Data Catalog in Denodo Platform 7.0: Creating a Data Marketplace with Data Vi...Denodo
This document discusses using Denodo's data virtualization platform to create a data marketplace. It describes how the Denodo Data Catalog integrated with the data virtualization layer allows business users to discover, access, customize and share data views. The catalog provides metadata about available datasets and allows users to preview the actual data. This creates a single point of access for self-service business intelligence and application development across the organization. The presentation concludes with a demo of the Denodo Data Catalog capabilities.
Vensai Consultants is an IT consulting firm that specializes in building data warehouses. They provide a roadmap for building a data warehouse that includes data acquisition, integration, storage in a data repository, and reporting services. They recommend tools for each step of the data warehouse development process, including data modeling, ETL, databases, analytics, and reporting tools.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
The document discusses data warehousing, including its purpose of realizing value from data to support better business decisions. A data warehouse contains integrated data from multiple sources to support analysis. It discusses the components of a data warehouse like staging areas, data marts, and operational data stores. The document also covers topics like the evolution of data warehouse architectures, complexities in creating a data warehouse, potential pitfalls, and best practices.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Data Catalog in Denodo Platform 7.0: Creating a Data Marketplace with Data Vi...Denodo
This document discusses using Denodo's data virtualization platform to create a data marketplace. It describes how the Denodo Data Catalog integrated with the data virtualization layer allows business users to discover, access, customize and share data views. The catalog provides metadata about available datasets and allows users to preview the actual data. This creates a single point of access for self-service business intelligence and application development across the organization. The presentation concludes with a demo of the Denodo Data Catalog capabilities.
Vensai Consultants is an IT consulting firm that specializes in building data warehouses. They provide a roadmap for building a data warehouse that includes data acquisition, integration, storage in a data repository, and reporting services. They recommend tools for each step of the data warehouse development process, including data modeling, ETL, databases, analytics, and reporting tools.
The document discusses different types of data marts:
- Dependent data marts draw data directly from a centralized data warehouse, allowing for unified data access but with a focus on a specific group's needs.
- Independent data marts are standalone systems built from direct access to operational or external data sources without using a centralized warehouse. They are suitable for smaller groups.
- Hybrid data marts can integrate data from both a centralized warehouse and other sources, providing flexibility for ad hoc integration needs.
The document discusses data warehousing, including its purpose of realizing value from data to support better business decisions. A data warehouse contains integrated data from multiple sources to support analysis. It discusses the components of a data warehouse like staging areas, data marts, and operational data stores. The document also covers topics like the evolution of data warehouse architectures, complexities in creating a data warehouse, potential pitfalls, and best practices.
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
The document discusses data warehousing and describes its key characteristics and components. It defines a data warehouse as a copy of transaction data structured for querying and reporting to support strategic decision making. It outlines the stages of constructing a data warehouse including extraction, integration, and dimensional analysis to design the data warehouse database.
The key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
Reconciling your Enterprise Data Warehouse to Source SystemsMethod360
Implementing and an enterprise BI system is a significant organization investment. Too many times the expected benefit of that investment isn’t realized due to inconsistent data between the organization’s operational and BI systems.
This webcast will explain several options to enable your organization to leverage its investment by providing options to reconcile the data from source operational systems to BI.
Data warehousing involves collecting data from different sources and organizing it in a way that allows for analysis to make business decisions. It provides a single, complete view of data that end users can easily understand. A data warehouse stores integrated data from multiple sources and provides historical views of data to support analysis. It allows organizations to access critical information to support reporting, queries and decision making. Common applications of data warehousing include banking, healthcare, airlines and telecommunications.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
Data warehousing and online analytical processing (OLAP) allow organizations to consolidate data from multiple sources and analyze it to answer business questions. A data warehouse stores integrated and subject-oriented data to support organizational decision making. OLAP transforms the data into meaningful information through operations like roll-ups, drills downs, slicing and dicing to enable interactive analysis. Data mining then identifies patterns and relationships in the warehoused data to provide intelligence for businesses.
This document discusses data warehousing, including its definition, importance, components, strategies, ETL processes, and considerations for success and pitfalls. A data warehouse is a collection of integrated, subject-oriented, non-volatile data used for analysis. It allows more effective decision making through consolidated historical data from multiple sources. Key components include summarized and current detailed data, as well as transformation programs. Common strategies are enterprise-wide and data mart approaches. ETL processes extract, transform and load the data. Clean data and proper implementation, training and maintenance are important for success.
(1) The document discusses data warehousing, business intelligence, and their relationship to addressing challenges from multiple data sources.
(2) A layered scalable architecture is presented as a reference architecture for data warehouses to provide reliable, consistent, and understandable data from different source systems.
(3) Big data is also discussed in relation to data warehousing, noting differences in schema and consistency needs between traditional warehouses and big data systems handling high volumes and varieties of data.
This document provides an overview of data warehousing, including its definition, typical architecture, methodologies, advantages, and disadvantages. It defines a data warehouse as a collection of integrated, non-volatile data used to support organizational decision-making. The typical architecture includes layers for operational data, a data access layer, metadata, an informational access layer, and presentation tools. Methodologies include bottom-up design starting with data marts and top-down design using a normalized enterprise data model. Advantages include resolving inconsistencies and retrieving data without impacting operations, while disadvantages include latency and limitations with unstructured data.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
RDBMS gave us table schemas. A table schema, which is an essential metadata component, gave us the power to validate data types, and enforce constraints. In the age of varying data and schema-less data stores, how can we enforce these rules and how can we leverage metadata (even in RDBMS) to empower data validity, code checks, and automation.
This is a brief background into Big data (data lake) to put in context the importance of metadata from a governance perspective and more especially in todays heterogeneous big data platforms.
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
This document discusses various business intelligence tools for data analysis including ETL, OLAP, reporting, and metadata tools. It provides evaluation criteria for selecting tools, such as considering budget, requirements, and technical skills. Popular tools are identified for each category, including Informatica, Cognos, and Oracle Warehouse Builder. Implementation requires determining sources, data volume, and transformations for ETL as well as performance needs and customization for OLAP and reporting.
This document discusses data mining in marketing. It begins with defining data mining and explaining why it is important for predicting trends and analyzing customer behavior. The purpose of data mining in marketing is to better understand customers to target campaigns accurately. Key techniques discussed include market basket analysis, social media marketing, and knowledge-based marketing. Common data mining tools for marketing include RapidMiner, WEKA, and Python libraries. In conclusion, data mining can help businesses increase profits, reduce costs, and analyze customer purchase patterns.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
The data services marketplace is enabled by a data abstraction layer that supports rapid development of operational applications and single data view portals. In this presentation yo will learn services-based reference architecture, modality, and latency of data access.
- Reference architecture for enterprise data services marketplace
- Modality and latency of data access
- Customer use cases and demo
This presentation is part of the Denodo Educational Seminar , and you can watch the video here goo.gl/vycYmZ.
The document discusses two types of data marts: independent and dependent. Independent data marts focus on a single subject area but are not designed enterprise-wide, examples include manufacturing or finance. They are quicker and cheaper to build but can contain duplicate data and inconsistencies. Dependent data marts get their data from an enterprise data warehouse, offering benefits like improved performance, security, and key performance indicator tracking. The document also outlines the key steps in designing, building, populating, accessing, and managing a data mart project.
This document provides an overview of data warehousing and online analytical processing (OLAP). It defines a data warehouse as a single, consistent store of subject-oriented data obtained from various sources to support end-user business analysis and decision-making. OLAP allows users to easily perform complex multidimensional analyses of data in areas such as comparisons, aggregations, and rankings. The document also discusses key aspects of data warehousing such as extraction, transformation, loading, and management of data from operational systems into the warehouse to support OLAP and decision support.
This document discusses various aspects of data marts, including external data, reference data, performance issues, monitoring requirements, and security. External data is stored in the data warehouse to avoid redundancy. Reference data cannot be modified and is copied from the data warehouse. Performance considerations for data marts are different from OLAP environments, with response times ranging from 1 minute to 24 hours. Monitoring helps track data access, users, usage times, and content growth. Security measures like firewalls, login/logout, and encryption are needed to protect sensitive information in data marts.
The document discusses the Department of Defense's (DoD) policy awareness and data reference model (DRM) for enabling information sharing across agencies. The DRM provides a framework for horizontal and vertical data sharing independently of individual agency systems. It defines common ways to represent, classify and describe data to facilitate integration and access. The model is driven by model-driven architecture principles and aims to abstract data sources and details to promote extensibility. Communities of interest are identified as key to implementing the DoD's net-centric data strategy goals of making data visible, accessible, understandable and trusted across the enterprise.
This document provides an overview of physical layer concepts related to data transmission. It discusses how data is represented as signals and transmitted over communication media. It describes analog and digital signals, including their characteristics like frequency, period, phase, and bandwidth. It also covers different types of transmission media, including twisted pair cable, coaxial cable, and optical fiber, which are used to transmit electromagnetic signals representing data. The physical layer is responsible for moving data in the form of signals across the transmission medium.
The document discusses data warehousing and describes its key characteristics and components. It defines a data warehouse as a copy of transaction data structured for querying and reporting to support strategic decision making. It outlines the stages of constructing a data warehouse including extraction, integration, and dimensional analysis to design the data warehouse database.
The key components of a data warehouse are the source data component, data staging component, data storage component, information delivery component, meta-data component, and management and control component. The source data component includes production data, internal data, archived data, and external data. The data staging component involves extracting, transforming through processes like handling synonyms and homonyms, and loading the data. The information delivery component provides access and reports to different user types from novice to senior executives.
Reconciling your Enterprise Data Warehouse to Source SystemsMethod360
Implementing and an enterprise BI system is a significant organization investment. Too many times the expected benefit of that investment isn’t realized due to inconsistent data between the organization’s operational and BI systems.
This webcast will explain several options to enable your organization to leverage its investment by providing options to reconcile the data from source operational systems to BI.
Data warehousing involves collecting data from different sources and organizing it in a way that allows for analysis to make business decisions. It provides a single, complete view of data that end users can easily understand. A data warehouse stores integrated data from multiple sources and provides historical views of data to support analysis. It allows organizations to access critical information to support reporting, queries and decision making. Common applications of data warehousing include banking, healthcare, airlines and telecommunications.
This document discusses data warehousing and decision support systems. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management decision making. It describes key features of a data warehouse including being subject-oriented, integrated, time-variant, and non-volatile. The document also discusses the need for decision support systems in business and different architectural styles for data warehousing like OLTP and OLAP.
Data warehousing and online analytical processing (OLAP) allow organizations to consolidate data from multiple sources and analyze it to answer business questions. A data warehouse stores integrated and subject-oriented data to support organizational decision making. OLAP transforms the data into meaningful information through operations like roll-ups, drills downs, slicing and dicing to enable interactive analysis. Data mining then identifies patterns and relationships in the warehoused data to provide intelligence for businesses.
This document discusses data warehousing, including its definition, importance, components, strategies, ETL processes, and considerations for success and pitfalls. A data warehouse is a collection of integrated, subject-oriented, non-volatile data used for analysis. It allows more effective decision making through consolidated historical data from multiple sources. Key components include summarized and current detailed data, as well as transformation programs. Common strategies are enterprise-wide and data mart approaches. ETL processes extract, transform and load the data. Clean data and proper implementation, training and maintenance are important for success.
(1) The document discusses data warehousing, business intelligence, and their relationship to addressing challenges from multiple data sources.
(2) A layered scalable architecture is presented as a reference architecture for data warehouses to provide reliable, consistent, and understandable data from different source systems.
(3) Big data is also discussed in relation to data warehousing, noting differences in schema and consistency needs between traditional warehouses and big data systems handling high volumes and varieties of data.
This document provides an overview of data warehousing, including its definition, typical architecture, methodologies, advantages, and disadvantages. It defines a data warehouse as a collection of integrated, non-volatile data used to support organizational decision-making. The typical architecture includes layers for operational data, a data access layer, metadata, an informational access layer, and presentation tools. Methodologies include bottom-up design starting with data marts and top-down design using a normalized enterprise data model. Advantages include resolving inconsistencies and retrieving data without impacting operations, while disadvantages include latency and limitations with unstructured data.
The document discusses the need for data warehousing and provides examples of how data warehousing can help companies analyze data from multiple sources to help with decision making. It describes common data warehouse architectures like star schemas and snowflake schemas. It also outlines the process of building a data warehouse, including data selection, preprocessing, transformation, integration and loading. Finally, it discusses some advantages and disadvantages of data warehousing.
RDBMS gave us table schemas. A table schema, which is an essential metadata component, gave us the power to validate data types, and enforce constraints. In the age of varying data and schema-less data stores, how can we enforce these rules and how can we leverage metadata (even in RDBMS) to empower data validity, code checks, and automation.
This is a brief background into Big data (data lake) to put in context the importance of metadata from a governance perspective and more especially in todays heterogeneous big data platforms.
Rohit Sharma presented a seminar on a project that discussed data warehousing, data mining, and how to apply data warehousing concepts to project data. The presentation covered terminology, pulling together and correctly using data from multiple sources, software requirements including PHP and MySQL, and screenshots of the admin panel and user interfaces.
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
This document discusses various business intelligence tools for data analysis including ETL, OLAP, reporting, and metadata tools. It provides evaluation criteria for selecting tools, such as considering budget, requirements, and technical skills. Popular tools are identified for each category, including Informatica, Cognos, and Oracle Warehouse Builder. Implementation requires determining sources, data volume, and transformations for ETL as well as performance needs and customization for OLAP and reporting.
This document discusses data mining in marketing. It begins with defining data mining and explaining why it is important for predicting trends and analyzing customer behavior. The purpose of data mining in marketing is to better understand customers to target campaigns accurately. Key techniques discussed include market basket analysis, social media marketing, and knowledge-based marketing. Common data mining tools for marketing include RapidMiner, WEKA, and Python libraries. In conclusion, data mining can help businesses increase profits, reduce costs, and analyze customer purchase patterns.
This document outlines a course on data warehousing and data mining. It introduces key concepts like relational databases, data warehouses, dimensional modeling, and data mining techniques. It also details the course objectives, schedule, assignments, and policies. The goal is for students to gain experience applying data mining methods and understanding the relationship between data mining and other fields.
The data services marketplace is enabled by a data abstraction layer that supports rapid development of operational applications and single data view portals. In this presentation yo will learn services-based reference architecture, modality, and latency of data access.
- Reference architecture for enterprise data services marketplace
- Modality and latency of data access
- Customer use cases and demo
This presentation is part of the Denodo Educational Seminar , and you can watch the video here goo.gl/vycYmZ.
The document discusses two types of data marts: independent and dependent. Independent data marts focus on a single subject area but are not designed enterprise-wide, examples include manufacturing or finance. They are quicker and cheaper to build but can contain duplicate data and inconsistencies. Dependent data marts get their data from an enterprise data warehouse, offering benefits like improved performance, security, and key performance indicator tracking. The document also outlines the key steps in designing, building, populating, accessing, and managing a data mart project.
This document provides an overview of data warehousing and online analytical processing (OLAP). It defines a data warehouse as a single, consistent store of subject-oriented data obtained from various sources to support end-user business analysis and decision-making. OLAP allows users to easily perform complex multidimensional analyses of data in areas such as comparisons, aggregations, and rankings. The document also discusses key aspects of data warehousing such as extraction, transformation, loading, and management of data from operational systems into the warehouse to support OLAP and decision support.
This document discusses various aspects of data marts, including external data, reference data, performance issues, monitoring requirements, and security. External data is stored in the data warehouse to avoid redundancy. Reference data cannot be modified and is copied from the data warehouse. Performance considerations for data marts are different from OLAP environments, with response times ranging from 1 minute to 24 hours. Monitoring helps track data access, users, usage times, and content growth. Security measures like firewalls, login/logout, and encryption are needed to protect sensitive information in data marts.
The document discusses the Department of Defense's (DoD) policy awareness and data reference model (DRM) for enabling information sharing across agencies. The DRM provides a framework for horizontal and vertical data sharing independently of individual agency systems. It defines common ways to represent, classify and describe data to facilitate integration and access. The model is driven by model-driven architecture principles and aims to abstract data sources and details to promote extensibility. Communities of interest are identified as key to implementing the DoD's net-centric data strategy goals of making data visible, accessible, understandable and trusted across the enterprise.
This document provides an overview of physical layer concepts related to data transmission. It discusses how data is represented as signals and transmitted over communication media. It describes analog and digital signals, including their characteristics like frequency, period, phase, and bandwidth. It also covers different types of transmission media, including twisted pair cable, coaxial cable, and optical fiber, which are used to transmit electromagnetic signals representing data. The physical layer is responsible for moving data in the form of signals across the transmission medium.
The document provides 10 tips for creating awesome presentation slides: (1) A slide should convey one main idea rather than act as a document. (2) Avoid stock templates and make your own slides to show effort. (3) Choose a color scheme and 3-5 colors that match your theme. (4) Select 3 fonts that match your theme and avoid common stock fonts. (5) Use images to convey messages as people remember images better than words. (6) Include icons to represent complex ideas simply. (7) Leave white space to help focus the audience. (8) Visualize statistics rather than just presenting numbers. (9) Include signposts to help navigate slides. (10) Keep animations to a
The document provides information about Kenya Airways (KQ), the national airline of Kenya. It discusses KQ's vision, mission, fleet, destinations, and products. Key information includes that KQ operates to 55 destinations in Africa and has a fleet of Boeing and Embraer aircraft. It also has codeshare partnerships with other airlines through SkyTeam and offers frequent flyer and holiday travel packages.
The document summarizes the features of Lisha switches and wires. It highlights that Lisha switches have an Italian mechanism that ensures quick switching and a long life of over 40 years. Lisha also offers computer jacks, indicator lamps with a 25,000 hour life, and high-quality sockets. The document promotes Lisha wires for having 99.97% pure copper, high conductivity, and a 10-year guarantee. It provides an overview of Lisha's product lines, services, clients, and locations.
Enscape™ product presentation - The Revit plugin for 3D visualizations of architectural projects. Use the one-click solution and virtually walk through your architectural projects. Enscape also offers realtime feedback with Revit – a change in Revit, is a change in Enscape.
Leon International develops accessories that complement various décor styles while meeting installer and user needs. It has over one lac square feet of production space and stringent quality control processes, including mechanical, electrical, and chemical testing. The document highlights several of Leon's modular switch ranges, including premium, deluxe, elegant, grand, and super switches, which are available in various amperages. It also details the company's frame options and the technical features of its switches, such as long lifespans, LED indicators, insulating polyurethane inlays, and fire-resistant materials.
This document introduces the SlideShare Business social media marketing platform. It addresses common challenges like getting more people to view content and engaging on social media with measurable impact. SlideShare Business allows marketers to connect with customers by sharing valuable content in communities, gain people's trust, and promote some content while capturing leads from other content - leveraging more marketing content for real results. It provides targeted advertising and lead capture solutions that feel like content sharing rather than traditional ads.
This document provides details on a product launch presentation for Clinique Mineral Translucent Spray Blusher. It discusses the company profile of Clinique, SWOT analysis, product characteristics, PR consultant objectives and target public, proposed media plan and budget, event program information, and press kit and goody bag contents. The launch event will be held on August 2, 2008 in Jakarta to create awareness of the new blusher product among female consumers aged 22-35.
An inside look at a $1M seed round. Props to Daniel Odio of Appmakr for working with me on this.
Check out the full map at http://brendanbaker.co/anatomy.pdf and join the Quora fun at http://b.qr.ae/m3xRAI.
The new LinkedIn Sales Navigator is a stand-alone solution designed for sales professionals that allows you to focus on the right people and companies, stay informed of key updates, and build trust with prospects and customers.
The document provides five design principles for creating slides that effectively communicate messages to audiences:
1. Focus on the main message you want the audience to remember.
2. Keep designs simple with less text and only 1 main point per slide.
3. Use interesting fonts instead of boring standard ones to engage audiences.
4. Include high quality images that visually represent the message.
5. Choose a color scheme that fits the theme and works cohesively.
The document summarizes the history and growth of SEOmoz, an SEO software company founded in 2001 by Rand Fishkin and his mother Gillian. It details how SEOmoz grew from a small consultancy into a profitable software company with over 10,000 subscribers. The document outlines SEOmoz's plans to raise $20-25 million in funding to expand its product suite, team, and marketing in order to serve a wider audience and become the leading software for organic marketers. The goal is for SEOmoz to become Seattle's next billion dollar company.
IBM InfoSphere Information Server 8.1 is a unified platform for understanding, cleansing, transforming and delivering trustworthy information. It combines the technologies of components like the Information Server Console, Metadata Workbench, Business Glossary, DataStage & QualityStage, Information Analyzer and Information Services Director. The platform provides shared services for administration and reporting. Metadata services allow accessing and integrating data. Key components include the Metadata Server, Metadata Workbench and Business Glossary for managing metadata. DataStage & QualityStage is used for designing jobs to transform and cleanse data, while Information Analyzer helps understand data quality.
Business Intelligence Data Warehouse SystemKiran kumar
This document provides an overview of data warehousing and business intelligence concepts. It discusses:
- What a data warehouse is and its key properties like being integrated, non-volatile, time-variant and subject-oriented.
- Common data warehouse architectures including dimensional modeling, ETL processes, and different layers like the data storage layer and presentation layer.
- How data marts are subsets of the data warehouse that focus on specific business functions or departments.
- Different types of dimensions tables and slowly changing dimensions.
- How business intelligence uses the data warehouse for analysis, querying, reporting and generating insights to help with decision making.
How a Logical Data Fabric Enhances the Customer 360 ViewDenodo
Watch full webinar here: https://bit.ly/3GI802M
Organisations have struggled for years in understanding their customers, this has mainly been due to not having the right data available at the right point in time. In this session we will discuss the role of Data Virtualization in providing customer 360 degree view and look at some of the success stories our customers have told us about.
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
The document describes an architecture for semantically integrating enterprise data lakes. It proposes a knowledge graph that links metadata, data models and key performance indicators to provide a common meaning for data. Raw data is stored in a data lake and ingested from various sources. A metadata layer captures dataset metadata, ontologies and integration rules to link disparate data. An interface allows users to access consolidated views generated by executing queries on Hadoop. The process involves cataloging, discovering, lifting, linking and validating datasets to integrate them based on rules into the knowledge graph.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Robert Isele | eccenca CorporateMemory - Semantically integrated Enterprise D...semanticsconference
The document discusses an architecture for semantically integrating enterprise data lakes. It proposes a corporate memory that centrally manages metadata, ontologies and integration rules. Data is ingested from various sources and stored in a data lake. A knowledge graph is used to semantically link datasets using lifting and linking rules. Users can then generate consolidated views over the integrated data and execute analytics using Apache Spark. The process involves dataset management, discovery, integration and providing domain-specific access to the data.
Pysyvästi laadukasta masterdataa SmartMDM:n avullaBilot
1.9.2016 aamiaistilaisuuden esitys.
Mitäpä jos valjastaisit koko organisaatio masterdatan ylläpitoon? Hallitsisit hajauttamalla? Uudistunut SmartMDM tuo käyttöösi hallinnan, Microsoft SQL Server Master Data Services (MDS) keskityksen.
Lisää tapahtumiamme sivustollamme: http://www.bilot.fi/en/events/
Why an AI-Powered Data Catalog Tool is Critical to Business SuccessInformatica
Imagine a fast, more efficient business thriving on trusted data-driven decisions. An intelligent data catalog can help your organization discover, organize, and inventory all data assets across the org and democratize data with the right balance of governance and flexibility. Informatica's data catalog tools are powered by AI and can automate tedious data management tasks and offer immediate recommendations based on derived business intelligence. We offer data catalog workshops globally. Visit Informatica.com to attend one near you.
Maximizing Business Value: Optimizing Technology InvestmentTeradata
The document summarizes Teradata's data warehousing solutions and capabilities. It highlights Teradata's ability to provide unmatched performance, scalability, and manageability. It also emphasizes Teradata's architectural flexibility to meet various requirements, optimized decisioning through superior in-database analytics, and driving superior operational execution with better insights.
GDPR Noncompliance: Avoid the Risk with Data VirtualizationDenodo
The document discusses how data virtualization can help organizations comply with the General Data Protection Regulation (GDPR). It provides an overview of GDPR requirements and outlines how data virtualization addresses three pillars of compliance: providing a complete view of data subjects, enabling self-service data catalogs, and designing for privacy and responsibility. Specifically, data virtualization can give a single, real-time view of customer data across systems, allow discovery and access to curated data, and ensure consistent security, governance and auditability of personal data.
Achieving a Single View of Business – Critical Data with Master Data ManagementDATAVERSITY
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- Reference data is foundational data used across business transactions, such as client, product, and legal entity data. Consistent reference data is important for accurate reporting and analysis. However, Credit Suisse currently faces challenges of inconsistent views of reference data across applications.
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2. 2Metastudio DRM. General product presentation.
Agenda
About SanmargarTeam
Introduction: Master Data, Reference Data, Meta Data
Metastudio DRM. Use cases,Architecture, Key Features
Useful info
2016-07-01
3. 3
SanmargarTeam
About us
• On the market since 2005
• Business Intelligence specialisation
• Enterprise class customers
• Finance, Energy, Utilities,Telco sectors
COMPETENCIES
• Data integration, processing and migration
• Reference Data Management
• Data Quality Management
• Data Warehouse and reporting solutions
• CRM solutions
• Planning & Budgeting solutions
• Design, Implementation,Audits
PROPRIETARY SOLUTIONS
• Reference Data Management
• Customer 360View
• Data cleansing, standardisation
& de-duplication
Metastudio DRM. General product presentation.2016-07-01
4. 4
SanmargarTeam
Offer summary
Selected Customers
• AGORA SA (*)
• Jastrzebska Spolka Weglowa SA (*)
• BNP Paribas Polska SA (*)
• Energa Obrot SA (*)
• Bank BGZ (*)
• Raiffeisen Bank Polska SA (*)
• Multimedia Polska SA (*)
• Ultimo SA (*)
• PLL LOT SA (*)
• Polska Spolka Gazownictwa
• PGE (*)
• PGNIG
• Cyfrowy Polsat SA (*)
• Poczta Polska SA
• Axcess Financial Poland
(*) Metastudio DRM references
Proprietary solutions
• Reference Data Management (Metastudio DRM)
• Data Quality Management (Sanmargar DQS)
• Customer 360 View (Sanmargar CKK)
Technologies
Competencies
• Business Intelligence
• Data Warehouse and reporting solutions
• Data integration, processing & migration
• Customer Information Integration
• IT processes & strategy analysis
Metastudio DRM. General product presentation.2016-07-01
5. 5Metastudio DRM. General product presentation.
Agenda
About Sanmargar Team
Introduction: Master Data, Reference Data, Meta Data
Metastudio DRM. Use cases,Architecture, Key Features
Useful info
2016-07-01
6. Master Data
A set of trusted data of business objects which are agreed on and shared across
the enterprise (e.g. customer records, products, employees, etc.).
Wikipedia
• Provide description of basic subjects of business processes
• Depend on company’s business model, may include among others:
• Counterparties (customers, suppliers, subcontractors, sales partners)
• Products or services
• Employees
• Organisational Units
• Provide transactional data with suitable context (transactional data should be
linked with master data)
• Valid in periods rather than in particular moments of time (slowly changing in
nature)
• Basis for DataWarehouse / Business Intelligence analytical dimensions
construction
2016-07-01 Metastudio DRM. General product presentation. 6
Master Data, Reference Data, Meta Data
7. Reference Data
In the context of data management, the data describing the permissible values
used in other data sets (i.e. fact, events, transactions or customers tables).
Wikipedia
• Define
• Master Data grouping and hierarchies
• Master andTransactional Data attribute values
(code, description / label)
• Attribute values recoding maps
• Data processing rules and parameters
• Cover both external (standard) and internal
(company specific) data sets
• Valid either permanently or for specific periods of time
• Usually distributed across multiple systems,
especially important for DataWarehouse /
Business Intelligence systems.
Metastudio DRM. General product presentation. 7
Master Data, Reference Data, Meta Data
2016-07-01
8. Meta Data (also Metadata)
Data that provide information about other data – structured
information used to describe information resources or objects,
providing details of their attributes in order to make them easier to
find or manage.
Wikipedia
• Technical Meta Data
• System name, Data type, Data size,Value constraints (PK, FK, Not NULL,
Check), Resources,Technical parameters
• Stored in data processing, data management and data analysis systems
• Business Meta Data
• Business name (Title), Description, Keywords, Categories,Author, Owner,
Purpose, Impact and Lineage info, Period of validity, Modification timestamp
• Stored in data processing, data management and data analysis systems, but in
Business Catalogue solutions as well.
Metastudio DRM. General product presentation. 8
Master Data, Reference Data, Meta Data
2016-07-01
9. Metastudio DRM
Reference Data Management solution, enabling Business Units to
take the responsibility for the process.
Metastudio DRM. General product presentation. 9
Master Data, Reference Data, Meta Data
One place
Various groups
of users
Thousands of
dictionaries…
…from many
sources
2016-07-01
10. 10Metastudio DRM. General product presentation.
Agenda
About Sanmargar Team
Introduction: Master Data, Reference Data, Meta Data
Metastudio DRM. Use cases,Architecture, Key Features
Useful info
2016-07-01
11. • Enterprise reporting and analytical systems
• Business labels for value codes
• Grouping hierarchies
• Analytical dimension hierarchies
• KPI definitions
• Data Warehouses
• Attribute values recoding maps
• Data aggregation and allocation keys
• Data validation rules
• Data processing parameters and metadata
• Subject specific systems
• List of values
• Code descriptions
• Rate tables
• Product and material indexes
Metastudio DRM. General product presentation. 11
Metastudio DRM
Use cases
2016-07-01
12. 12Metastudio DRM. General product presentation.
Metastudio DRM
Architecture
Solution host
Java application server
Meta Data
LDAP
Reference Data
– various databases
JDBC
Administrator
User
User
HTTP/HTTPS
Metastudio DRM
application
container
Linux orWindows
Any operating system supporting Java
may be used.
JDBC data sources
Many database engines supporting
JDBC protocol for reference data.
Web user interface
Full functionality available through
popular web browsers.
LDAP /Windows AD
User authentication with external
LDAP orWindows AD service
2016-07-01
13. • Central repository of the managed reference data, organised into
a hierarchical structure of folders and objects
• Central application server for the solution, limiting technical
requirements on the user side to JavaScript enabled web browser
• Integration with external
authentication services
(LDAP, Windows AD)
• RDM characteristic data
managing mechanism
Metastudio DRM. General product presentation. 13
Metastudio DRM
Key features (1/3)
2016-07-01
14. • Authorization system, which enables controlling of user access level
with respect to specific folders and objects of the reference data
repository
• Automatic registration of author and modification time on a row level
• Edition and presentation of reference data in a flat or hierarchical
(parent-child) structures
• Portlets in the form of
websites embedded in the
application
• Layouts of related dictionaries
and portlets in the form of
master-details views
• Periods of validity for specific
entries of reference data
Metastudio DRM. General product presentation. 14
Metastudio DRM
Key features (2/3)
2016-07-01
15. • Reference Data validation and verification mechanisms
• Audit of actions performed by the users at the level of user
accounts, objects, date and time, as well as incident type
• Export and import of Reference Data to CSV,TXT, XLS files
• Reference Metadata and Data
search engine
• Protection against changes
on a column level
• Column default values
• Value constraints
(text masks, RegExp, LoV,
data ranges)
Metastudio DRM. General product presentation. 15
Metastudio DRM
Key features (3/3)
2016-07-01
16. 16Metastudio DRM. General product presentation.
Agenda
About Sanmargar Team
Introduction: Master Data, Reference Data, Meta Data
Metastudio DRM. Use cases,Architecture, Key Features
Useful info
2016-07-01
17. • Product info: http://sanmargar.com/produkt/metastudio-drm/
• White Paper: https://goo.gl/k2CqPt
• Product presentations: http://www.slideshare.net/sanmargar/
• Demo: http://ms3-demo.sanmargar.com:8080/msdemo
Metastudio DRM. General product presentation. 17
Metastudio DRM
Useful info
2016-07-01
18. SanmargarTeam sp. z o.o.
Lukowska 1 lok 133, 04-133Warszawa
www.sanmargar.com; office@sanmargar.com
Additional info
Metastudio DRM. General product presentation. 18
Michal Chronowski
Proxy of the Management Board
michal.chronowski@sanmargar.pl
+48 661 321 361
2016-07-01