Creating enterprise standards 09302010
•
1 like•961 views
The document discusses how to create enterprise data standards using CA ERwin Data Modeling. It describes leveraging naming standards, domains, user defined properties, and data type standards to promote consistency and reuse. The presentation also demonstrates sharing standards using CA ERwin Model Manager and reporting standards to stakeholders using various reporting options like Crystal Reports. Live demo examples are provided of implementing standards in CA ERwin Data Modeling.
Report
Share
Report
Share
Download to read offline
Recommended
Data warehouseold
A data warehouse is a collection of data integrated from multiple sources to support decision making. It contains subject-oriented, integrated, time-variant, and non-volatile data stored in a way that makes it readily available for analysis. Data marts can be dependent on the warehouse or independent subsets designed for specific departments. Successful implementation requires identifying data sources and governance, planning data quality and modeling, selecting ETL and database tools, and supporting end users. Key challenges include unrealistic expectations, technical issues, and ensuring ongoing value.
Mirroring and replications
This document summarizes a seminar presentation on database mirroring and replication. It defines database administration as managing database management systems and describes different types. It also defines mirroring as copying a database for redundancy and replication as copying database objects across distributed databases. Finally, it outlines the architectures for mirroring and replication and compares their advantages and disadvantages.
Upgrade webcast avoid the mess id
This document discusses migrating from SharePoint 2007 to 2010. It recommends taking advantage of new 2010 features like managed metadata, improved searching, and social features. The migration process involves planning, data migration, training, and support. It advises cleaning up content by organizing it properly, applying retention policies, and designing new term stores, site hierarchies, and content types for reuse. Migrating provides an opportunity to clean up issues from the prior system and design 2010 structures to better support access and use of content. Questions can be directed to the author.
The Data Cleansing Process - A Roadmap to Material Master Data Quality
The document outlines a 12-step process for cleansing material master data to improve data quality. The steps include pre-cleanse evaluation, establishing data standards, reviewing test data, mining and standardizing existing data, enhancing data, identifying and consolidating duplicates, addressing incomplete records, quality control review, formatting data, final delivery and upload, and implementing post-cleanse governance. The overall goal is to assess current data quality, apply standard rules and formats, research and validate data, and maintain quality going forward.
Document Archiving & Sharing System
Muhammad Ashik Iqbal presented his final year defense project on a web based document archiving and sharing system. The system allows users to store, view, search, and share scanned documents stored in the cloud. Users can preview PDF documents in their web browser and download original documents. Documents can be shared securely using encrypted URLs. The system was implemented using ASP.NET, C#, SQL Server, and other technologies. While functional, the initial system has some limitations but provides a foundation for future enhancements to document management.
ETL Testing - Introduction to ETL testing
This presentation is about the following topics
1. What is use of testing,
2. What is quality & standards,
3. Responsibilities of a ETL Tester,
Document Archieving System (DAS) by In2sol Riyadh
Document Archiving allow management of document, this application is bilingual customized and deliver to many companies in Kingdom of Saudi Arabia.
Optim Archive
InfoSphere Optim Data Growth for Archiving on z/OS provides options for archiving DB2 data from operational databases to archive databases on mainframes or Hadoop clusters. The data can then be restored, deleted from the operational database, and reported on using various tools. The archiving process involves multiple steps of archiving, restoring, deleting, and in some cases FTP, depending on the selected archiving option.
Recommended
Data warehouseold
A data warehouse is a collection of data integrated from multiple sources to support decision making. It contains subject-oriented, integrated, time-variant, and non-volatile data stored in a way that makes it readily available for analysis. Data marts can be dependent on the warehouse or independent subsets designed for specific departments. Successful implementation requires identifying data sources and governance, planning data quality and modeling, selecting ETL and database tools, and supporting end users. Key challenges include unrealistic expectations, technical issues, and ensuring ongoing value.
Mirroring and replications
This document summarizes a seminar presentation on database mirroring and replication. It defines database administration as managing database management systems and describes different types. It also defines mirroring as copying a database for redundancy and replication as copying database objects across distributed databases. Finally, it outlines the architectures for mirroring and replication and compares their advantages and disadvantages.
Upgrade webcast avoid the mess id
This document discusses migrating from SharePoint 2007 to 2010. It recommends taking advantage of new 2010 features like managed metadata, improved searching, and social features. The migration process involves planning, data migration, training, and support. It advises cleaning up content by organizing it properly, applying retention policies, and designing new term stores, site hierarchies, and content types for reuse. Migrating provides an opportunity to clean up issues from the prior system and design 2010 structures to better support access and use of content. Questions can be directed to the author.
The Data Cleansing Process - A Roadmap to Material Master Data Quality
The document outlines a 12-step process for cleansing material master data to improve data quality. The steps include pre-cleanse evaluation, establishing data standards, reviewing test data, mining and standardizing existing data, enhancing data, identifying and consolidating duplicates, addressing incomplete records, quality control review, formatting data, final delivery and upload, and implementing post-cleanse governance. The overall goal is to assess current data quality, apply standard rules and formats, research and validate data, and maintain quality going forward.
Document Archiving & Sharing System
Muhammad Ashik Iqbal presented his final year defense project on a web based document archiving and sharing system. The system allows users to store, view, search, and share scanned documents stored in the cloud. Users can preview PDF documents in their web browser and download original documents. Documents can be shared securely using encrypted URLs. The system was implemented using ASP.NET, C#, SQL Server, and other technologies. While functional, the initial system has some limitations but provides a foundation for future enhancements to document management.
ETL Testing - Introduction to ETL testing
This presentation is about the following topics
1. What is use of testing,
2. What is quality & standards,
3. Responsibilities of a ETL Tester,
Document Archieving System (DAS) by In2sol Riyadh
Document Archiving allow management of document, this application is bilingual customized and deliver to many companies in Kingdom of Saudi Arabia.
Optim Archive
InfoSphere Optim Data Growth for Archiving on z/OS provides options for archiving DB2 data from operational databases to archive databases on mainframes or Hadoop clusters. The data can then be restored, deleted from the operational database, and reported on using various tools. The archiving process involves multiple steps of archiving, restoring, deleting, and in some cases FTP, depending on the selected archiving option.
Lançamento ERwin 08/02
The document discusses CA ERwin, a data modeling tool that can be used to visualize data across different platforms through conceptual, logical, and physical data models in order to effectively manage increasingly complex data environments with multiple databases and applications. CA ERwin provides a centralized repository for storing metadata from multiple data sources and supports communication between business and technical stakeholders through intuitive reports and high-level conceptual models.
CA ERwin Data Modeler End User Presentation
CA ERwin is a suite of data modeling products that allows users to model databases across multiple platforms from a single graphical representation. It reduces costs and improves database performance by standardizing database design. The latest release, ERwin 7.3, features enhancements such as ODBC-based metadata access, a SQL query tool, and template customizations. ERwin helps users manage growing database demands with fewer resources through its ability to support multiple database platforms from a single data model.
rm006sn (2)
This document provides a case study on using CA ERwin Data Modeler (CA ERwin DM) and CA ERwin Model Manager (CA ERwin MM) across insurance industries. It discusses why these tools were chosen over other options, how strategic business elements like vision and goals impact data modeling. Key elements of the tools like opportunities/threats and capabilities are examined. The document also includes practical demonstrations of using the tools, including reverse engineering, comparing models, and integrating the tools. Appendices provide a SWOT analysis and sample data model.
Importance of data model
Data models can facilitate communication between designers, programmers, and users. A well-developed data model can improve understanding of an organization. Data models are a communication tool that represent different types of relationships in a database. Common data models include hierarchical, network, relational, entity-relationship, and object-oriented models. Each model has advantages like conceptual simplicity and flexibility as well as disadvantages like complexity and implementation limitations.
Ca e rwin state of the union 09082010
CA ERwin Modeling provides data modeling solutions to help reduce costs and increase ROI. Their next release, r8, will include improved visualization, customization, and productivity features. r8 is focused on balancing usability improvements and database support to increase user satisfaction and ROI. The data modeling market is seeing new players and a diversification in how solutions are used, with CA ERwin striving to scale their products to meet new requirements.
All data models in dbms
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
Nagendra Resume
This document provides a summary of Nagendra Kumar Busetti's work experience and qualifications. He has over 7 years of experience as a data modeler working with tools like Erwin and databases like Oracle, SQL Server, and DB2. Currently he works for TCS as a data modeler on projects for clients like Toys "R" Us, where he is responsible for logical and physical database design, maintaining metadata, and providing support to applications. Previously he worked for iGATE as a senior system engineer, where he performed similar data modeling duties for clients such as MetLife Insurance. He has a B.Tech degree from 2011 and is proficient with programming languages like SQL and T-SQL.
Mastering your data with ca e rwin dm 09082010
This document discusses using data modeling to build the foundations for strong data quality. It outlines a process with six steps: [1] defining metadata standards, [2] encouraging collaboration, [3] organizing models and data, [4] enforcing standards, [5] changing organizational culture, and [6] creating a "to be" target state. The key points are that data quality requires treating data as a valuable asset, establishing good metadata and modeling habits, and ongoing cultural changes rather than a single solution.
Sybase PowerDesigner Vs Erwin
This document provides a comprehensive analysis comparing the data modeling capabilities of Sybase PowerDesigner 16.0 InformationArchitect and CA ERwin Data Modeler r8.1 Standard Edition. It examines how each tool supports key data modeling activities like creating different types of data models (conceptual, logical, physical), impact analysis across model levels, and model integration. The analysis finds that while both tools allow creating different model types and linking models, PowerDesigner provides more robust, integrated support through dedicated model types and built-in impact/lineage analysis. It concludes PowerDesigner better enables managing relationships across complex data modeling projects.
Ernesto_Arce_ERwin_Data_Modeling
This certificate of achievement certifies that Ernesto Arce completed 3 days of training totaling 24 hours in ERwin Data Modeling. He has met all requirements and completed all exercises to receive this certification, which was issued on October 26, 2016.
Data modeling for the business 09282010
This document discusses using high-level data modeling to facilitate communication between business and IT stakeholders. It provides examples of high-level data models and discusses best practices for building high-level models, including getting input from all relevant parties, choosing an intuitive notation, and using the model to achieve consensus on key business concepts and definitions. The document also describes how modeling tools from CA like ERwin can help manage technical data sources from multiple systems and databases, and share information with various audiences.
Integrating data process a roundtrip modeling using e rwin data modeler_erwin...
This document discusses integrating data and process modeling using ERwin Data Modeler and ERwin Process Modeler. It provides an overview of the modeling tools and a 4 step process for mapping process models to data models. The steps include: 1) Mapping entities to process arrows, 2) Mapping attributes to entities, 3) Identifying process actions on entities, and 4) Identifying process actions on attributes. Rules for allowable actions on entities and attributes based on their usage in the process model are also defined.
Using ca e rwin modeling to asure data 09162010
Data profiling analyzes data content to infer metadata and increase the accuracy of data assets and models. It can help with data quality assessments, master data management, and reducing risks in data warehousing projects. The presentation provided examples of how profiling was used to uncover issues, validate models and requirements, standardize values, and reduce development times for various organizations.
Cust experience a practical guide 09152010
This document outlines how WorkSafe BC adopted CA ERwin Data Modeler and CA ERwin Model Manager to build and manage their enterprise data models. They faced challenges like standards adoption, new tool learning curves, and change management. They built infrastructure like standards, templates, and procedures. They then built their enterprise data model by merging existing models and resolving conflicts. For model management, they implemented processes for checking models in and out, using the model manager. This centralized their metadata and allowed for faster development and data consistency across projects.
Sneak peak ca e rwin data modeler r8 preview09222010
This document provides an overview of new features and enhancements in the upcoming CA ERwin Data Modeler r8 release. Key highlights include a state-of-the-art visualization with dynamic and customizable user interface, productivity-enhancing workflows, support for additional databases, and improvements to modeling tools, editors, and licensing. The goal is to provide the leading data modeling solution while balancing usability, productivity, and database currency.
Rm006sn ca world2010
Paper published as speaker CA World 2010 at Las Vegas, USA.
Speaker & Author: Rasananda Behera Insurance Industry Expert on Enterprise Architecture [Business, Data & Applications] Management.
Different data models
This document discusses different types of data models, including hierarchical, network, relational, and object-oriented models. It focuses on explaining the relational model. The relational model organizes data into tables with rows and columns and handles relationships using keys. It allows for simple and symmetric data retrieval and integrity through mechanisms like normalization. The relational model is well-suited for the database assignment scenario because it supports linking data across multiple tables using primary and foreign keys, and provides query capabilities through SQL.
Dbms models
The document provides an introduction to database management systems (DBMS) and database models. It defines key terms like data, database, DBMS, file system vs DBMS. It describes the evolution of DBMS from 1960 onwards and different database models like hierarchical, network and relational models. It also discusses the roles of different people who work with databases like database designers, administrators, application programmers and end users.
Data models
This document discusses different types of data models, including object based models like entity relationship and object oriented models, physical models that describe how data is stored, and record based logical models. It specifically mentions hierarchical, network, and relational models as examples of record based logical data models. The purpose of data models is to represent and make data understandable by specifying rules for database construction, allowed data operations, and integrity.
Data Modeling PPT
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
Webinar: Initiating a Customer MDM/Data Governance Program
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
Akili Data Integration using PPDM
Akili provides data integration and management services for oil and gas companies. They leverage over 25 years of experience and experts in SAP, BI platforms, financial systems, and oil and gas data. Akili helps customers address challenges around data quality, reliability, disparate systems and gaining a single view of data. They provide predefined solutions and accelerators using industry standards from PPDM (Professional Petroleum Data Management). Akili's approach involves assessing an organization's data maturity, developing a data integration strategy, addressing governance, master data and tools to integrate data from multiple sources and systems into meaningful business information.
More Related Content
Viewers also liked
Lançamento ERwin 08/02
The document discusses CA ERwin, a data modeling tool that can be used to visualize data across different platforms through conceptual, logical, and physical data models in order to effectively manage increasingly complex data environments with multiple databases and applications. CA ERwin provides a centralized repository for storing metadata from multiple data sources and supports communication between business and technical stakeholders through intuitive reports and high-level conceptual models.
CA ERwin Data Modeler End User Presentation
CA ERwin is a suite of data modeling products that allows users to model databases across multiple platforms from a single graphical representation. It reduces costs and improves database performance by standardizing database design. The latest release, ERwin 7.3, features enhancements such as ODBC-based metadata access, a SQL query tool, and template customizations. ERwin helps users manage growing database demands with fewer resources through its ability to support multiple database platforms from a single data model.
rm006sn (2)
This document provides a case study on using CA ERwin Data Modeler (CA ERwin DM) and CA ERwin Model Manager (CA ERwin MM) across insurance industries. It discusses why these tools were chosen over other options, how strategic business elements like vision and goals impact data modeling. Key elements of the tools like opportunities/threats and capabilities are examined. The document also includes practical demonstrations of using the tools, including reverse engineering, comparing models, and integrating the tools. Appendices provide a SWOT analysis and sample data model.
Importance of data model
Data models can facilitate communication between designers, programmers, and users. A well-developed data model can improve understanding of an organization. Data models are a communication tool that represent different types of relationships in a database. Common data models include hierarchical, network, relational, entity-relationship, and object-oriented models. Each model has advantages like conceptual simplicity and flexibility as well as disadvantages like complexity and implementation limitations.
Ca e rwin state of the union 09082010
CA ERwin Modeling provides data modeling solutions to help reduce costs and increase ROI. Their next release, r8, will include improved visualization, customization, and productivity features. r8 is focused on balancing usability improvements and database support to increase user satisfaction and ROI. The data modeling market is seeing new players and a diversification in how solutions are used, with CA ERwin striving to scale their products to meet new requirements.
All data models in dbms
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
Nagendra Resume
This document provides a summary of Nagendra Kumar Busetti's work experience and qualifications. He has over 7 years of experience as a data modeler working with tools like Erwin and databases like Oracle, SQL Server, and DB2. Currently he works for TCS as a data modeler on projects for clients like Toys "R" Us, where he is responsible for logical and physical database design, maintaining metadata, and providing support to applications. Previously he worked for iGATE as a senior system engineer, where he performed similar data modeling duties for clients such as MetLife Insurance. He has a B.Tech degree from 2011 and is proficient with programming languages like SQL and T-SQL.
Mastering your data with ca e rwin dm 09082010
This document discusses using data modeling to build the foundations for strong data quality. It outlines a process with six steps: [1] defining metadata standards, [2] encouraging collaboration, [3] organizing models and data, [4] enforcing standards, [5] changing organizational culture, and [6] creating a "to be" target state. The key points are that data quality requires treating data as a valuable asset, establishing good metadata and modeling habits, and ongoing cultural changes rather than a single solution.
Sybase PowerDesigner Vs Erwin
This document provides a comprehensive analysis comparing the data modeling capabilities of Sybase PowerDesigner 16.0 InformationArchitect and CA ERwin Data Modeler r8.1 Standard Edition. It examines how each tool supports key data modeling activities like creating different types of data models (conceptual, logical, physical), impact analysis across model levels, and model integration. The analysis finds that while both tools allow creating different model types and linking models, PowerDesigner provides more robust, integrated support through dedicated model types and built-in impact/lineage analysis. It concludes PowerDesigner better enables managing relationships across complex data modeling projects.
Ernesto_Arce_ERwin_Data_Modeling
This certificate of achievement certifies that Ernesto Arce completed 3 days of training totaling 24 hours in ERwin Data Modeling. He has met all requirements and completed all exercises to receive this certification, which was issued on October 26, 2016.
Data modeling for the business 09282010
This document discusses using high-level data modeling to facilitate communication between business and IT stakeholders. It provides examples of high-level data models and discusses best practices for building high-level models, including getting input from all relevant parties, choosing an intuitive notation, and using the model to achieve consensus on key business concepts and definitions. The document also describes how modeling tools from CA like ERwin can help manage technical data sources from multiple systems and databases, and share information with various audiences.
Integrating data process a roundtrip modeling using e rwin data modeler_erwin...
This document discusses integrating data and process modeling using ERwin Data Modeler and ERwin Process Modeler. It provides an overview of the modeling tools and a 4 step process for mapping process models to data models. The steps include: 1) Mapping entities to process arrows, 2) Mapping attributes to entities, 3) Identifying process actions on entities, and 4) Identifying process actions on attributes. Rules for allowable actions on entities and attributes based on their usage in the process model are also defined.
Using ca e rwin modeling to asure data 09162010
Data profiling analyzes data content to infer metadata and increase the accuracy of data assets and models. It can help with data quality assessments, master data management, and reducing risks in data warehousing projects. The presentation provided examples of how profiling was used to uncover issues, validate models and requirements, standardize values, and reduce development times for various organizations.
Cust experience a practical guide 09152010
This document outlines how WorkSafe BC adopted CA ERwin Data Modeler and CA ERwin Model Manager to build and manage their enterprise data models. They faced challenges like standards adoption, new tool learning curves, and change management. They built infrastructure like standards, templates, and procedures. They then built their enterprise data model by merging existing models and resolving conflicts. For model management, they implemented processes for checking models in and out, using the model manager. This centralized their metadata and allowed for faster development and data consistency across projects.
Sneak peak ca e rwin data modeler r8 preview09222010
This document provides an overview of new features and enhancements in the upcoming CA ERwin Data Modeler r8 release. Key highlights include a state-of-the-art visualization with dynamic and customizable user interface, productivity-enhancing workflows, support for additional databases, and improvements to modeling tools, editors, and licensing. The goal is to provide the leading data modeling solution while balancing usability, productivity, and database currency.
Rm006sn ca world2010
Paper published as speaker CA World 2010 at Las Vegas, USA.
Speaker & Author: Rasananda Behera Insurance Industry Expert on Enterprise Architecture [Business, Data & Applications] Management.
Different data models
This document discusses different types of data models, including hierarchical, network, relational, and object-oriented models. It focuses on explaining the relational model. The relational model organizes data into tables with rows and columns and handles relationships using keys. It allows for simple and symmetric data retrieval and integrity through mechanisms like normalization. The relational model is well-suited for the database assignment scenario because it supports linking data across multiple tables using primary and foreign keys, and provides query capabilities through SQL.
Dbms models
The document provides an introduction to database management systems (DBMS) and database models. It defines key terms like data, database, DBMS, file system vs DBMS. It describes the evolution of DBMS from 1960 onwards and different database models like hierarchical, network and relational models. It also discusses the roles of different people who work with databases like database designers, administrators, application programmers and end users.
Data models
This document discusses different types of data models, including object based models like entity relationship and object oriented models, physical models that describe how data is stored, and record based logical models. It specifically mentions hierarchical, network, and relational models as examples of record based logical data models. The purpose of data models is to represent and make data understandable by specifying rules for database construction, allowed data operations, and integrity.
Data Modeling PPT
The document discusses data modeling, which involves creating a conceptual model of the data required for an information system. There are three types of data models - conceptual, logical, and physical. A conceptual data model describes what the system contains, a logical model describes how the system will be implemented regardless of the database, and a physical model describes the implementation using a specific database. Common elements of a data model include entities, attributes, and relationships. Data modeling is used to standardize and communicate an organization's data requirements and establish business rules.
Viewers also liked (20)
Integrating data process a roundtrip modeling using e rwin data modeler_erwin...
Integrating data process a roundtrip modeling using e rwin data modeler_erwin...
Sneak peak ca e rwin data modeler r8 preview09222010
Sneak peak ca e rwin data modeler r8 preview09222010
Similar to Creating enterprise standards 09302010
Webinar: Initiating a Customer MDM/Data Governance Program
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
Akili Data Integration using PPDM
Akili provides data integration and management services for oil and gas companies. They leverage over 25 years of experience and experts in SAP, BI platforms, financial systems, and oil and gas data. Akili helps customers address challenges around data quality, reliability, disparate systems and gaining a single view of data. They provide predefined solutions and accelerators using industry standards from PPDM (Professional Petroleum Data Management). Akili's approach involves assessing an organization's data maturity, developing a data integration strategy, addressing governance, master data and tools to integrate data from multiple sources and systems into meaningful business information.
Data Management Workshop - ETOT 2016
In his Data Management Workshop at the 8th ETOT Summit in London, October 2016 - DataGenic's CTO Colin Hartley looked at trends and best practice when it comes to commodity data management. As well as sharing the dos and don'ts of forward curve creation and management.
Microsoft master data services mds overview
Master data management (MDM) is a technology discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
Master data management tools can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data.
Microsoft Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions.
Other Master Data Services features include hierarchies, granular security, transactions, data versioning, and business rules.
Master Data Services includes the following components and tools:
- Master Data Services Configuration Manager, a tool you use to create and configure Master Data Services databases and web applications.
- Master Data Manager, a web application you use to perform administrative tasks (like creating a model or business rule), and that users access to update data.
- MDSModelDeploy.exe, a tool you use to create packages of your model objects and data so you can deploy them to other environments.
- Master Data Services web service, which developers can use to extend or develop custom solutions for Master Data Services.
Marlabs Capabilities Overview: DWBI, Analytics and Big Data Services
Marlabs’ Business Intelligence and Analytics practice can support customers’ needs throughout the information management lifecycle. As a vendor-agnostic and holistic service provider with expertise in a range of tools and technologies, we can help clients make informed decisions to employ the right technologies that align with their business needs.
Geek Sync | Avoid the Seven Mistakes Data Modelers Make in Aiding Data Govern...
You can watch the replay for this Geek Sync webcast, Avoid the Seven Mistakes Data Modelers Make in Aiding Data Governance, in the IDERA Resource Center, http://ow.ly/nCrq50A4q8G.
Data privacy, protection, and compliance legislation is becoming ever more important. In that context, organizations have been looking towards their data governance teams to make sure that they understand their data, know how it is classified, and where it resides.
In this session, join Karen Lopez in discussing the mistakes that data modelers make in supporting data governance programs — and that you should avoid! These mistakes include collaboration errors, data model security fails, data stewarding missteps, data model integrity harms, and more.
Newer compliance regulations can make these mistakes costly and difficult to recover from. Karen wants you to love your data — and your data model!
Speaker: Karen Lopez has more than 20 years of database design experience. She specializes in the practical application of design approaches, balancing development time frames with the need to deliver solutions that will support business agility and data quality needs. She’s known for her fun and engaging speaking and teaching style. She tweets about data, space exploration and her travel experiences at @datachick. Karen blogs at www.datamodel.com.
Pr dc 2015 sql server is cheaper than open source
SQL Server was found to be cheaper than open source options for a data warehouse project with the following requirements:
- Serve 100% operational reports from 1TB of data
- No need for advanced features like big data support
- Requirement was for basic textual reporting
An investigation was conducted of SQL Server, Oracle, Sybase, MySQL, and PostgreSQL. SQL Server and PostgreSQL were evaluated further based on costs and functionality. After a 10 year total cost of ownership analysis, SQL Server was found to be cheaper despite having a higher initial license cost. The lessons learned were that open source options are not always cheaper, to test options yourself rather than rely on biased reports, and that Oracle is very expensive.
Model Confidence for Master Data with David Loshin
Watch the webinar at: http://embt.co/1OMDHK7
Although master data management (MDM) systems have been deployed in numerous industries and organizations, the vision of creating an overall “single source of truth” is beginning to yield to a more pragmatic perspective of providing visibility to shared information about uniquely-identifiable entities within the enterprise. This more mature approach sheds light on some of the potential gaps associated with the typical out-of-the-box data models for customer or product.
In this webinar, David Loshin addresses data modeling for MDM systems, and share insights about:
+ Some of the complexities emerging from reliance on canned master data models
+ Alternatives for revising how master data entities are viewed and consumed within the enterprise
+ How a consumption-oriented engagement process will help the master data modeler devise thoughtful conceptual and logical representations of shared master data
He also discusses how these different ways of looking at master data modeling will help reduce complexity for master data adoption, system interoperability, and legacy migration.
Learn more about ER/Studio at http://www.embarcadero.com/products/er-studio
Architect’s Open-Source Guide for a Data Mesh Architecture
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
MDM & BI Strategy For Large Enterprises
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
Foundation of Business Intelligence for Business Firms .ppt
This document discusses business intelligence and data analytics. It describes how contemporary business intelligence infrastructure utilizes tools like data warehouses, data marts, Hadoop, in-memory computing, and analytical platforms to manage and analyze large amounts of structured and unstructured data from multiple sources. It also discusses various analytical tools for data mining, online analytical processing, text mining, and web mining that can reveal patterns to help businesses make better decisions.
The Shifting Landscape of Data Integration
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Akili Oil & Gas Data Practice - PPDM
Akili offers an end to end approach to oil and gas upstream data management which includes: data strategy, governance, sustainability, Master Well Data Structure, system integration, system synchronization and data harmony.
Akili leverages best practices from PPDM (Professional Petroleum Data Management Group and applies our experiences leveraging the PPDM models with other companies.
SCRIMPS-STD: Test Automation Design Principles - and asking the right questions!
Download, install, record… we’re automating!??
Asking good questions and building good design
to reach reasonable Automation outcomes
Strategically manage data quality in an erp rollout
Many large companies with complex IT landscapes are consolidating ERP/ EAM platforms to deliver improved operational visibility and decision velocity to business.
While such roll out projects focus on aligning business requirements with ERP/EAM functionalities and configurations, many teams overlook one of the most business-critical factors - Strategic data quality.
View this 40 minute webinar, to learn :
Tactical v/s Strategic approaches to data quality in ERP roll-outs
Case studies of large ERP roll outs - winners and losers
Pitfalls to avoid in ERP/ EAM material data quality projects
Strategically Manage Data Quality in an ERP Rollout
Many large companies with complex IT landscapes are consolidating ERP/ EAM platforms to deliver improved operational visibility and decision velocity to business.
While such roll out projects focus on aligning business requirements with ERP/EAM functionalities and configurations, many teams overlook one of the most business-critical factors - Strategic data quality.
This deck seeks to covers-
Tactical v/s Strategic approaches to data quality in ERP roll-outs
Case studies of large ERP roll outs - winners and losers
Pitfalls to avoid in ERP/ EAM material data quality projects
Cloud Strategy
Methods for deploying in the cloud, best practices and the importance of including all users, stake holders in the process.
ATAGTR2017 Performance Testing and Non-Functional Testing Strategy for Big Da...
ATAGTR2017 Performance Testing and Non-Functional Testing Strategy for Big Da...Agile Testing Alliance
The presentation on Performance Testing and Non-Functional Testing Strategy for Big Data Applications was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Abhinav GuptaDevOps Spain 2019. Olivier Perard-Oracle
This document discusses DataOps, which is an agile methodology for developing and deploying data-intensive applications. DataOps supports cross-functional collaboration and fast time to value. It expands on DevOps practices to include data-related roles like data engineers and data scientists. The key goals of DataOps are to promote continuous model deployment, repeatability, productivity, agility, self-service, and to make data central to applications. It discusses how DataOps brings flexibility and focus to data-driven organizations through principles like continuous model deployment, improved efficiency, and faster time to value.
Achieving a Single View of Business – Critical Data with Master Data Management
This document discusses achieving a single view of critical business data through master data management (MDM). It outlines how MDM can consolidate data from various internal and external sources to provide a centralized, trusted view across different business domains. The key benefits of MDM include improved data quality, governance and compliance. It also enables contextual insights and more informed decision-making through cross-domain intelligence and analytics. Successful MDM requires flexible technologies, processes and organizational support to ensure data governance and deliver ongoing value.
Similar to Creating enterprise standards 09302010 (20)
Webinar: Initiating a Customer MDM/Data Governance Program
Webinar: Initiating a Customer MDM/Data Governance Program
Marlabs Capabilities Overview: DWBI, Analytics and Big Data Services
Marlabs Capabilities Overview: DWBI, Analytics and Big Data Services
Geek Sync | Avoid the Seven Mistakes Data Modelers Make in Aiding Data Govern...
Geek Sync | Avoid the Seven Mistakes Data Modelers Make in Aiding Data Govern...
Model Confidence for Master Data with David Loshin
Model Confidence for Master Data with David Loshin
Architect’s Open-Source Guide for a Data Mesh Architecture
Architect’s Open-Source Guide for a Data Mesh Architecture
Foundation of Business Intelligence for Business Firms .ppt
Foundation of Business Intelligence for Business Firms .ppt
SCRIMPS-STD: Test Automation Design Principles - and asking the right questions!
SCRIMPS-STD: Test Automation Design Principles - and asking the right questions!
Strategically manage data quality in an erp rollout
Strategically manage data quality in an erp rollout
Strategically Manage Data Quality in an ERP Rollout
Strategically Manage Data Quality in an ERP Rollout
ATAGTR2017 Performance Testing and Non-Functional Testing Strategy for Big Da...
ATAGTR2017 Performance Testing and Non-Functional Testing Strategy for Big Da...
Achieving a Single View of Business – Critical Data with Master Data Management
Achieving a Single View of Business – Critical Data with Master Data Management
More from ERwin Modeling
Zen of metadata 09212010
The document discusses metadata and the importance of metadata management. It describes the speaker's role as a data architect at the City and County of San Francisco Department of Public Works. It outlines an agenda covering data, metadata, business requirements, metadata types and philosophies, how the CIA compiles and uses metadata, and examples. It emphasizes that metadata is data about data and discusses the co-existence of data and metadata in business processes and requirements.
Staying relevant in todays changing dm environment 09282010
Thank you for the presentation. Does anyone have any questions?
Monetizing data management 09162010
not the same. Wisdom is
1) Organizations have lost millions due to poor data management practices but remain unaware of the root causes. the ability to apply one's
2) Unless the costs of poor data management are quantified, gaining approval for basic investments will be difficult. knowledge and experience
3) The talk illustrates how to identify specific costs of poor practices in HR, finance, supply chain, and compliance with good judgment.
to show data management as the root cause of problems and gain support for required investments.
Effective capture of metadata using ca e rwin data modeler 09232010
CA ERwin Data Modeler provides flexible features to effectively capture metadata in data warehouse environments. This includes data sources, transformation rules, and data movement rules. It uses a customer dimension example to demonstrate capturing source tables from various sources, defining transformation templates, and attaching data movement rules to tables. Other options like importing metadata definitions from Excel allow business stakeholders to define and import column metadata. Effective metadata capture helps communicate requirements, identify issues, and understand the data model.
Deciding to go cloud 09212010
This document discusses a methodology for organizations to assess moving their data and applications to the cloud. It involves 5 steps: 1) conducting a thorough analysis of business processes, 2) utilizing data modeling to understand how data relates, 3) generating prototypes using simulated data, 4) reevaluating the cloud offering if needed, and 5) preparing a transition initiative including training for both IT and business users. The methodology helps organizations prioritize what data and applications make the most sense to move first by addressing concerns around data privacy, security and control.
Ca e rwin modeling global user communities_09232010 - webcast
Tom Bilcze, president of the CA Technologies Modeling Global User Communities, provided an overview of the global and regional user communities for CA ERwin® modeling tools. He discussed the resources and activities offered through the global community portal on communities.ca.com. Bilcze also provided updates on recent activities like feedback from regional presidents and the board's focus on the upcoming CA ERwin® 8 release and knowledge sharing initiatives. He encouraged attendees to get involved with their local and global user communities.
10 things to avoid in data model 09242010
The document discusses 10 strategic and detailed pitfalls to avoid in data modeling. Strategic pitfalls include having a vague purpose, modeling literally instead of abstracting, creating large models, including speculative content, and lack of clarity. Detailed pitfalls include recklessly violating normal forms, including needless redundancy, using parallel attributes, symmetric relationships, and anonymous fields. The document emphasizes that data modeling is pivotal to a project's success and avoiding these pitfalls can improve data quality, extensibility, and performance.
5 physical data modeling blunders 09092010
The document discusses 5 common physical data modeling mistakes that can negatively impact performance, development timelines, and cause bugs. These include: 1) Assuming that numbers will always be numeric, 2) Choosing the wrong primary key, 3) Incorrectly applying surrogate keys, 4) Turning off referential integrity for development, and 5) Relying on default settings rather than customizing the physical model. The presentation provides tips on how to identify and avoid these mistakes such as establishing primary key standards, using the correct data types, and testing physical models in a separate database before implementing.
Optimizing the design of your data warehouse 09222010
This document discusses optimizing the design of a data warehouse using ERwin Data Modeler (ERwin DM) and ERwin Process Modeler (ERwin PM). It recommends using ERwin PM to model the data flow through context and level 1 diagrams, then using ERwin DM to further model external entities, data stores, and activities identified in the diagrams. Subject areas in ERwin DM are named based on the activity/entity they represent. Together, the two tools provide technical and business users a comprehensive understanding of how data will flow and be stored in the data warehouse.
More from ERwin Modeling (9)
Staying relevant in todays changing dm environment 09282010
Staying relevant in todays changing dm environment 09282010
Effective capture of metadata using ca e rwin data modeler 09232010
Effective capture of metadata using ca e rwin data modeler 09232010
Ca e rwin modeling global user communities_09232010 - webcast
Ca e rwin modeling global user communities_09232010 - webcast
Optimizing the design of your data warehouse 09222010
Optimizing the design of your data warehouse 09222010
Recently uploaded
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Recently uploaded (20)
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Creating enterprise standards 09302010
- 1. Creating Enterprise Standards with CA ERwin® Data Modeling Terry Hanks – CA Presales Consultant
- 2. Abstract • As data volumes increase, and organizations are faced with data integration challenges associated with mergers and acquisitions, master data management, business intelligence, and/or data governance initiatives, the need to create consistent, reusable standards is more critical than ever before. Standards help increase efficiency and quality by promoting reuse across the organization. PAGE 2
- 3. Speaker Bio • Terry Hanks is a CA Technologies Presales Consultant for the CA Erwin Modeling Family of products. He has worked with the Modeling Product line for 5 years. • Prior to joining CA Technologies, Terry worked in the Health care IT industry. PAGE 3
- 4. Agenda • Why Use Enterprise Data Standards • Implementing Standards with CA ERwin Modeling – Leveraging Naming Standards and Domains – User Defined Properties (UDP’s) – Defining Data Type Standards – Using Model Templates – Sharing Standards using CA ERwin Model Manager – Reporting to Stakeholders using Crystal Reports • Live Demo • Q&A PAGE 4
- 5. Why use Enterprise Standards? • Business decisions are based on data – bad data or design results in bad business decisions • Benefits of using data standards: – Increased Efficiency – Increased Data Quality – Promotes Reuse and Communication across the organization. PAGE 5
- 6. Who uses Enterprise Standards? • Many types of initiatives can benefit from Enterprise Standards Data Warehousing Master Data Management (MDM) Mergers and Acquisitions Data Integration Application Development Data Quality Data Governance PAGE 6
- 7. Using Naming Standards • What is a Naming Standard? – A glossary of words and abbreviations that can be used in model object names • Why use the Naming Standards Editor? – Develop model naming standards – Check model names for accuracy – Name model objects in models derived from other models – Reuse of standards across the enterprise PAGE 7
- 8. Using Domains • What is a Domain? – A set of values and rules assigned to a data element • Example: Data types, Validation rules – Used to quickly assign properties to columns • Why use Domains? – Speeds Design process – Easy to maintain – Use across entire model PAGE 8
- 9. Using User Defined Properties (UDPs) • What is a UDP ? – Provides the ability to define custom model object properties • Why use UDPs? – Further documentation of model objects – Clarification for users or viewers of models – Standardize usage across models • Examples of Common UDPs – Data Stewards, Status, Document attachments, etc. PAGE 9
- 10. Using Datatype Standards • What is a Datatype Standards file? – Includes user-defined datatypes and mapping information that is needed to convert logical & physical datatypes. • Why use a Datatype Standards file? – Converting models from Logical to Physical – Converting models from one Physical DBMS to another – Deriving multiple models from one source – Introducing new data types PAGE 10
- 11. Using Model Templates with Standards • What is a Model Template? – A starting point for new models containing reusable objects such as: • Domains • Naming standards • Formatting options • Etc. • Why use a Model Template? – Provides Standardization and Reuse – Speeds up development/altering of models/databases PAGE 11
- 12. Sharing Standards Using CA ERwin Model Manager • What is CA ERwin Model Manager? – Central location for storing standards, such as: • Naming Standards • Datatype Standards • Model Templates – Collaboration for use, reuse, and audit • Why use CA ERwin Model Manager? – Reuse of core data assets – Efficiencies for team collaboration – Access to other models for sharing objects via Complete Compare PAGE 12
- 13. Reporting to Stakeholders • Sharing information in customizable formats • CA ERwin Data Modeler provides multiple reporting options – HTML – PDF – Text – RTF – Crystal Reports – OBDC queries • In addition, these reports may be filtered by Subject Areas or Stored Displays PAGE 13
- 14. Demo • CA ERwin Data Modeler Enterprise Standards Demo PAGE 14
- 15. Visit www.ERwin.com PAGE 15