DMDW 5. Student Presentation - Pentaho Data Integration (Kettle)
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This document discusses data warehousing and data mining. It describes a typical data warehouse architecture including data consolidation, security, modeling, and querying requirements. It outlines the extract, transform, load (ETL) process for moving raw data into the warehouse. Specific tools used are MySQL, MySQL Workbench, and Pentaho Data Integration. The steps taken include verifying source Excel data, preparing the database structure in MySQL, and performing the extract, transform, and load of data using Pentaho.
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systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by
subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure
from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has
not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The
current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed
because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation
should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a
technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries
out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic
Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test
scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the
specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data Access
This document proposes enhancing data staging as a mechanism for fast data access. It discusses challenges with current data warehousing systems, including performance issues as data volumes increase. It then introduces the concept of data staging and proposes a new approach called Deterministic Prioritization to improve data staging. This approach would prioritize and filter data in the staging area based on confidence levels and distinctiveness, to reduce processing loads. It would create clustered indexes on distinct data columns to alter query execution plans and improve performance. The goal is to develop a new cross-platform data staging framework with forecasting and prioritization mechanisms to optimize resource usage and data transfer speeds.
Dwdm unit 1-2016-Data ingarehousing
This document outlines the objectives and units of study for a course on data warehousing and mining. The 5 units cover: 1) data warehousing components and architecture; 2) business analysis tools; 3) data mining tasks and techniques; 4) association rule mining and classification; and 5) clustering applications and trends in data mining. Key topics include extracting, transforming, and loading data into a data warehouse; using metadata and query/reporting tools; building dependent data marts; and applying data mining techniques like classification, clustering, and association rule mining. The course aims to introduce these concepts and their real-world implications.
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptx
The document discusses data warehousing and data warehouse architectures. It defines a data warehouse as a system that aggregates data from different sources into a consistent data store to support analysis and machine learning on huge volumes of historical data. It describes three common types of data warehouses and characteristics like being subject-oriented, integrated, and time-variant. It then outlines common data warehouse architectures including single tier, two tier, and three tier architectures and discusses components like the source layer, data staging, data warehouse layer, and analysis layer. Finally, it discusses properties of data warehouse architectures like separation of analytical and transactional processing and scalability.
Data Warehouse By Piyush
Data warehousing is a repository of an organization's electronically stored data designed for reporting and analysis. A data warehouse uses an extract, transform, load process to integrate data from multiple sources and organize it into a dimensional model to support business intelligence needs. It provides consistent, integrated views of data across an organization to help analyze patterns and trends.
Datawarehousing
The document discusses key concepts related to data warehousing including:
1) What data warehousing is, its main components, and differences from OLTP systems.
2) The typical architecture of a data warehouse including operational data sources, storage, and end-user access tools.
3) Important considerations like data flows, integration, management of metadata, and tools/technologies used.
4) Additional topics such as benefits, challenges, administration, and data marts.
DBT PU BI Lab Manual for ETL Exercise.pdf
The document provides instructions for building a data warehouse using Microsoft SQL Server and performing extract, transform, and load (ETL) operations on data from various sources about personal loans. It describes creating ETL workflows to extract data from files on loans, customers, employees, and payments then loading it into tables. Dimensional models and reports are then developed using the data to analyze repayment trends by company, region, gender and other attributes.
Data warehouse concepts
A data warehouse is a central repository of historical data from an organization's various sources designed for analysis and reporting. It contains integrated data from multiple systems optimized for querying and analysis rather than transactions. Data is extracted, cleaned, and loaded from operational sources into the data warehouse periodically. The data warehouse uses a dimensional model to organize data into facts and dimensions for intuitive analysis and is optimized for reporting rather than transaction processing like operational databases. Data warehousing emerged to meet the growing demand for analysis that operational systems could not support due to impacts on performance and limitations in reporting capabilities.
What are the benefits of learning ETL Development and where to start learning...
Learning ETL (Extract, Transform, Load) development can unlock numerous career opportunities in data management and analytics. By mastering ETL skills, individuals can integrate data from various sources, ensuring its quality and efficiency for decision-making processes. To embark on this journey, understanding ETL concepts, acquiring SQL proficiency, familiarizing oneself with ETL tools like Informatica or Talend, practicing with real-world datasets, and staying updated with industry trends are essential steps. Institutes offering specialized ETL Development training in Pune provide hands-on experience and expert guidance, preparing individuals for success in the dynamic field of data engineering. Benefits of learning ETL development include lucrative career prospects, data integration skills, data quality assurance, efficiency through automation, and the ability to derive actionable insights from raw data. To start learning ETL development, one should understand the core concepts, learn SQL, explore ETL tools, practice with real-world data, stay updated with industry trends, and build a portfolio of projects to showcase expertise. Optimizing ETL workflows for performance and scalability involves strategies such as data profiling, incremental loading, parallel processing, partitioning, indexing, data compression, memory management, resource monitoring, and scalable architecture design. Handling data consistency issues in ETL development requires strategies like data profiling and cleansing, validation rules, transformation and mapping, master data management, change data capture, error handling and logging, transactional processing, data quality monitoring, documentation, and metadata management. Data lineage tracing is crucial in ETL development for understanding data flow, identifying dependencies, ensuring data quality, compliance, and auditing. It is implemented through metadata management, data lineage tools, annotation and documentation, logging and monitoring, and integration with ETL tools. By mastering ETL development and implementing data lineage tracing effectively, individuals and organizations can enhance data management, analytics, and decision-making processes.
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SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCE
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Enhancing Data Staging as a Mechanism for Fast Data Access
Enhancing Data Staging as a Mechanism for Fast Data Access
Enhancing Data Staging as a Mechanism for Fast Data Access
Enhancing Data Staging as a Mechanism for Fast Data Access
Enhancing Data Staging as a Mechanism for Fast Data Access
Enhancing Data Staging as a Mechanism for Fast Data Access
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Enhancing Data Staging as a Mechanism for Fast Data Access
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptx
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptx
What are the benefits of learning ETL Development and where to start learning...
What are the benefits of learning ETL Development and where to start learning...
More from Johannes Hoppe
NoSQL - Hands on
This document provides an overview of MongoDB and examples of CRUD operations. It discusses setting up a MongoDB directory and starting the server and shell. It then demonstrates creating, reading, updating, and deleting documents. Examples show inserting single documents and bulk inserting 1000 documents with random scores. Queries are demonstrated using find() with filters. Updates are shown using update() with $set and $push operators. Hands-on exercises are provided to practice CRUD operations on sample datasets.
Einführung in Angular 2
Letztes Wochenende waren Danny Koppenhagen, Ferdinand Malcher, Gregor Woiwode und ich Teilnehmer des Developer Open Space 2015.
Per Live-Coding mit Pair-Programming haben Gregor und ich einen Schnellstart in das neue Framework gegeben: TypeScript, Komponenten, Dekoratoren, Bindings, Events, Formularverarbeitung und Datenfluss in einer NG2-App. In 45 Minuten stand die erste Single-Page-App. Im Anschluss an die tolle Session möchte ich hier noch einmal alle Infos zusammen tragen.
Die didaktisch aufgebauten Folien findest du hier auf Slideshare, Links, Downloads und das Video findest du auf http://blog.johanneshoppe.de/2015/10/angular2-einfuehrung-schnellstart/.
MDC kompakt 2014: Hybride Apps mit Cordova, AngularJS und Ionic
Sehen Sie sich das Video an: http://haushoppe-its.de/videos/mdc-kompakt-2014-hybride-apps-mit-cordova-angularjs-und-ionic/
Dieser Vortrag wurde am 18.11.2014 bei der "MDC - Mobile Developer Conference kompakt 2014" aufgenommen.
Dank Apache Cordova ist es möglich, Ihr bestehendes Wissen zu HTML5, JavaScript und CSS3 auf mobile Apps anzuwenden. Nutzen Sie AngularJS von Google, um Techniken wie MVC und Data Binding in den Browser zu bringen. Erfahren Sie in diesem Vortrag, wie Sie mit dem jüngsten Spross, dem Ionic Framework, ansprechende Oberflächen für iOS, Android und Windows Phone gestalten können. Statt PowerPoint erwartet Sie viel Live Coding. Johannes Hoppe wird zusammen mit Ihnen eine erste hybride Anwendung entwickeln.
2015 02-09 - NoSQL Vorlesung Mosbach
The document discusses trends in data, networking, and individualization. It then covers topics around scaling databases including scale-up (vertical scaling) and scale-out (horizontal scaling). It notes that NoSQL databases do not use a relational data model or SQL, instead supporting distributed and horizontal scalability with loose or no schema restrictions and different consistency models. Requirements for distributed systems like consistency, availability, and partition tolerance are discussed in relation to Brewer's CAP theorem from 2000.
2013-06-25 - HTML5 & JavaScript Security
Sie kennen die bekannten Angriffsvektoren wie SQL-Injections oder XSS. Ihre Anwendung ist sicher. Ist Sie das wirklich? Auch wenn Sie in Ihrer Webanwendung kein HTML5 einsetzen, die Browser sind bereit! Kennen Sie alle neuen Markups? Haben Sie bereits die Potentiale von Cross Origin Requests, WebSockets oder Local Storage auf dem Radar? Lernen Sie neue Gefahrenpotentiale kennen, die durch die Unterstützung von HTML5 und dessen APIs entstanden sind. - See more at: http://www.developer-week.de/Programm/Veranstaltung/(event)/11133#sthash.ZRPweawl.dpuf
2013-06-24 - Software Craftsmanship with JavaScript
This document discusses principles of software craftsmanship including:
- Common JavaScript pitfalls like implied globals, trailing commas, and return undefined
- Best practices like avoiding globals, using module patterns for organization, and test-driven development
- Tools for JavaScript development like Jasmine for testing, RequireJS for module loading, and Visual Studio for debugging
- The process of test-driven development including writing tests, making them fail, making them pass, refactoring code, and repeating
2013 05-03 - HTML5 & JavaScript Security
INTERAKTIVE SLIDES:
http://johanneshoppe.github.com/HTML5Security/
Sie kennen die bekannten Angriffsvektoren wie SQL-Injections oder XSS. Ihre Anwendung ist sicher. Ist Sie das wirklich? Auch wenn Sie in Ihrer Webanwendung kein HTML5 einsetzen, die Browser sind bereit! Kennen Sie alle neuen Markups? Haben Sie bereits die Potentiale von Cross Origin Requests, WebSockets oder Local Storage auf dem Radar? Lernen Sie neue Gefahrenpotentiale kennen, die durch die Unterstützung von HTML5 und dessen APIs entstanden sind.
2013-03-23 - NoSQL Spartakiade
This document discusses different data modeling techniques for MongoDB including embedding documents, linking documents by ID, and handling many-to-many relationships. It provides code examples of saving documents, querying, and updating in MongoDB. It also briefly covers software testing strategies for MongoDB applications.
2013 02-26 - Software Tests with Mongo db
Quality matters. That’s why we write software tests. They give us confidence to release complex business applications. In a schema-free world they are going to be our escape rope! This talk introduces some Unit Testing principles and separates them from Integration Tests. We will see how other databases solve common problems and how we can archive the same with MongoDB. Of course, you can expect some Test Driven Development (TDD).
2012-09-18 - HTML5 & WebGL
Mit HTML5 & JavaScript lassen sich Browser-Games kreieren, die vormals nur mit proprietären Plugins möglich waren. Dazu zählen hardwarebeschleunigte 3D-Grafiken, welche über den WebGL-Standard unterstützt werden. Johannes Hoppe stellt die Tools und Technologien vor, die für die Erstellung von „SolarTournament" verwendet wurden. Allen Teilnehmern wird der Source-Code dieses Multiplayer 3D Action-Shooters sowie eine Auswahl der Arbeitsdateien zur Verfügung gestellt.
2012-09-17 - WDC12: Node.js & MongoDB
Zwei neue Technologien für die Cloud sind das JavaScript-Framework Node.js und die NoSQL-Datenbank MongoDB. Johannes Hoppe gibt einen Schnellstart in die beiden Open-Source-Systeme.
2012-08-29 - NoSQL Bootcamp (Redis, RavenDB & MongoDB für .NET Entwickler)
Folienset von meinem eintägigen Workshop bei der DNUG Braunschweig.
Blogeintrag: http://blog.johanneshoppe.de/2012/08/nosql-bootcamp-fazit/
2012-05-14 NoSQL in .NET - mit Redis und MongoDB
1: Vortrag: NoSQL in .NET – mit Redis und MongoDB
Der Vortrag führt in die Theorie ein, stellt die beiden NoSQL-Datenbanksysteme Redis und MongoDB näher vor und gibt Praxisbeispiele. Ich präsentiere die beiden Systeme live an einem ASP.NET MVC Beispielprojekt, welches allen Teilnehmern zur Verfügung gestellt wird.
2. Vortrag: NoSQL – Dokumente und Relationen
Eine dokumentenbasierte NoSQL-Datenbank wie MongoDB hat nicht nur eine andere API zur Abfrage der Daten. Die tiefergehende Neuerung ist eine grundlegende andere Art die Daten abzuspeichern. Der Vortrag konzentriert sich auf Schema-Design, das Map-Reduce Verfahren und bekannten Patterns für Dokumenten-basierte Datenbanken.
2012-04-12 - AOP .NET UserGroup Niederrhein
1. The document discusses applying architectural patterns and principles like logging, validation, transactions, and exception handling to business logic code in .NET.
2. It shows an example of refactoring a method that rents a book to a customer to add logging, validate method parameters, retry transactions using a scope, and handle exceptions.
3. By applying these techniques, the business logic is made more robust, maintainable, and aligned with best practices for .NET development.
2012-03-20 - Getting started with Node.js and MongoDB on MS Azure
This talk concentrates on Microsoft's cloud platform, called Azure. Johannes gives an introduction to the new platform and speaks about its possibilities and limitations. By utilizing the Windows Azure SDK for Node.js he is going to demonstrate a simple JavaScript-driven browser game that bases on Node.js and MongoDB.
More from Johannes Hoppe (20)
MDC kompakt 2014: Hybride Apps mit Cordova, AngularJS und Ionic
MDC kompakt 2014: Hybride Apps mit Cordova, AngularJS und Ionic
2013-06-24 - Software Craftsmanship with JavaScript
2013-06-24 - Software Craftsmanship with JavaScript
2012-10-12 - NoSQL in .NET - mit Redis und Mongodb
2012-10-12 - NoSQL in .NET - mit Redis und Mongodb
2012-08-29 - NoSQL Bootcamp (Redis, RavenDB & MongoDB für .NET Entwickler)
2012-08-29 - NoSQL Bootcamp (Redis, RavenDB & MongoDB für .NET Entwickler)
2012-05-10 - UG Karlsruhe: NoSQL in .NET - mit Redis und MongoDB
2012-05-10 - UG Karlsruhe: NoSQL in .NET - mit Redis und MongoDB
2012-03-20 - Getting started with Node.js and MongoDB on MS Azure
2012-03-20 - Getting started with Node.js and MongoDB on MS Azure
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Overview
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5th LF Energy Power Grid Model Meet-up Slides
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The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Building Production Ready Search Pipelines with Spark and Milvus
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GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
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