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Data Integration
 
03:43
In this video I will discuss about Data Integration in data mining. Data integration means combining data from multiple sources into a one single store. There are number of issues to consider during data integration like schema integration , object matching and entity identification problem.
Views: 1128 DataMining Tutorials
Data Mining & Business Intelligence | Tutorial #12 | Data Integration Process
 
07:23
Order my books at 👉 http://www.tek97.com/ Lets see what is Data Integration and its issues in various spheres of Data Mining. Watch now ! لنرى ما هو التكامل البيانات وقضاياها في مختلف مجالات البيانات التنقيب. شاهد الآن ! Давайте посмотрим, что такое Интеграция данных и ее проблемы в различных областях интеллектуального анализа данных. Смотри ! Voyons ce qu'est l'intégration de données et ses problèmes dans diverses sphères de l'exploration de données. Regarde maintenant ! Sehen wir uns an, was Data Integration und ihre Probleme in verschiedenen Bereichen des Data Mining sind. Schau jetzt ! Veamos qué es la integración de datos y sus problemas en varias esferas de la minería de datos. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 899 Ranji Raj
What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning & explanation
 
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What is DATA INTEGRATION? What does DATA INTEGRATION mean? DATA INTEGRATION meaning - DATA INTEGRATION definition - DATA INTEGRATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Consider a web application where a user can query a variety of information about cities (such as crime statistics, weather, hotels, demographics, etc.). Traditionally, the information must be stored in a single database with a single schema. But any single enterprise would find information of this breadth somewhat difficult and expensive to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data. A data-integration solution may address this problem by considering these external resources as materialized views over a virtual mediated schema, resulting in "virtual data integration". This means application-developers construct a virtual schema—the mediated schema—to best model the kinds of answers their users want. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution (see figure 2). When an application-user queries the mediated schema, the data-integration solution transforms this query into appropriate queries over the respective data sources. Finally, the virtual database combines the results of these queries into the answer to the user's query. This solution offers the convenience of adding new sources by simply constructing an adapter or an application software blade for them. It contrasts with ETL systems or with a single database solution, which require manual integration of entire new dataset into the system. The virtual ETL solutions leverage virtual mediated schema to implement data harmonization; whereby the data are copied from the designated "master" source to the defined targets, field by field. Advanced data virtualization is also built on the concept of object-oriented modeling in order to construct virtual mediated schema or virtual metadata repository, using hub and spoke architecture. Each data source is disparate and as such is not designed to support reliable joins between data sources. Therefore, data virtualization as well as data federation depends upon accidental data commonality to support combining data and information from disparate data sets. Because of this lack of data value commonality across data sources, the return set may be inaccurate, incomplete, and impossible to validate. One solution is to recast disparate databases to integrate these databases without the need for ETL. The recast databases support commonality constraints where referential integrity may be enforced between databases. The recast databases provide designed data access paths with data value commonality across databases. ....
Views: 3807 The Audiopedia
Data Integration Made Easy: Automate Data Aggregation from Websites and Portals
 
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Partners, suppliers, and portals are often managed manually, which is complex and labor-intensive. Quickly and easily automate manual data aggregation and business processes, enhance data-driven decisions, and free staff to focus on higher-value tasks, without relying completely on IT.
Views: 530 Kofax, Inc.
Data Preprocessing in Hindi |DMBI | Data Warehouse and Data mining Tutorials
 
05:28
Sample Notes : https://drive.google.com/file/d/19xmu... for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho : https://goo.gl/85HQGm for full notes please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made notes of data warehouse and data mining its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 8798 Last moment tuitions
KDD ( knowledge data discovery )  in data mining in hindi
 
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Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 50891 Last moment tuitions
DM7 Data Integration تكامل البيانات
 
02:25
أ.محمود رفيق الفرا مختصر مساق التنقيب عن البيانات Data Mining
Views: 1468 MahmoudRFarra
Data Integration UAS --- Tutorial Pentaho & Data Mining
 
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I Kadek Putrawan-----13101167 Agus Putra Utama Yasa-----13101292 Ekky Agustana-----13101325
Views: 305 deks putrawan
An architecture for federated data discovery & lineage with Apache Atlas
 
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Comcast's Streaming Data platform comprises a variety of ingest, transformation, and storage services in the public cloud. Peer-reviewed Apache Avro schemas support end-to-end data governance. We have previously reported (DataWorks Summit 2017) on how we extended Atlas with custom entity and process types for discovery and lineage in the AWS public cloud. Custom lambda functions notify Atlas of creation of new entities and new lineage links via asynchronous kafka messaging. Recently we were presented the challenge of providing integrated data discovery and lineage across our public cloud datasources and on-prem datasources, both Hadoop-based and traditional data warehouses and RDBMSs. Can Apache Atlas meet this challenge? A resounding yes! This talk will present our federated architecture, with Atlas providing SQL-like, free-text, and graph search across select metadata from all on-prem and public cloud data sources in our purview. Lightweight, custom connectors/bridges identify metadata/lineage changes in underlying sources and publish them to Atlas via the asynchronous API. A portal layer provides Atlas query access and a federation of UIs. Once data of interest is identified via Atlas queries, interfaces specific to underlying sources may be used for special-purpose metadata mining. While metadata repositories for data discovery and lineage abound, none of them have built-in connectors and listeners for the entire complement of data sources that Comcast and many other large enterprises use to support their business needs. In-house-built solutions typically underestimate the cost of development and maintenance and often suffer from architecture-by-accretion. Atlas' commitment to extensibility, built-in provision of typed, free-text, and graph search, and REST and asynchronous APIs, position it uniquely in the build-vs-buy sweet spot. Speaker BARBARA ECKMAN Principal Data Architect Comcast
Views: 270 DataWorks Summit
Data Cleaning Process Steps / Phases [Data Mining] Easiest Explanation Ever (Hindi)
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Data Reduction (As breif as Possible)
 
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This video is about brief knowledge about data reduction. how data reduction is used to compressed data.
Views: 94 Tech Insight
What is Data Warehousing?
 
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Data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting. Data warehousing involves data cleaning, data integration, and data consolidations. Data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW). Data warehouse is a system used for reporting and data analysis. Data warehouse is considered a core component of business intelligence. Data warehouse are central repositories of integrated data from one or more disparate sources. Data warehouse store current and historical data in one single place. Data warehouse are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (ERP). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the Data warehouse for reporting. Staging database stores raw data extracted from source data systems. Data warehouse stores transformed data. The combination of facts and dimensions is sometimes called a star schema. The main source of the data is cleansed, transformed, cataloged and made available for use by managers and other business professionals.
Views: 722 BI Guru
Data Mining & Business Intelligence | Tutorial #3 | Issues in Data Mining
 
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This video addresses the issues which are there involved in Data Mining system. Watch now !
Views: 859 Ranji Raj
Data Warehouse in Telugu
 
17:45
ETL and SSIS : https://youtu.be/kWxv9E7g-JM Business Intelligence is a technology based on customer and profit-oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision-making capabilities at no time. Business Intelligence Models are based on multidimensional analysis and key performance indicators (KPI) of an enterprise "A data warehouse is a subject oriented, integrated, time variant, a nonvolatile collection of data in support of management's decision-making process". In addition to a relational/multidimensional database, a data warehouse environment often consists of an ETL solution, an OLAP engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users. Data Mart - Datamart is a subset of data warehouse and it supports a particular region, business unit or business function. ETL (Extract, Transform and Load) is a process in data warehousing responsible for pulling data out of the source systems and placing it into a data warehouse. ETL and SSIS : https://youtu.be/kWxv9E7g-JM The Online Analytical Processing is designed to answer multi-dimensional queries, whereas the Online Transaction Processing is designed to facilitate and manage the usual business applications. While OLAP is customer-oriented, OLTP is market-oriented. Both OLTP and OLAP are two of the common systems for the management of data. The OLTP is a category of systems that manage transaction processing. OLAP is a compilation of ways to query multi-dimensional databases Tools for Data warehouse: Amazon Redshift Oracle 12c MSBI Informatica Data Validation. QuerySurge. ICEDQ. Datagaps ETL Validator. QualiDI. Talend Open Studio for Data Integration. Codoid's ETL Testing Services. Data Centric Testing.
Views: 1958 Learners Page
DWH: Snowflake Vs Star Schema
 
03:50
In this tutorial, you 'll learn what is the difference between snowflake and star schema? 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. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting. The typical extract-transform-load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
Views: 12693 radhikaravikumar
what is  Olap operation in hindi
 
08:07
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 130791 Last moment tuitions
ETL ( Extract Transform Load )   process fully explained  in hindi | Datawarehouse
 
08:42
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 45109 Last moment tuitions
Data Mining & Business Intelligence | Tutorial #16 | Data Reduction - Attribute Subset Selection
 
05:39
Order my books at 👉 http://www.tek97.com/ Confused about Attribute Subset Selection in Data Mining well this video can help you . Watch now ! ارتباك حول اختيار مجموعة فرعية سمة في استخراج البيانات بشكل جيد هذا الفيديو يمكن أن تساعدك. شاهد الآن ! Confundido acerca de la selección de subconjuntos de atributos en Data Mining, este video puede ayudarlo. Ver ahora ! Смутно о выборе подмножества атрибутов в Data Mining, это видео может вам помочь. Смотри ! Verwirrt über Attribut-Subset-Auswahl in Data Mining gut dieses Video kann Ihnen helfen. Schau jetzt ! Confus à propos de la sélection de sous-ensemble d'attributs dans Data Mining, cette vidéo peut vous aider. Regarde maintenant ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 698 Ranji Raj
Join - Operator - RapidMiner - Data Mining
 
11:41
The Join operator. The Join operator joins two example sets together in a variety of ways. Its parameters allow example sets to be enriched with new attributes using data from other sources, filtered to include only examples of interest and combined with other example sets to form subsets or supersets. Topics covered Inner join Left join Right join Outer join The Id attribute parameter Double attributes
Views: 158 Markus Hofmann
Data warehouse Features Lecture in Hindi - DWDM Lectures in Hindi, English
 
16:30
Data warehouse Features Lecture in Hindi - DWDM Lectures in Hindi, English Data warehouse Features – Subject Oriented, Integrated, Time Variant, Non-Volatile Data, Data Granularity Data Warehouse and Data Mining Lectures in Hindi
DWH: What is fact/dimension/snowflake/star schema?
 
06:22
In this tutorial, you 'll learn what is fact and dimension table in simple terms along with snowflake and star schema 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. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting. The typical extract-transform-load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.[4] This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support.[5] However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.
Views: 36390 radhikaravikumar
Data Mining & Business Intelligence | Tutorial #15 | Data Reduction - Data Cube Aggregation
 
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Order my books at 👉 http://www.tek97.com/ See the detailed explanation about Data Cube Aggregation in Data Mining as part of Data Reduction step. Watch now ! انظر الشرح التفصيلي حول تجميع بيانات المكعب في Data Mining كجزء من خطوة تقليل البيانات. شاهد الآن ! Weitere Informationen zur Datenwürfelaggregation im Data Mining finden Sie im Abschnitt Datenreduktion. Schau jetzt ! Consultez l'explication détaillée sur l'agrégation de cubes de données dans l'exploration de données dans le cadre de l'étape de réduction des données. Regarde maintenant ! Подробное описание агрегации Data Cube в Data Mining см. В разделе шага Data Reduction. Смотри ! Consulte la explicación detallada sobre la agregación de cubo de datos en la minería de datos como parte del paso de reducción de datos. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 1213 Ranji Raj
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Warehousing | Edureka
 
01:38:50
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial: 1. What Is The Need For BI? 2. What Is Data Warehousing? 3. Key Terminologies Related To DWH Architecture: a. OLTP Vs OLAP b. ETL c. Data Mart d. Metadata 4. DWH Architecture 5. Demo: Creating A DWH - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Intelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - Please write back to us at [email protected] or call us at +91 90660 20866 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 167101 edureka!
Data Mining & Business Intelligence | Tutorial #4 | Forms Of Data Preprocessing
 
09:49
Order my books at 👉 http://www.tek97.com/ Its important to preprocess the data before processing. Have a look at different forms of data preprocessing in data mining. Watch now ! Il est important de prétraiter les données avant le traitement. Jetez un oeil à différentes formes de prétraitement des données dans l'exploration de données. Regarde maintenant ! Es ist wichtig, die Daten vor der Verarbeitung vorzuverarbeiten. Sehen Sie sich verschiedene Formen der Datenvorverarbeitung im Data Mining an. Schau jetzt ! Es importante preprocesar los datos antes del procesamiento. Eche un vistazo a las diferentes formas de preprocesamiento de datos en la minería de datos. Ver ahora ! من المهم أن preprocess البيانات قبل المعالجة. إلقاء نظرة على أشكال مختلفة من معالجة البيانات في تعدين البيانات. شاهد الآن ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 951 Ranji Raj
Data Mining & Business Intelligence | Tutorial #17 | Data Reduction - Dimensionality Reduction
 
07:13
Order my books at 👉 http://www.tek97.com/ This video explains what is Dimensionality Reduction in Data Mining as a Data Reduction step. Watch Now ! يشرح هذا الفيديو ما هو Dimensionality Reduction في Data Mining كخطوة لخفض البيانات. شاهد الآن ! Este video explica qué es la reducción de la dimensionalidad en la minería de datos como un paso de reducción de datos. Ver ahora ! В этом видео объясняется, что такое уменьшение размера в интеллектуальном анализе данных как шаг сокращения данных. Смотри ! Cette vidéo explique ce qu'est la réduction de la dimension dans l'exploration de données en tant qu'étape de réduction des données. Regarde maintenant ! In diesem Video wird erklärt, was Dimensionality Reduction in Data Mining als Datenreduktionsschritt bedeutet. Schau jetzt ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 607 Ranji Raj
Dealing With Noisy Data : Binning Technique [Data Mining] (HINDI)
 
04:08
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Using Pentaho Schema Workbench   Part 2  Using the Schema Editor
 
00:54
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Data Integration and Data Exchange
 
53:29
Google TechTalks March 24, 2006 Alan Nash ABSTRACT I will discuss two fundamental problems in information integration: (1) how to answer a query over a public interface which combines data from several sources and (2) how to create a single database conforming to the public interface which combines data from several sources. I consider the case where the sources are relational databases, where the public interface is a public schema (a specification of the format of a database), and where the sources are related to the public schema by a mapping that is specified by constraints.
Views: 2654 Google
Data Mining   KDD Process
 
03:08
KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
Data Warehouse - Integration Service
 
10:45
What is Microsoft SSIS (SQL Server Integration Services Implementing Data Warehouses with Integration Services SSIS Novices' Guide to Data Warehouses: Flattening While ... Data Integration Services in Data Warehouse | Wipro High impact Data Warehousing with SQL Server Integration SQL Server Integration Services - Wikipedia, the free ... Essbase Integration Services | Business Intelligence | Oracle SQL Server Integration Services Design Patterns Popular Data warehouse & SQL Server Integration Services ... The Microsoft Data Warehouse Toolkit: With SQL Server 2008 ... Data Warehouses & Integration Services - BISS-003 Advanced Integration Services | Pluralsight Data Integration for Real-Time Data Warehousing and Data ... Integrated Data Warehousing Solution from Teradata Diving into Azure SQL Data Warehouse LightStream » Data Integration Services PDF]SAS Data Integration Service Microsoft SQL Server 2012 Integration Services - Google Books R Integration Services - PARC Systems Inc Using Integration Services 2014 (SSIS) to Load a Data ... Integration Services | FICO TONBELLER Data Integration Architecture: What It Does, Where It's Going ... Data Integration | BI project deployments, Data Warehouse ... Automation of Data Mining Using Integration Services - MSDN
Views: 701 Ahmed Elsayed
Data Mining & Business Intelligence | Tutorial #11 | Smoothing by Binning (Solved Problem)
 
17:35
Order my books at 👉 http://www.tek97.com/ Worried about how to smooth data by binning with means, medians and mode well this video will solve your queries. Watch now ! قلق حول كيفية بسلاسة البيانات عن طريق binning بالوسائل ، والوسائط والوضع بشكل جيد هذا الفيديو سوف يحل استفساراتك. شاهد الآن ! Besorgt darüber, wie Daten durch Binning mit Mitteln, Medianen und Modus geglättet werden können, wird dieses Video Ihre Fragen lösen. Schau jetzt ! Preocupado acerca de cómo suavizar los datos al agrupar los medios, las medianas y el modo, este video resolverá sus dudas. Ver ahora ! Inquiet sur la façon de lisser les données en binning avec les moyens, les médianes et le mode bien cette vidéo va résoudre vos questions. Regarde maintenant ! Беспокоитесь о том, как сгладить данные путем биннинга средствами, медианами и режимами, это видео решает ваши запросы. Смотри ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 2190 Ranji Raj
Data Warehouse Concepts | Data Warehouse Tutorial | Data Warehouse Architecture | Edureka
 
52:30
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** This tutorial on data warehouse concepts will tell you everything you need to know in performing data warehousing and business intelligence. The various data warehouse concepts explained in this video are: 1. What Is Data Warehousing? 2. Data Warehousing Concepts: 3. OLAP (On-Line Analytical Processing) 4. Types Of OLAP Cubes 5. Dimensions, Facts & Measures 6. Data Warehouse Schema - - - - - - - - - - - - - - Check our complete Data Warehousing & Business Inelligence playlist here: https://goo.gl/DZEuZt. #DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining #DataWarehouseConcepts Subscribe to our channel to get video updates. Hit the subscribe button above. - - - - - - - - - - - - - - How it Works? 1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course: Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like: 1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations 2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards 3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Data warehousing enthusiasts 2. Analytics Managers 3. Data Modelers 4. ETL Developers and BI Developers - - - - - - - - - - - - - - Why learn Data Warehousing and Business Intelligence? All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success. With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin. - - - - - - - - - - - - - - Please write back to us at [email protected] or call us at +91 90660 20866 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - Customer Review: Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
Views: 39193 edureka!
Star Schema & Snowflake Schema
 
12:49
This video talks about Star Schema: Every dimension table is related directly to the fact table. Snowflake Schema: Some dimension tables are related indirectly to the fact table. Part of DWH concepts and step by step DWH training
Views: 1996 Training2SQL MSBI
Video Tutorial - Star Schema vs Starflake / Using Aggregate Tables
 
05:11
In this brief video tutorial, Doug Terbush from our customer education team describes the benefits of leveraging aggregate tables with MicroStrategy.
KDD(knowledge discovery in database) in hindi
 
07:32
KDD in hindi Data cleaning Data integration Data transformation Data mining Pattern evaluation Knowledge representation
Views: 85 Nk lectures
Intro to Data Reduction
 
09:57
Understanding Data Reduction, Descriptive Statistics, Types of Variables
Views: 4044 Patricia Jenkinson
Data Mining & Business Intelligence | Tutorial #20 | Data Reduction - Concept Hierarchy Generation
 
05:09
Order my books at 👉 http://www.tek97.com/ Confused about what is Concept Hierarchy Generation in Data Mining in the context of Data Mining well this video is for you. Watch Now! ارتباك حول ما هو الجيل الهرمي مفهوم في "التعدين البيانات" في سياق "التنقيب عن البيانات" جيدا هذا الفيديو لك. شاهد الآن! Confus au sujet de ce qu'est la génération de hiérarchie de concept dans l'exploration de données dans le contexte de l'exploration de données bien cette vidéo est pour vous. Regarde maintenant! Verwirrt darüber, was ist Konzept-Hierarchie-Generierung in Data Mining im Zusammenhang mit Data Mining gut dieses Video ist für Sie. Schau jetzt! Confundido sobre qué es la Generación de Jerarquía Conceptual en Minería de Datos en el contexto de la Minería de Datos, este video es para usted. ¡Ver ahora! Смутно о том, что представляет собой генерация концепции иерархии в интеллектуальном анализе данных в контексте Data Mining, это видео для вас. Смотри! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 579 Ranji Raj
حصريا - تطبيق كامل-  لـ Data Warehouse - مستودعات البيانات - من الألف إلى الياء-
 
42:42
SQl Server 2012 Data Warehouse - Star schema – Dimensions - Fact Table ETL – Integration Service – SSIS – Data Flow Task - Slowly Changing Dimension – Integration Service Catalog – SSISDB Olap – Cube – Service analysis – SSAS Business intelligence – BI – SSRS – Report Builder – matrix - Pivot الدرس الثاني شرح : ماهي Data Warehouse - YouTube شرح Building Data Warehouse بالعربي - YouTube مستودع البيانات - ويكيبيديا، الموسوعة الحرة مستودعات البيانات Data warehousesو استخراج البيانات Data Mining ... مستودعات البيانات data warehouse - الرئيسية مستودعات البيانات Data Warehousing | php Tricks مستودعات البيانات / أحمد عبد الله - Cybrarians Journal data warehouse, data mining, OLAP, OLTP - قواعد بيانات Microsoft .. الملف الأول سؤال وإجابة: ما هو الفرق بين قواعد البيانات ومخازن البيانات ... Search Results سؤال وإجابة: ما هو الفرق بين قواعد البيانات ومخازن البيانات ... اريد معرفه معانى بعض المصطلحات الهامة في الداتابيز - منتدى ... Data Warehousing Concepts - Oracle Help Center استخدام تقنيات مستودعات البيانات في دعم القرارات ... ما هي أنواع الـindexes في data warehouse مع شرح بسيط لكل نوع مستودعات البيانات pdf داتا ويرهاوس
Views: 6334 Ahmed Elsayed
Using Pentaho Schema Workbench   Part 5  Associating Cubes and Dimensions
 
01:56
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Building Advanced Analytics Applications with R and Python Integration
 
46:57
At Tableau we help people see and understand data. Seven words that drive everything we do. And they’ve never been more relevant. Tableau is all about making your analytics faster, smarter, and more powerful, so that everyone can get the answers they need. Helping people gain insight into their data to solve unexpected problems is what drives us. Tableau is a visual analytics and reporting solution that connects directly to R, Python, and more. It’s designed for you, the domain expert who understands the data. Its drag-and-drop interface allows you effortlessly connect to libraries and packages, import saved models, or write new ones directly into calculations, visualizing them in seconds. In this webinar, we will explore how various analytics partners are leveraged in Tableau, and how to take advantage of these integrations to move your analysis to the next level. Whether you work with R, Python, or other statistical or data mining environments, Tableau allows you to take advantage of your existing investments and knowledge to compose impactful data stories. Read more at http://forums.bsdinsight.com/forums/tableau.95/
Views: 4335 Tableau
JSON Integration with KNIME
 
10:12
There is a full new category in KNIME 2.11 about JSON Processing. In particular, there are nodes to read and extract content from JSON structures, validate JSON schema, ascertain differences between 2 JSON objects, transform and convert JSON structures to other structures like XML. This video is part of the recording of the "What is new in KNIME 2.11" webinar held on Dec 11 2014 and available on Youtube at: http://youtu.be/9RkRHI32Dy8 For more infos about updates in KNIME 2.11 check http://tech.knime.org/whats-new-in-knime-211
Views: 2326 KNIMETV
How to Create Report with Pentaho Report Designer
 
09:51
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Data Management_ Oncor Meter Data Management - Outage Management System Integration
 
18:15
Help with inventory management? for my small business, need help updating data? sheet? looking for software!? How to change file system from ntfs to fat32 while keeping the data? on the drive? Which is better a mba in project management? or go for a (mpm) masters in project management? degree? Is it worth the time and energy to pursue a master is degree in sports management? Would a certificate in business management? help me get into grad school? does it add any value? Will increasing the logical drive size from adding an additional drive into an array cause me to lose my data? Whats your idea is on a time management? program? i want to hear your thoughts? What is the best method of inventory management? in auto component (gears, axles & shaft) manufacturing company? Recover data? off of a corrupted hdd? How do i ensure that the result of an if statement in excel doesnt exceed a certain number? Which is good subject network management? or data? mining? Report on data? management? Why am i having re-occuring nightmares about gr.12 math? Data? flow diagram of hospital management? system according to system development life cycle?
Why do you use star schema for data warehouse design?
 
02:16
itp_set3_03 Short video from IT Performs - Business Intelligence SpecialistsIT Performs (ITP), recently awarded Most Satisfied Customers & Outstanding Sales Performance by SAP BusinessObjects, has delivered business intelligence, data warehousing and information management solutions since 1996. As an award winning Gold Partner of SAP BusinessObjects and a Migration Specialist, ITP has a comprehensive track record of delivering high quality solutions for data warehousing, sales forecasting, financial reporting, budgeting & planning, dashboarding, performance management and data integration / migration initiatives across a number of industry sectors. We work with all the major databases and ERP systems to turn your data into trusted information and use a pragmatic and real world approach to deploy cost effective solutions, ensuring project success. If you would like to discuss any of the issues raised in this video or take part in a business intelligence workshop please use the following contact information: Call 0845 124 9495 or email [email protected]
Using Pentaho Schema Workbench   Part 3  Adding a Cube and Measures
 
02:15
Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.
Creating Cubes in Pentaho Schema Workbench
 
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Pentaho is a powerful Business Intelligence Suite offering many features: reporting, OLAP pivot tables, dashboarding and more. Report Designer: An advanced report creation tool. If you want to build a complex data-driven report, this is the right tool to use. Report Designer offers far more flexibility and functionality than the ad hoc reporting capabilities of the Pentaho User Console. • Design Studio: An Eclipse-based tool that enables you to hand-edit a report or analysis view xaction file. Generally, people use Design Studio to add modifications to an existing report that cannot be added with Report Designer. • Aggregation Designer: A graphical tool that helps improve Mondrian cube efficiency. • Metadata Editor: Enables you to add a custom metadata layer to an existing data source. Usually you would do this for a data source that you intend to use for reporting; it's not required, but it makes it easier for business users to parse the database when building a query. • Pentaho Data Integration: The Kettle extract, transform, and load (ETL) tool, which enables you to access and prepare data sources for analysis, data mining, or reporting. This is generally where you will start if you want to prepare data for analysis. • Schema Workbench: A graphical tool that helps you create ROLAP schemas for analysis. This is a required step in preparing data for analysis.