The Common BI/Big Data Challenges and Solutions presented by seasoned experts, Andriy Zabavskyy (BI Architect) and Serhiy Haziyev (Director of Software Architecture).
This was a complimentary workshop where attendees had the opportunity to learn, network and share knowledge during the lunch and education session.
Keys toSuccess: Business Intelligence Proven, Practical Strategies That WorkSenturus
Keys you need to know to achieve BI success. View the webinar video recording and download this deck: http://www.senturus.com/resources/keys-to-success-in-business-intelligence/.
With realistic advice taken from Senturus CEO and co-founder John Peterson, he shares 16+ years of real world expertise working with over 1000 clients and 2000 projects to describe how you can truly optimize BI investments.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Keys toSuccess: Business Intelligence Proven, Practical Strategies That WorkSenturus
Keys you need to know to achieve BI success. View the webinar video recording and download this deck: http://www.senturus.com/resources/keys-to-success-in-business-intelligence/.
With realistic advice taken from Senturus CEO and co-founder John Peterson, he shares 16+ years of real world expertise working with over 1000 clients and 2000 projects to describe how you can truly optimize BI investments.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
A summary of the philosophy and approach taken by the TravelBird Data Science team (and company as a whole) that allows rapid development of new machine learning algorithms, data insights, and integration into production and operations.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
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 Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn
Performance Considerations in Logical Data WarehouseDenodo
Watch the live presentation on-demand here: https://goo.gl/6RsqrA
When processing very large amounts of data at the speed of thought, performance questions raise their ugly head. Logical data warehouse architectures rival the conventional data warehouses in speed while reducing the need to extract, transform, and load the data.
Watch this Denodo DataFest 2017 session to discover:
• The perks of a logical data warehouse vs. the physical data warehouse.
• Challenging the myths of performance of a logical data warehouse.
• Denodo's dynamic query optimizer tool.
What you Need to Know about Machine Learning?ESRI Bulgaria
Passionate about Machine learning? Same for us here @Dreamix.
Machine learning is so vast today that you probably use it dozens of times a day without knowing it. In the past years, machine learning has given us effective web search,self-driving cars, practical speech recognition. Now is the time to learn more about it.
Enjoy!
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
A summary of the philosophy and approach taken by the TravelBird Data Science team (and company as a whole) that allows rapid development of new machine learning algorithms, data insights, and integration into production and operations.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
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 Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
Presentation about BigData from a German Webcast: http://business-services.heise.de/it-management/big-data/beitrag/big-data-technologie-einsatzgebiete-datenschutz-160.html?source=IBM_12_2013_IT_Conn
Performance Considerations in Logical Data WarehouseDenodo
Watch the live presentation on-demand here: https://goo.gl/6RsqrA
When processing very large amounts of data at the speed of thought, performance questions raise their ugly head. Logical data warehouse architectures rival the conventional data warehouses in speed while reducing the need to extract, transform, and load the data.
Watch this Denodo DataFest 2017 session to discover:
• The perks of a logical data warehouse vs. the physical data warehouse.
• Challenging the myths of performance of a logical data warehouse.
• Denodo's dynamic query optimizer tool.
What you Need to Know about Machine Learning?ESRI Bulgaria
Passionate about Machine learning? Same for us here @Dreamix.
Machine learning is so vast today that you probably use it dozens of times a day without knowing it. In the past years, machine learning has given us effective web search,self-driving cars, practical speech recognition. Now is the time to learn more about it.
Enjoy!
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
Instant Visualizations in Every Step of AnalysisDatameer
Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives.
How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process.
In this webinar, you will learn how to:
-Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data
-Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations
-Create self-service visualizations for business and IT users
How to successfully implement Business Intelligence into your organisation.
A completely agnostic and independent view from a market leader in delivering technology transformation.
Details on how to build a strategy to successfully execute on and more importantly how to get the business to adopt Business Intelligence into their day to day role.
Essential tool kit for any organisation looking to invest in Business Intelligence.
Introduce the Big-Data data characteristic, big-data process flow/architecture, and take out an example about EKG solution to explain why we are run into big data issue, and try to build up a big-data server farm architecture. From there, you can have more concrete point of view, what the big-data is.
Matinée Micropole DE LA BI A LA DATA INTELLIGENCE 18-10-2016Micropole Group
Avec l’explosion du volume des données disponibles (structurées, non structurées, dark data, open data…) et la multiplicité des sources d’information, les utilisateurs ont besoin d’accéder à des données de plus en plus nombreuses et d’avoir la main sur leur exploration et leur manipulation pour gagner en agilité.
Webinar: Bring Your Data to Life with Power BI-2016-01-28TechSoup
Power BI, a free Microsoft program, enables anyone to get rapid actionable insights from any data, any way, anywhere. Within minutes you’ll see and experience your data in new ways that you never imagined were possible. Using natural language and simple drag-and-drop ease, you can proactively monitor key metrics, spot real-time trends and get critical alerts from personalized dashboards.
Pentaho Data Integration. Preparing and blending data from any source for analytics. Thus, enabling data-driven decision making. Application for education, specially, academic and learning analytics.
Global Business Intelligence (BI) software vendor, Yellowfin, and Actian Corporation, pioneers of the record-breaking analytical database Vectorwise, will host a series of Big Data and BI Best Practices Webinars.
These are the slides from that presentation.
The Big Data & BI Best Practices Webinars and associated slides examine the phenomenal growth in business data and outline strategies for effectively, efficiently and quickly harnessing and exploring ‘Big Data’ for competitive advantage.
Choosing technologies for a big data solution in the cloudJames Serra
Has your company been building data warehouses for years using SQL Server? And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”? What technologies and tools should use? That is what this presentation will help you answer. First we will cover what questions to ask concerning data (type, size, frequency), reporting, performance needs, on-prem vs cloud, staff technology skills, OSS requirements, cost, and MDM needs. Then we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data? Should I use a data lake? Do I still need a cube? What about Hadoop/NoSQL? Do I need the power of MPP? Should I build a "logical data warehouse"? What is this lambda architecture? Can I use Hadoop for my DW? Finally, we’ll show some architectures of real-world customer big data solutions. Come to this session to get started down the path to making the proper technology choices in moving to the cloud.
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarioskcmallu
What's the origin of Big Data? What are the real life usage scenarios where Hadoop has been successfully adopted? How do you get started within your organizations?
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
Erik Baardse and Ajit Gadge from EDB Postgres presented on how to transform your DBMS in order to drive digital business. How Postgres enables you to support a wider range of workloads with your relational database which opens the Big Data doors. They also cover EnterpriseDB’s Strategy around Big Data which focuses on 3 areas and finally last but not the last how to find money in IT with Big Data and digital transformation
Enable Better Decision Making with Power BI Visualizations & Modern Data EstateCCG
Self-service BI empowers users to reach analytic outputs through data visualizations and reporting tools. Solution Architect and Cloud Solution Specialist, James McAuliffe, will be taking you through a journey of Azure's Modern Data Estate.
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
As a follow-on to the presentation "Building an Effective Data Warehouse Architecture", this presentation will explain exactly what Big Data is and its benefits, including use cases. We will discuss how Hadoop, the cloud and massively parallel processing (MPP) is changing the way data warehouses are being built. We will talk about hybrid architectures that combine on-premise data with data in the cloud as well as relational data and non-relational (unstructured) data. We will look at the benefits of MPP over SMP and how to integrate data from Internet of Things (IoT) devices. You will learn what a modern data warehouse should look like and how the role of a Data Lake and Hadoop fit in. In the end you will have guidance on the best solution for your data warehouse going forward.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Which Change Data Capture Strategy is Right for You?Precisely
Change Data Capture or CDC is the practice of moving the changes made in an important transactional system to other systems, so that data is kept current and consistent across the enterprise. CDC keeps reporting and analytic systems working on the latest, most accurate data.
Many different CDC strategies exist. Each strategy has advantages and disadvantages. Some put an undue burden on the source database. They can cause queries or applications to become slow or even fail. Some bog down network bandwidth, or have big delays between change and replication.
Each business process has different requirements, as well. For some business needs, a replication delay of more than a second is too long. For others, a delay of less than 24 hours is excellent.
Which CDC strategy will match your business needs? How do you choose?
View this webcast on-demand to learn:
• Advantages and disadvantages of different CDC methods
• The replication latency your project requires
• How to keep data current in Big Data technologies like Hadoop
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
Data Pipelines and Tools to Integrate with Power BI and Spotfire.pdfGregKreutzer2
The goal is to highlight the business value of utilizing these skills in oil and gas applications while creating a workshop that can still accommodate and provide value to a larger audience that has a wide range of analytic and data science skills, roles and interests.
SMAC - Social, Mobile, Analytics and Cloud - An overview Rajesh Menon
In this presentation, all the aspects of SMAC are covered in as much detail as possible. You will find some ideas worth sharing and also get attuned to Social, Mobile, Analytics and Cloud
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. SoftServe BI/Big Data Lunch and Learn Workshop in Utah
January 30, 2013
The Common BI/Big Data Challenges and Solutions presented by seasoned
SoftServe experts, Andriy Zabavskyy (BI Architect) and Serhiy Haziyev (Director of
Software Architecture).
This was a complimentary workshop where attendees had the opportunity to learn,
network and share knowledge during the lunch and education session.
About SoftServe Inc.
SoftServe, founded in 1993, is a leading global outsourced product and application
development company dedicated to empowering businesses worldwide by providing end-toend capabilities from product concept to completion. Utilizing Product Development Services
2.0 (PDS 2.0), we deliver proactive solutions in the areas of SaaS/Cloud, Mobility, BI/Analytics
and UI/UX for industries including Healthcare, Retail, Manufacturing, Logistics, and
Infrastructure & Storage. SoftServe is a rapidly growing global company with 3,000
professionals and offices in North America, Western Europe, Russia and Ukraine.
4. Typical BI Solution
Data Sources
Data Integration
OLTP: CRM,
ERP, Finance
Data Warehouse
Data Mining
Users
Predictive
Prescriptive
Analytics
Data
Warehouse
OLAP cubes
Data Visualization
and Analysis
Flat files
ETL/ELT
Big Data
Reports
Dashboards
Spreadsheets
Legacy System
BI Tools
Analysts
6. Dashboard & Scorecard
Client
Problem:
▪ Single view from multiple
sources
▪ Track performance against
company targets
Internet
Solution:
▪ Dashboard
▪ KPI and Scorecards
Server Tier
7. Dashboard & Scorecard: Implementation
Software Vendors Offering
Boxed solutions from
big players
Development Efforts
Customization
(e.g. SAS, SAP, IBI)
Dashboard Frameworks
(e.g. Tableau, QlikView, JasperSoft)
Dashboard libs
(JIDE libs)
Custom defined KPI
Integration Efforts
Custom defined KPI &
Custom built dashboard
framework
8. Dashboard & Scorecard: Highlights
• Adopting/Customizing of business lines ready
solution could be painful, long and costly process
• Not all dashboard solutions support multitenancy out-of-the-box
9. Self Service BI
Problem:
▪ Give ability for BI users to
explore and analyze data in
highly customizable manner
BI Users
Data Model
Solution:
Toolset
▪ Expose to users a data model
▪ Give a toolset with data
exploring and analysis
capabilities
OLAP
In-Memory
RDBMS/
NoSQL
10. Self Service BI: Implementation
• OLAP engines with proper OLAP
viewers
• BI tools with in-memory engines and
semantic/domain layers
• Report Authoring Tools :
– Microsoft Report Builder
– JasperServer Report Designer
11. Self Service BI: Traditional vs Agile BI Trade-off
Features
Time to Value
Self Service
Collaboration
Interactivity and UX
Customization
Data Quality
Pixel-perfect
Low cost solutions
Traditional
Agile
12. Self Service BI: Highlights
• Need to educate data consumers to properly use
SSBI tools
• Desktop versions of many SSBI vendors are often
more mature in comparison to Web tools
• In-memory capabilities are limited by RAM size
15. ELT
Problem:
• Efficiently processing very
large volumes of data within
ever shortening processing
windows
Solution:
• Perform transformation steps
on target platform
• Set-based processing
Data Warehouse
Semantic Layer
Load
Staging Layer
Transform
Source
Source
Extract
16. ELT: Highlights
• Some data integration platforms have clearly separated
ETL and ELT components
• Consider usage of custom scripts native to target
platform vs. built-in DI component
17. ETL vs. ELT
ETL
Flow
Advantages
Disadvantages
ELT
Data pipeline are used
Transformations to the data one
record at a time
Intermediate data results are
stored in memory
Data is loaded into the
destination server
Set-based processing
Transformations and Lookups
are within the SQL
Complex transformations
Intermediate results in memory
is faster than persisting to disk
The power of the relational
database system can be
utilized for very large data
sets
Large data sets could
Load on RDBMS
overwhelm the memory
More disk activity
Updates are more efficient using
set-based processing
19. Kimball’s Multidimensional EDW
Problem:
• Integrate and consolidate data
from heterogeneous sources
• Keep data history
Data Warehouse
Solution:
• Use multidimensional model to
store data
• Iterate by business lines
• Integrate by conformed
dimensions
Data Sources
23. DWH: Highlights
Implications of column-based storage:
– Additional columns vs. Junked dimensions
– Update scenarios should be omitted where
possible
– Partitions scenario should be carefully established
to support maintenance activities
26. Big Data: Hybrid Approach
Problem:
• Under big data circumstances:
– Flexible online analytics
– Access to most detailed raw
data
Operational and
Historical Analytics
Solution:
• Analytical RDBMS for online
analytics
• NoSQL DB as source for
RDBMS and most detailed row
data
NoSQL
RDBMS/DW
Source
28. Tape Library
HDFS
Disk Array
Throughput
(600 GB load time)
140-500 MB/s
(0.3-1.2 h)
10-30 MB/s
(5.5-16 h)
50-700 MB/s
(0.25-4 h)
2-40 MB/s
(83h)
Max capacity
30-900 PB
21+ PB
16 PB
~Unlimited
Max file size
~Unlimited
~Unlimited
4 – 16 TB (OSlimited)
Accessibility
SAN
Java API, HTTP,
NFS (MapR)
NFS, CIFS, SAN
REST, SOAP
Scalability
Adding cartridges
Adding nodes
Adding disks
Pay-as-you-go
Reliability
Redundancy
Redundancy
(MapR)
Redundancy
99.99%
Encryption
Yes
Yes*
Yes*
Yes
By datacenter
By datacenter
By datacenter
By Amazon
?
No
Yes
Yes
Yes
No
No
Yes
Yes**
100 TB Cost
$40-60K
$100-200K
$80-400K
$132-216K/year
$12-96K/year
1 PB Cost
$90-140K
$1-2M
$0.5-4M
$1.1-1.6M/year
$120-360K/year
15 PB Cost
$0.7-1.2M
$15-30M
~$18M
$9.9-15M/year
$1.8-3.5M/year
HIPAA Compliancy
Random access
Parallel processing
Retention Storage
Requirements
Operation Storage
Big Data isn’t only Hadoop
Amazon S3
Amazon
Glacier
5 TB
40 TB
No
29. Big Data: Highlights
• Clickstream analysis is a classic use case
• Scheduled reports are well suited for Hadoop based
reports
• Majority of Self Service BI tools need relational
representation of data
33. DM Models: Implementation
• Custom algorithm implementation
• Statistical packages like R
• Ready data mining model implementations
34. DM Models: Highlights
• The approach should be:
Problem -> Data Strategy -> Data analysis
… and not vice versa
• DM Algorithms should be carefully selected
• DM Algorithms are highly dependent on business
domain you create them for
35. SoftServe BI Maturity Model
• Improving the business
Wisdom
• decision making (executives)
• data mining, forecasting
• Gaining business insight
Knowledge
• analytical reports (analysts)
• dashboards, KPIs, scorecards, slice & dice, data
warehouse, OLAP
• Measuring and monitoring
Information
• consolidated reports (managers)
• charts, parametrized reports, dedicated
reporting database
• Running the business
Data
• personal operational reports
(workers, customers)
• simple reports, OLTP or files
37. More Info about SoftServe BI Offerings
http://www.softserveinc.com/en-us/services/software-architecture/
http://www.softserveinc.com/en-us/services/bi-analytics/