Here's the second version of our big data landscape. Thoughts, questions, comments? We'd love to hear your feedback in the comments section here: http://wp.me/p2dLS7-6A
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.
With more than 30 years of experience in the insurance industry, SAS can help you achieve long-term success and obtain peace of mind. Integrated and extensible insurance solutions built on a flexible business analytics framework and insurance-specific data model speed up both implementation and results, giving you a fast track to significant ROI.
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.
With more than 30 years of experience in the insurance industry, SAS can help you achieve long-term success and obtain peace of mind. Integrated and extensible insurance solutions built on a flexible business analytics framework and insurance-specific data model speed up both implementation and results, giving you a fast track to significant ROI.
So what is SAP HANA? How can it help my area (Line of Business) and our business overall!. Presentation lays out BASICS and how can help users enable their area/business "Real time".
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
An attempt at categorizing the thriving big data ecosystem by @mattturck and @shivonZ - comments are welcome (please add your thoughts on mattturck.com)
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
A 3 day examination preparation course including live sitting of examinations for students who wish to attain the DAMA Certified Data Management Professional qualification (CDMP)
chris.bradley@dmadvisors.co.uk
Databricks: A Tool That Empowers You To Do More With DataDatabricks
In this talk we will present how Databricks has enabled the author to achieve more with data, enabling one person to build a coherent data project with data engineering, analysis and science components, with better collaboration, better productionalization methods, with larger datasets and faster.
The talk will include a demo that will illustrate how the multiple functionalities of Databricks help to build a coherent data project with Databricks jobs, Delta Lake and auto-loader for data engineering, SQL Analytics for Data Analysis, Spark ML and MLFlow for data science, and Projects for collaboration.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
Big Data Taiwan 2014 Track1-3: Big Data, Big Challenge — Splunk 幫你解決 Big Data...Etu Solution
講者:SYSTEX 數據加值應用發展部產品經理 | 陶靖霖
議題簡介:認清現實吧! Big Data 是個熱門詞彙、熱門議題,但是問題的核心仍然圍繞在資料處理的流程、架構與技術,要踏入 Big Data 的領域,使用者會遭遇哪些挑戰? Splunk 被譽為「全球最佳的 Big Data Company」,究竟在資料處理的流程中擁有什麼獨特的技術優勢,能夠幫助使用者克服這些挑戰?又有哪些成功幫助使用者從資料中萃取出價值的應用案例?歡迎來認識 Splunk 以及全球 Big Data 成功案例。
So what is SAP HANA? How can it help my area (Line of Business) and our business overall!. Presentation lays out BASICS and how can help users enable their area/business "Real time".
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
An attempt at categorizing the thriving big data ecosystem by @mattturck and @shivonZ - comments are welcome (please add your thoughts on mattturck.com)
Organizations are grappling to manually classify and create an inventory for distributed and heterogeneous data assets to deliver value. However, the new Azure service for enterprises – Azure Synapse Analytics is poised to help organizations and fill the gap between data warehouses and data lakes.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
A 3 day examination preparation course including live sitting of examinations for students who wish to attain the DAMA Certified Data Management Professional qualification (CDMP)
chris.bradley@dmadvisors.co.uk
Databricks: A Tool That Empowers You To Do More With DataDatabricks
In this talk we will present how Databricks has enabled the author to achieve more with data, enabling one person to build a coherent data project with data engineering, analysis and science components, with better collaboration, better productionalization methods, with larger datasets and faster.
The talk will include a demo that will illustrate how the multiple functionalities of Databricks help to build a coherent data project with Databricks jobs, Delta Lake and auto-loader for data engineering, SQL Analytics for Data Analysis, Spark ML and MLFlow for data science, and Projects for collaboration.
Data Vault Modeling and Methodology introduction that I provided to a Montreal event in September 2011. It covers an introduction and overview of the Data Vault components for Business Intelligence and Data Warehousing. I am Dan Linstedt, the author and inventor of Data Vault Modeling and methodology.
If you use the images anywhere in your presentations, please credit http://LearnDataVault.com as the source (me).
Thank-you kindly,
Daniel Linstedt
Big Data Taiwan 2014 Track1-3: Big Data, Big Challenge — Splunk 幫你解決 Big Data...Etu Solution
講者:SYSTEX 數據加值應用發展部產品經理 | 陶靖霖
議題簡介:認清現實吧! Big Data 是個熱門詞彙、熱門議題,但是問題的核心仍然圍繞在資料處理的流程、架構與技術,要踏入 Big Data 的領域,使用者會遭遇哪些挑戰? Splunk 被譽為「全球最佳的 Big Data Company」,究竟在資料處理的流程中擁有什麼獨特的技術優勢,能夠幫助使用者克服這些挑戰?又有哪些成功幫助使用者從資料中萃取出價值的應用案例?歡迎來認識 Splunk 以及全球 Big Data 成功案例。
Big Data, Big Deal? (A Big Data 101 presentation)Matt Turck
Background: I prepared this slide deck for a couple of “Big Data 101” guest lectures I did in February 2013 at New York University’s Stern School of Business and at The New School. They’re intended for a college level, non technical audience, as a first exposure to Big Data and related concepts. I have re-used a number of stats, graphics, cartoons and other materials freely available on the internet. Thanks to the authors of those materials.
The Comprehensive Approach: A Unified Information ArchitectureInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Slides from the Live Webcast on May 29, 2012
The worlds of Business Intelligence (BI) and Big Data Analytics can seem at odds, but only because we have yet to fully experience comprehensive approach to managing big data – a Unified Big Data Architecture. The dynamics continue to change as vendors begin to emphasize the importance of leveraging SQL, engineering and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing.
Register for this episode of The Briefing Room to learn the value of taking a strategic approach for managing big data from veteran BI and data warehouse consultant Richard Hackathorn. He'll be briefed by Chris Twogood of Teradata, who will outline his company's recent advances in bridging the gap between Hadoop and SQL to unlock deeper insights and explain the role of Teradata Aster and SQL-MapReduce as a Discovery Platform for Hadoop environments.
For more information visit: http://www.insideanalysis.com
Watch us on YouTube: http://www.youtube.com/playlist?list=PL5EE76E2EEEC8CF9E
BDT101 Big Data with Amazon Elastic MapReduce - AWS re: Invent 2012Amazon Web Services
Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. This session seeks to answer questions such as "what is big data," "how can I use unstructured data," and "how can I integrate data collections from different sources" using Hadoop with Amazon Elastic MapReduce. Join general manager of EMR, Peter Sirota, on a journey through real-world use cases of data-driven discovery.
Big Data Beyond Hadoop*: Research Directions for the FutureOdinot Stanislas
Michael Wrinn
Research Program Director, University Research Office,
Intel Corporation
Jason Dai
Engineering Director and Principal Engineer,
Intel Corporation
Evolving analytics at ebay - 2012 Tableau Customer Conferencegdougan1
From Data to Knowledge: Evolving Analytics at ebay.
Gary Dougan's presentation at TCC 2012 (http://www.linkedin.com/in/garydougan)
Learn about eBay’s extensive analytics environment, and how eBay’s Business Intelligence platform team is enabling “visual analytics” across a complex ecosystem of platforms, technologies, and data enthusiasts, to synthesize information and derive insights from dynamic and complex data.
The Digital Intelligence Imperative — Driving Digital Customer Experiences W...Tealium
Joe Stanhope, Forrester Research
It’s no secret that marketing sophistication is growing by leaps and bounds to support the delivery of relevant and engaging customer experiences. And as marketing’s reach grows, so too has the remit of digital analytics. The emergence of digital analytics as a hub for understanding and optimizing customer experiences has placed a premium on the effective collection, processing, and distribution of data. This trend dovetails with the emergence of tag management solutions, which has rapidly become a key capability for supporting digital data management.
Selecting BI Tool - Proof of Concept - Андрій МузичукIgor Bronovskyy
A large number of tools and techniques have been developed over the years to support managerial decision making. Thus process of selecting appropriate BI tool turns to be an issue. Implementing and deploying a BI initiative can be lengthy, expensive and failure pron. The Proof of concept method can be used by stakeholders to avoid unnecessary losses.
In the presentation, the description of Proof of Concept method is provided based on the example of selecting among Microsoft stack, MicroStrategy and Business Object Bi tools. The example includes above mentioned technologies overview, reports modeling process, reports development process, report integration in SharePoint, performance testing as well as the decision making model and summary for final tools selection.
Building an AI Startup: Realities & TacticsMatt Turck
AI is all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants. As always, the realities of building a startup are different, and the path to success requires not just technical prowess but also thoughtful market positioning and business excellence.
In a talk of interest to anyone building or implementing an AI product, Matt Turck and Peter Brodsky leverage hundreds of conversations with AI (and big data) founders and hard-learned lessons building companies from the ground up to highlight successful strategies and tactics.
Topics include:
Successful data acquisition strategies
Data network effects
Competing with the giants
A pragmatic approach to building an AI team
Why social engineering is just as important to success as groundbreaking AI technology
Annual Big Data Landscape prepared by FIrstMark. Check out full blog post: "Is Big Data Still a Thing"? at http://mattturck.com/2016/02/01/big-data-landscape/
Among all the excitement for the Internet of Things and the resurgence of hardware as an investable category, venture capitalists, many of whom new to the space, have been re-discovering the opportunities and challenges of working alongside entrepreneurs to build hardware companies. Combined with a rapid evolution of the venture financing path across categories over the last couple of years, the increasing importance of crowdfunding and a certain frothiness in the market, this leads to a certain confusion, as both entrepreneurs and VCs try to figure out the best way of financing and scaling hardware startups. Some patterns emerge, however: for example, VCs are mostly interested in opportunities that include a strong software and data component; and they are increasingly demanding when it comes to seeing the product actually shipping and gaining early traction.
The Astonishing Resurrection of AI (A Primer on Artificial Intelligence)Matt Turck
Supporting slides for a presentation at the Yale Entrepreneurship Breakfast on March 27, 2015. A primer on how artificial intelligence (AI) rose from of the ashes to become a fascinating category for startup founders and venture capitalists. Mentions our portfolio company x.ai as an example.
Internet of Things Landscape (Version 3.0)Matt Turck
(Latest/revised version uploaded December 23, 2014)
It's been about 18 months since my original attempt at charting the Internet of Things (IoT) space. To say the least, it's been a period of extraordinary activity in the ecosystem.
While the Internet of Things will inevitably ride the ups and downs of inflated hype and unmet expectations, at this stage there's no putting the genie back in the bottle. The Internet of Things is propelled by an exceptional convergence of trends (mobile phone ubiquity, open hardware, Big Data, the resurrection of AI, cloud computing, 3D printing, crowdfunding). In addition, there's an element of self-fulfilling prophecy at play with enterprises, consumers, retailers and the press all equally excited about the possibilities. As a result, the IoT space is now reaching escape velocity. Whether we're ready for it or not, we're rapidly evolving towards a world where just about everything will be connected. This has profound implications for society and how we collectively interact with the world around us. Key concerns around privacy and security will need to be addressed.
NYC: A Natural Home for European EntrepreneursMatt Turck
Presentation for the inaugural NYC European Tech meetup on August 27, 2014. Examines the current state of European startups in New York. Makes the (fairly obvious) case that New York is a very natural landing spot for European tech entrepreneurs.