Deploying Flowable at scale in AWS
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Grant Allen, CTO Chief Product Officer at Dow Jones explains how to deploy Flowable at scale in AWS. It was presented at the Flowfest 2018 in Barcelona, Spain
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© 2010 All Rights Reserved. SAP UAIntroduction to Busine.docx
© 2010 All Rights Reserved. SAP UA
Introduction to Business Intelligence
SAP University Alliances
Version 2.0
Authors Klaus Freyburger
Peter Lehmann
*
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
AgendaWhat is Business Intelligence?What is a „Data Warehouse“?What are the benefits and challenges?What does „multi-dimensional“ mean?What is „OLAP“?OLTP versus OLAPBusiness Intelligence tool box
Agenda
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
What is Business Intelligence?
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
What is Intelligence?
Definition:
The ability to learn or understand or deal with new and trying situations
The ability to apply knowledge to manipulate one’s environment
Source: Merriam-Webster’s Online Dictionary
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Intelligence in BusinessWhat is the current status of the business?What’s going well?What needs improvement?
What are the business’ strengths and weaknesses?
Are there opportunities for innovation or competitive advantage?
How do we improve our decision making?
Yesterday – Now - Tomorrow
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Main Objective of Business Intelligence Support intelligence (e.g., knowledge of status)
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
A Brief History of Information SystemsERP Systems
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Information Systems in a Company!?
SAP UA
Page *
© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Definition: „Business Intelligence“„Business Intelligence“ covers strategies, processes and technologies in order to achieve knowledge about status, potentials and perspectives of a company out of heterogeneous and distributed data.
?
Definition Source: Institut für Business Intelligence (IBI), http://www.i-bi.de/home/index.html
SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Business Intelligence Turns Data into Knowledge
Data
Information
Knowledge
Decision
Product A
Product B
Customer Smith
Customer Smith
buys product A
Product A & B have
a 80% sales correlation
Offer product B to
customer Smith
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SAP UA
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© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
Business Intelligence Stages
Source: Brobst, S. and J. Rarey, "Five Stages of Data Warehouse Decision Support Evolution", DSSResources.COM, 01/06/2003
SAP UA
Page *
© 2010 SAP AG
SAP BI Curriculum
BI1-M1 Introduction to Business Intelligence
AgendaWhat is Business Intelligence?What is a „Data Warehouse“?What are the benefits and challenges?What does „multi-dimensional“ mean?What.
Jakob Freund: Camunda for IT Executives - Camunda Days
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All impoundments of water need flowing mostly pollution free water constantly coming in or they become stagnant. The Data Lake is no different.
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Presentation quaest strategic sourcing software
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2) The Quaest platform provides configurable sourcing apps and tools, pre-defined analytics, and a process environment hosted on Microsoft SharePoint. It includes over 50 apps, 100 analytics, and support from a tutor network.
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Business intelligence (BI) is a set of tools and technologies that analyze raw data and convert it into useful information that can help businesses make better decisions. BI databases store facts, like sales amounts, and assign multiple attributes to each fact using a "star schema." This allows for fast analysis of data stored in online analytical processing (OLAP) databases. OLAP databases have dimensions like time, accounts, customers, and products that can be aggregated on the fly. Extract, transform, and load (ETL) tools are used to retrieve raw data, transform it into a format for OLAP databases, and load the data into "cubes" for analysis. User interfaces for BI tools include web interfaces for sharing reports, mobile interfaces
Profile_Harish_Gaddale
Harish Gaddale is an integration architect with 8 years of experience in integration architecture and development. He has extensive experience with TIBCO products and full SDLC, and has worked on numerous integration projects for customers like NXP Semiconductors. Some of his responsibilities have included designing integration architectures, developing interfaces, unit testing, supporting go-lives, and providing technical consultancy. He is proficient in technologies like TIBCO Suite, Web services, EDI, and SaaS integrations with applications such as Workday, Ariba, and Salesforce.
Modern Thinking área digital MSKM 21/09/2017
Modern Thinking: Cómo el Big Data y Cognitive están cambiando la estrategia de Marketing
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Two #ModernDataStack talks and one DevOps talk: https://youtu.be/4R--iLnjCmU
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SAP Systems Integration by SAP PI (XI)
This is my master's thesis final presentation. For more info please check my blog: www.celikalper.com
Alper Celik
Implement Data Ware House
This document discusses implementing a data warehouse for an insurance company called Axiom Care. It outlines the problems with the company's current inability to provide strategic information to decision makers. The proposed solution is to create a data warehouse with schemas representing policy creation and claims processing data. It provides a work breakdown structure and job schedule for the project. Risks are also identified such as unrealistic timelines, budget issues, and technical challenges around data quality and scalability.
Pioneering a path to the future with JBoss BPM
As one of the top four Dutch financial institutions, SNS Bank in the Netherlands made a strategic decision to use technology to empower its customers online by fully automating its service and selling channels. In order to effectively move toward a full-scale straight-through processing (STP) experience, SNS Bank chose to achieve its goals by making use of open source software, service-oriented architecture (SOA), and business process management (BPM).
In this session, SNS Bank’s Michel Blok and Red Hat’s Eric Schabell will:
- Take attendees through the history of SNS Bank, laying the groundwork for the vision and strategy for choosing JBoss open source solutions
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The SAP Startup Focus Program – Tackling Big Data With the Power of Small by ...
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A Journey to a Serverless Business Intelligence, Machine Learning and Big Dat...
In this talk we will describe the journey we made with one of our customers, Volotea, to deploy a serverless Business Intelligence (BI), Machine Learning (ML) and Big Data (BD) platform on the Cloud. The new platform leverages Platform-as-a-Service (PaaS) Cloud services, and it is the result of the reengineering and extension of an existing platform based on Cloud Infrastructure-as-a-Service (IaaS) services and bare-metal systems. Managing and maintaining BI, ML and BD platforms based on bare-metal or IaaS deployments is not a straightforward task, and as size and complexity grow, we often find ourselves spending more and more time in tasks that are rather administrative, more than of a development or analytics nature. That is exactly what Volotea realized, and together we envisioned and executed a plan to lift and reengineer their platform into a new solution that leverages Microsoft Azure PaaS services. We have delivered a solution that manages to greatly reduce the administrative burden as well as the technical complexity when implementing new use cases. The new platform is based on the Microsoft Azure stack and it includes Azure Data Lake, Azure Data Lake Analytics, Azure Data Factory, Azure Machine Learning and Azure SQL Database. Join us in this talk where we will share our lessons learned and we will discuss how to plan and execute such an endeavor.
Sap pi overview
The document analyzes the needs for creating a SAP PI integration platform at Vattenfall Sweden. It discusses Vattenfall's business environment undergoing changes due to energy market regulations. An analysis is presented of the options of SAP PI and Microsoft BizTalk for the integration platform. The analysis finds that while SAP PI should be used for SAP-to-SAP integration, BizTalk is more suitable currently due to lack of SAP PI expertise, its unclear roadmap, BizTalk's maturity, and avoiding vendor lock-in. The proposal is to use BizTalk for 1-2 years and monitor SAP PI's development to potentially switch to it if it becomes more mature and widely adopted.
Nikhil sap fico training ppt
The document provides information about SAP modules and ERP concepts. It defines ERP as enterprise resource planning software that integrates various business functions like finance, manufacturing, supply chain, and human resources. The key modules described include FI, CO, MM, SD, and Basis. It also outlines SAP architecture and positions like functional consultant. Dashboards and visualization tools are presented as ways to access and report on ERP data.
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© 2010 All Rights Reserved. SAP UAIntroduction to Busine.docx
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Artificia Intellicence and XPath Extension Functions
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
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Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
E-commerce Application Development Company.pdf
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
APIs for Browser Automation (MoT Meetup 2024)
APIs for Browser Automation:
Selenium, Cypress, Puppeteer, and Playwright. Ministry of Testing Athens. Meetup on the Beach. 30 May 2024.
Launch Your Streaming Platforms in Minutes
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Quarkus Hidden and Forbidden Extensions
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx
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Deploying Flowable at scale in AWS
- 1. FLOWFEST - NOVEMBER 2018PROFESSIONAL INFORMATION BUSINESS Professional Information Business Deploying Flowable at scale in AWS 1
- 2. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 2 Introduction ● Dow Jones Professional Information Business - what does that really mean? ● Dealing with content, human research, and automated processing at scale ● Our historic approach, and why we needed BPM ● Choosing Flowable - as easy as ABC (anything but closed-source :) ) ● Our architecture, how we manage and evolve it ● Challenges ● Future Expansion and Long term goals
- 3. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 3 Dow Jones Professional Information Business A Brief Overview Millions of articles flow into our research engine Factiva.
- 4. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 4 Dow Jones Professional Information Business A Brief Overview (cont.) A range of processing steps occur in our content pipeline, variously using tools like rules-based coding engines, normalisation/transformation, ML models, etc.
- 5. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 5 Dow Jones Professional Information Business A Brief Overview (cont.) Historically, we scaled with people. If we needed to cover more content, or extract more structured data, we’d just add more people!
- 6. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 6 Dow Jones Professional Information Business A Brief Overview (cont.) We knew BPM was part of the solution. While not magically doing all of the work, it helps us standardise, decide what a “task” really is, highlights best areas for automation, unlocks insight, and more!
- 7. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 7 Choosing Flowable ● Light vs Heavy BPM ● Ease of Adoption ● Other features, e.g. Forms ● Support model(s)
- 8. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 8 Technical Architecture (v1.0)
- 9. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 9 Technical Architecture (v1.0)
- 10. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 10 PIBEP CI/D Workflows (docker)
- 11. PROFESSIONAL INFORMATION BUSINESS FLOWFEST - NOVEMBER 2018 11 PIBEP CI/D Workflows (docker)
- 12. 12 Challenges ● Decoupling BPM engine from custom workflow application ● User/Groups integration with the external user management application
- 13. 13 Future Expansion and Long Term Goals ● History / archive solution ● Deeper analytics, share data with operational and customer data lake ● Parallel deployments across other major businesses within Dow Jones - Ad Tech ● Using endpoints from AWS Sage Maker to use ML models
- 14. Thank you Professional Information Business