmap outlines steps to use HANA as the primary data store for SAP Business Warehouse and to develop new applications that leverage HANA. Key benefits mentioned include real-time decision making, accelerated business performance, unlocking new insights from large data volumes, and improved business productivity and IT efficiency.
- SAP stands for Systems, Applications & Products and was founded in 1972 in Germany by five former IBM employees. It provides enterprise software including ERP, CRM, SCM, PLM, HCM and more.
- The core components of SAP include ECC for ERP, SAP HANA for analytics, SAP BW for data warehousing, and various other modules. Data can be provisioned between systems using tools like SLT, BODS, RFCs, and third-party options.
- SAP uses a multi-tiered landscape with the front-end applications interfacing with back-end databases and data provisioning technologies to integrate systems and enable analytics.
SAP HANA SPS10 will include enhancements across several key areas:
- It will simplify administration and monitoring for multi-tenant and cloud deployments.
- It will strengthen high availability and disaster recovery capabilities to ensure the highest service level agreements.
- It will provide new and enhanced security features for enterprise-ready deployment, including simplified security administration and enhanced isolation for multitenant database containers.
The SAP solutions for Enterprise Information Management (EIM) Overview presentation provides a comprehensive overview on the portfolio of EIM solutions.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
This document discusses strategies for combining Hadoop and a data warehouse to leverage the strengths of both platforms. It outlines four architectures: split workloads where Hadoop handles large datasets and the warehouse operational data; ETL where Hadoop performs preprocessing; secure access where the warehouse provides SQL access to Hadoop data; and active archive where Hadoop stores cold warehouse data. Case studies demonstrate how these architectures provide benefits like reduced costs, improved analytics and access to more data. The key is finding the right balance of workloads between the platforms.
The document provides an introduction to sizing methods and tools for SAP systems. It discusses different sizing approaches including initial calculation methods, T-shirt sizing, formulas, questionnaires, and quick sizers. It also covers factors that influence sizing such as hardware, software, customizing, data volume, and user behavior. Finally, it discusses sizing based on users or throughput and risks involved in the sizing process.
This document discusses where returns on investment from cloud computing come from. It identifies the five key areas of cloud computing cost savings as: hardware, software, automated provisioning, productivity improvements, and system administration. For each area, it explains how cost savings are achieved and provides metrics to measure savings. The document is intended to help organizations understand how cloud computing can lower IT expenses and calculate the payback period of a cloud investment. Sample ROI projections from an IBM study show payback periods ranging from 4 to 18 months depending on the size of the environment and savings achieved across the five cost areas.
The Numberate Rapid Warehouse Solution is a data integration solution that maximises the cost efficiency of data oriented business intelligence environments.
It builds on over a 15 Years of experience delivering these solutions to a range of corporate and government clients who needed to leverage business growth from their organisations underlying data assets.
HANA the “Hot cake” of the market. I have been hearing about HANA since the beginning of this decade or even earlier. Initially I thought it was just a new database, so why the fuss? My crooked mind used to say: may be SAP does not want to share the market revenue with any other database provider (competitors); therefore they came up with their own database. Pat SAP for Smart Business Acumen. :)
Later I had a notion that HANA is only for BI/BW folks, so being an ABAPer – why should I care? Everyone used to talk about analysis and modelling. So, I used to think, let the BI/BW modelers worry about HANA.
Then the rumour started in market; ABAP and ABAPer are going to be extinct in near future. I used to wonder, if ABAPer are going to die, then who in this whole universe would support those tons and tons of ABAP code written in the history of SAP Implementations? What will happen to all those time, effort and money spent in those large and small scales SAP Implementations? What a waste of rumour!!
The document provides an overview of a 3-day training on SAP HANA for BW community. Day 1 covers what SAP HANA is, its architecture, and data acquisition methods. Day 2 focuses on modeling, reporting, and building apps in SAP HANA. Day 3 is about administration, monitoring, user management, and backup/recovery. The document also discusses how SAP HANA leverages in-memory computing on modern hardware for real-time analytics.
BW Adjusting settings and monitoring data loadsLuc Vanrobays
The document discusses various settings related to loading data into SAP BW, including:
1) Monitoring and adjusting data package settings to address performance issues during data loads. Large numbers of data packages or large individual package sizes can slow loads.
2) Checking transfer parameter settings for data loads from source systems into BW to ensure they are optimized.
3) Ways to split large initial data loads into smaller parallel loads to improve performance, such as using selection criteria to restrict the data per package.
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
What you need to know before migrating to SAP HanaDataVard
SAP HANA is superfast in memeory database and plaform enabling new possibilities in terms of analytical reporting and realtime data acquisition and consumption. However, the business case is often hard to prove due to high licence costs.
Based on our experience with SAP HANA migrations we have collected the most important points for Data Management in SAP BW and system optimisation before moving all data to SAP HANA. Just in few steps you can enhance the benefits of SAP HANA. This presentation will explain you how to analyse your BW, why to implement near line storage what data to housekeep and how code optimisation will help you.
Consolidate your SAP System landscape Teched && d-code 2014Goetz Lessmann
My slide deck from this year's SAP Teched && d-code on how to consolidate SAP system landscapes - both for SAP ERP and SAP BW (and actually any other SAP driven systems). The focus is on getting rid of some misconceptions about consolidations and focusing on solutions instead of problems to achieve tangible goals: TCO savings, quick wins, and a clear way of going for a one-SAP landscape.
This document provides an overview of sizing SAP HANA, including initial sizing for new SAP HANA systems (greenfield) and sizing for migrating existing SAP systems to HANA (migration). It discusses using the HANA Quick Sizer for greenfield sizing and SAP Note 1872170 reporting for migration sizing. Memory is the primary driver for HANA sizing while CPU, disk, and network are also factors. Data aging and analytical workloads further impact sizing. The reporting provides estimated memory requirements and savings from HANA optimizations. It concludes with resources for additional sizing documentation, tools, and contact information.
The document discusses sizing SAP S/4HANA systems using the Quick Sizer tool. It provides an overview of the Quick Sizer tool and the SAP HANA sizing approach, which focuses on memory as the leading factor. It also discusses greenfield sizing for SAP S/4HANA projects using the HANA version of Quick Sizer, including the option for data aging to reduce memory requirements. The document notes recent additions to Quick Sizer like what-if analysis for data aging retention times and sizing principles for SAP Fiori frontend servers.
The document discusses 4 secrets for maintaining fitness in an SAP BW system. It shows that most data in a typical BW system is not report data but rather temporary, master, or cube data. It identifies 4 categories of BW fitness to focus on: system robustness, data quality, query performance, and information lifecycle management. It provides tips in each category, such as regularly checking for unused IDs, building indexes and aggregates to improve query performance, and using nearline storage to archive cold data and reduce data volumes over time.
Rabobank banks on DSM for regulation complianceEPI-USE Labs
Rabobank faces strict regulations from the Dutch Banking Authority including the requirement to scramble all data outside the production environment. They invested in the Data Sync Manager (DSM) suite of products from EPI-USE Labs, specifically Client Sync and the Data Secure 3 solution for their SAP ECC and SRM environments. This enabled them to dramatically reduce their Runbook to only four pages, thanks to automation built into DSM to simplify the data refresh and scrambling process. Rabobank now has a clear method to create test data in their non-production environments, while complying with the Banking Authority’s regulations.
SAP HANA is an in-memory database and platform that allows for real-time analytics on large datasets. It utilizes columnar storage, massive parallelization across cores and servers, and in-memory computing to enable interactive queries and analysis of big data without the latency of disk access. SAP HANA provides a single system for both transaction processing and analytics, combining structured and unstructured data on a scalable platform.
Implementation involves deploying new SAP software components into an organization using the ASAP methodology. Key activities include sizing, installation, configuration, customizing, training, and go-live. Sizing determines hardware requirements and is influenced by business needs and technical factors. Implementation can fail due to vague requirements, lack of skills, scope changes, or growth. SAP software includes installation masters, kernels, exports, and GUIs installed based on the operating system, database, and architecture.
sap hana|sap hana database| Introduction to sap hanaJames L. Lee
SAP HANA, sap hana implementation scenarios, sap hana deployment scenarios, SAP HANA Implementations, sap hana implementation and modeling, sap hana implementation cost, sap hana implementation partners, Applications based on SAP HANA, SAP HANA Databases.
Introduction to HANA in-memory from SAPugur candan
This document provides an introduction to SAP HANA, an in-memory database that allows for real-time processing of massive quantities of data. It discusses SAP HANA's vision of enabling a real-time enterprise by removing constraints like data latency. The roadmap outlines steps to use HANA as the primary data store for SAP Business Warehouse and to develop new applications that leverage HANA. Key benefits mentioned include real-time decision making, accelerated business performance, unlocking new insights from large data volumes, and improved business productivity and IT efficiency.
- SAP stands for Systems, Applications & Products and was founded in 1972 in Germany by five former IBM employees. It provides enterprise software including ERP, CRM, SCM, PLM, HCM and more.
- The core components of SAP include ECC for ERP, SAP HANA for analytics, SAP BW for data warehousing, and various other modules. Data can be provisioned between systems using tools like SLT, BODS, RFCs, and third-party options.
- SAP uses a multi-tiered landscape with the front-end applications interfacing with back-end databases and data provisioning technologies to integrate systems and enable analytics.
SAP HANA SPS10 will include enhancements across several key areas:
- It will simplify administration and monitoring for multi-tenant and cloud deployments.
- It will strengthen high availability and disaster recovery capabilities to ensure the highest service level agreements.
- It will provide new and enhanced security features for enterprise-ready deployment, including simplified security administration and enhanced isolation for multitenant database containers.
The SAP solutions for Enterprise Information Management (EIM) Overview presentation provides a comprehensive overview on the portfolio of EIM solutions.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
This document discusses strategies for combining Hadoop and a data warehouse to leverage the strengths of both platforms. It outlines four architectures: split workloads where Hadoop handles large datasets and the warehouse operational data; ETL where Hadoop performs preprocessing; secure access where the warehouse provides SQL access to Hadoop data; and active archive where Hadoop stores cold warehouse data. Case studies demonstrate how these architectures provide benefits like reduced costs, improved analytics and access to more data. The key is finding the right balance of workloads between the platforms.
The document provides an introduction to sizing methods and tools for SAP systems. It discusses different sizing approaches including initial calculation methods, T-shirt sizing, formulas, questionnaires, and quick sizers. It also covers factors that influence sizing such as hardware, software, customizing, data volume, and user behavior. Finally, it discusses sizing based on users or throughput and risks involved in the sizing process.
This document discusses where returns on investment from cloud computing come from. It identifies the five key areas of cloud computing cost savings as: hardware, software, automated provisioning, productivity improvements, and system administration. For each area, it explains how cost savings are achieved and provides metrics to measure savings. The document is intended to help organizations understand how cloud computing can lower IT expenses and calculate the payback period of a cloud investment. Sample ROI projections from an IBM study show payback periods ranging from 4 to 18 months depending on the size of the environment and savings achieved across the five cost areas.
The Numberate Rapid Warehouse Solution is a data integration solution that maximises the cost efficiency of data oriented business intelligence environments.
It builds on over a 15 Years of experience delivering these solutions to a range of corporate and government clients who needed to leverage business growth from their organisations underlying data assets.
HANA the “Hot cake” of the market. I have been hearing about HANA since the beginning of this decade or even earlier. Initially I thought it was just a new database, so why the fuss? My crooked mind used to say: may be SAP does not want to share the market revenue with any other database provider (competitors); therefore they came up with their own database. Pat SAP for Smart Business Acumen. :)
Later I had a notion that HANA is only for BI/BW folks, so being an ABAPer – why should I care? Everyone used to talk about analysis and modelling. So, I used to think, let the BI/BW modelers worry about HANA.
Then the rumour started in market; ABAP and ABAPer are going to be extinct in near future. I used to wonder, if ABAPer are going to die, then who in this whole universe would support those tons and tons of ABAP code written in the history of SAP Implementations? What will happen to all those time, effort and money spent in those large and small scales SAP Implementations? What a waste of rumour!!
The document provides an overview of a 3-day training on SAP HANA for BW community. Day 1 covers what SAP HANA is, its architecture, and data acquisition methods. Day 2 focuses on modeling, reporting, and building apps in SAP HANA. Day 3 is about administration, monitoring, user management, and backup/recovery. The document also discusses how SAP HANA leverages in-memory computing on modern hardware for real-time analytics.
BW Adjusting settings and monitoring data loadsLuc Vanrobays
The document discusses various settings related to loading data into SAP BW, including:
1) Monitoring and adjusting data package settings to address performance issues during data loads. Large numbers of data packages or large individual package sizes can slow loads.
2) Checking transfer parameter settings for data loads from source systems into BW to ensure they are optimized.
3) Ways to split large initial data loads into smaller parallel loads to improve performance, such as using selection criteria to restrict the data per package.
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
What you need to know before migrating to SAP HanaDataVard
SAP HANA is superfast in memeory database and plaform enabling new possibilities in terms of analytical reporting and realtime data acquisition and consumption. However, the business case is often hard to prove due to high licence costs.
Based on our experience with SAP HANA migrations we have collected the most important points for Data Management in SAP BW and system optimisation before moving all data to SAP HANA. Just in few steps you can enhance the benefits of SAP HANA. This presentation will explain you how to analyse your BW, why to implement near line storage what data to housekeep and how code optimisation will help you.
Consolidate your SAP System landscape Teched && d-code 2014Goetz Lessmann
My slide deck from this year's SAP Teched && d-code on how to consolidate SAP system landscapes - both for SAP ERP and SAP BW (and actually any other SAP driven systems). The focus is on getting rid of some misconceptions about consolidations and focusing on solutions instead of problems to achieve tangible goals: TCO savings, quick wins, and a clear way of going for a one-SAP landscape.
The document discusses the growth of data and how SAP products can help manage and analyze large amounts of data. It provides the following key details:
- The amount of data in the world has grown dramatically to 1.8 zettabytes in 2011 and 90% of the data today was created in the last two years.
- SAP offers solutions like HANA, BusinessObjects, and big data applications to help organizations capture, store, manage and analyze massive amounts of structured and unstructured data from various sources.
- HANA provides an in-memory database platform for real-time analytics while integrating with Hadoop for infinite storage and processing of large unstructured data sets.
Exclusive Verizon Employee Webinar: Getting More From Your CDR DataPentaho
This document discusses a project between Pentaho and Verizon to leverage big data analytics. Verizon generates vast amounts of call detail record (CDR) data from mobile networks that is currently stored in a data warehouse for 2 years and then archived to tape. Pentaho's platform will help optimize the data warehouse by using Hadoop to store all CDR data history. This will free up data warehouse capacity for high value data and allow analysis of the full 10 years of CDR data. Pentaho tools will ingest raw CDR data into Hadoop, execute MapReduce jobs to enrich the data, load results into Hive, and enable analyzing the data to understand calling patterns by geography over time.
Die Data Warehouse Cloud (DWC) ist SAP's neuestes Data Warehouse (DWH) Produkt. Als Software-as-a-Service Lösung basiert es auf den neuen HANA Cloud Services. Dabei soll die DWC über, kurz oder lang, in der Lage sein neben einem Self Service Data Preparation Use Case auch ein vollwertiges Enterprise Data Warehouse abzubilden.
Die am weitesten verbreiteste SAP DWH Lösung ist bisher das SAP Business Warehouse (BW). Was passiert nun mit dem SAP BW? Ist die DWC das schleichende Ende von SAP BW?
Wir beantworten diese und weitere Fragen und geben einen Überblick zur Positionierung der SAP DWH Lösungen. Anhand eines Showcases zeigen wir zudem Potentiale hybrider Architekturen auf.
This document provides an overview of SAP HANA SQL data warehousing, including the process and products. It discusses why data warehousing is still necessary, how SAP approaches it with HANA SQL data warehousing, and the next generation landscape with BW/4HANA and SQL data warehouse on the same platform. It then details the integrated data warehouse process with tools for design, development, and deployment, including SAP Enterprise Architect Designer, SAP Web IDE, native data store objects, data lifecycle manager, and deployment with DevOps. Strengths and use cases of the SQL data warehouse approach are also summarized.
SAP BusinessObjects Data Services is a data integration platform that includes standard components such as the Designer, Repository, Job Server, Engine, and Access Server. It also includes optional components and management tools. The software has a distributed architecture that allows components to be installed across an organization's network and hardware infrastructure.
The document discusses SAP BW/4HANA architecture archetypes and the transition to more agile data warehousing environments. It describes how SAP BW/4HANA architectures are evolving from traditional enterprise data warehouse approaches to more flexible, simplified architectures and hybrid models that support real-time data and virtual data marts. A case study of a large oil and gas company's implementation of SAP BW/4HANA is presented, which used a hybrid virtual data model with real-time data replication to HANA and virtual objects. Lessons learned emphasized the need for agile development methods and business ownership of the solution.
This document provides hardware sizing recommendations for the Intercompany Integration Solution for SAP Business One. It recommends minimum server configurations based on the number of company databases that will be used with the solution. For up to 4 company databases, it recommends a dual-core 2.2 GHz processor with 4GB RAM and 10GB free disk space. As the number of company databases increases, it recommends progressively more powerful server hardware to support the additional processing needs. It also notes some considerations for allocating resources to SQL Server and the integration server.
This document provides an overview of data management strategies with SAP HANA for SAP software systems. It discusses tools for managing large data volumes in SAP HANA, including data management for SAP S/4HANA using information lifecycle management, archiving, and data aging. It also covers data management for SAP HANA data warehousing using dynamic tiering and warm data management in SAP BW powered by SAP HANA. The document outlines strategies and technologies for optimizing storage costs and memory footprint while maintaining performance and data accessibility.
This document discusses principles and methods for sizing SAP HANA systems, both on-premise and in the cloud. It provides an overview of key performance indicators for SAP HANA sizing such as memory, CPU, disk I/O, and network load. Common sizing approaches like greenfield, brownfield, and bluefield sizing are described. Official SAP sizing tools like the Quick Sizer are outlined, which provide structured questionnaires and sizing results. The roles of SAP, customers, hardware vendors, and service providers in the collaborative sizing process are also reviewed.
This document provides configuration steps for setting up basic funds management functionality in SAP, including maintaining FM areas, assigning company codes and fiscal year variants, activating account assignment elements, defining business areas, configuring general ledger and financial accounting settings, and more. The detailed steps cover areas like public sector management configuration, financial documents, grants management, and funds management master data.
Become More Data-driven by Leveraging Your SAP DataDenodo
Watch full webinar here: https://bit.ly/3K2SaCQ
In today’s world, management of data can be a major challenge. For many systems, including SAP, data in real-time and integrating it with other disparate sources has historically been difficult to accomplish. The traditional Data Warehouse approach can also be quite expensive to keep data fresh and control access to meet new and future data protection requirements. Denodo and Gateway Architect’s Meister Core™ offers a high-performance data virtualization solution, designed to fulfill those needs.
Join Denodo, Gateway Architects and W5 Consulting to learn about the value of a logical Data Fabric and delivery platform and its role in this new solution. The webinar will overview the solution including how it provides support for SAP Migrations and sharing of SAP data across geographic boundaries. In addition, you will see how this solution provides the added value of improved agility for supply chain management, and much more. We will also share a demonstration to showcase the benefits of this solution.
Do not miss this opportunity to learn all this as well as how the Joint Denodo/Meister Core solution can:
- Create an agile, real-time, robust data virtualization solution.
- Work with combinations of SAP and Non-SAP data in “Actual” real time scenarios.
- And deliver a true 360 degree view of analytics from multiple systems and seemingly tie that to all your SAP FICO documents 10X faster then previously possible.
Jeffrey Word presents on SAP's in-memory database technology called SAP HANA and its role in enabling real-time business. SAP HANA provides a unified platform for both transactions and analytics by storing all data in main memory, allowing for fast real-time queries and insights. This represents an evolution from traditional disk-based systems that are unable to meet the demands of today's real-time business needs. SAP's vision is for SAP HANA to serve as the database for the SAP Business Suite and other applications, simplifying enterprise architectures and accelerating key processes.
This document provides an overview of the key steps involved in migrating data from an existing source system to a new SAP Cloud Solution target system, including: 1) scheduling activities to plan the migration, 2) cleansing the source data, 3) extracting the data from the source system, 4) populating migration templates, 5) testing the migration, 6) verifying the migrated data, and 7) performing the final migration to the production system. The data migration process spans multiple phases of the SAP Cloud implementation methodology.
This document provides a comparison of SAP BW and Teradata, two leading tools for reporting and analysis. It begins with background information on each tool, describing SAP BW as a comprehensive business intelligence package that merges, transforms, and interprets business data to support decision making. Teradata is introduced as a fully scalable relational database management system designed for analytical queries. The document then compares the pros and cons of each tool based on factors like users, value proposition, usability, interfaces, and features. SAP BW is generally better for small organizations while Teradata can handle extremely large amounts of data and thousands of users through massively parallel processing.
The document discusses Master Data Management (MDM) and SAP NetWeaver MDM. It introduces MDM as a solution to problems organizations face with inconsistent master data across different systems. SAP NetWeaver MDM can help manage master data and integrate it across various SAP and non-SAP systems. The document outlines the course objectives, provides an example business scenario, and covers key topics like MDM architecture and best practices for MDM projects.
The document provides an agenda and overview of SAP Vora 1.4. It discusses SAP Vora's role in big data and data lakes, how it addresses challenges with big data, and its usage patterns across different industries like financial services, telecommunications, oil and gas, retail, and manufacturing. Key points include that SAP Vora leverages Hadoop and Spark for scalable and affordable big data storage and processing, provides a unified access layer and simplified data modeling for different data sources, and seamlessly integrates with SAP HANA for enterprise-grade analytics.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.