Patrick Sheehan of Microsoft covers platform architecture, data warehousing methodology, and multi-dimensional cube development.
You will learn:
* How to develop and deploy data cubes using SQL Server Analysis Services (SSAS)
* Optimal data warehouse methodology for use with SSAS
* Tips/tricks for designing & building cubes over no warehouse/suboptimal source system (it happens)
* Cube processing types - How/why to use each
* Cube design practices + How to build and deploy cubes!
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
QuerySurge - the automated Data Testing solutionRTTS
QuerySurge is the leading Data Testing solution built specifically to automate the testing of Data Warehouses & Big Data. QuerySurge ensures that the data extracted from data sources remains intact in the target data store by analyzing and pinpointing any differences quickly.
And QuerySurge makes it easy for both novice and experienced team members to validate their organization's data quickly through Query Wizards while still allowing power users the flexibility they need.
All with deep dive reporting and data health dashboards that quickly provides you with a holistic view of your project’s data.
Types of Automated Data Testing
--------------------------------------------
QuerySurge provides data testing solutions for all of your automated data testing needs
- Data Warehouse testing & ETL testing
- Big Data (Hadoop, NoSQL) testing
- Data Interface testing
- Data Migration testing
- Database Upgrade testing
FREE TRIAL
www.QuerySurge.com
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
A Fortune 100 company recently introduced Hadoop into their data warehouse environment and ETL workflow to save $30 Million. This session examines the specific use case to illustrate the design considerations, as well as the economics behind ETL offload with Hadoop. Additional information about how the Hadoop platform was leveraged to support extended analytics will also be referenced.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Power BI has in its DNA the goal of enabling everybody to experience their data any way, anywhere—in seconds and at global scale.
Power BI offers a set of capabilities that are uniquely enabled by its global and cloud nature:
The ability to harness data from Excel spreadsheets, on-premises data sources through the data gateway, big data, streaming data, and cloud services. It doesn’t matter what type of data you want or where it lives, Power BI allows you to connect to hundreds of data sources.
Out-of-the box SaaS content packs that deliver a curated experience with pre-built dashboards to get you up and running quickly. We have hundreds of ISVs building content packs to cater to the needs of millions of Power BI users.
Unmatched, unique ways for users to experience their data with speed and agility:
Live dashboards that maintain a real-time pulse on the business and provide critical insights.
Natural language query that enables users to simply and intuitively ask questions of their data, including through Cortana.
Custom visuals that bring data to life and surface intelligence hidden in the sea of data, with our community leveraging the Power BI visualization stack to create new ways to visualize data in a way that makes more sense. (Now available in the Office store.)
Integration of Power BI with the Microsoft stack. Power BI is part of larger ecosystem that integrates with services like Microsoft Teams, Office 365, and Dynamics 365. These services are aware of Power BI, are wired to Power BI, and enable you to use Power BI in the context of your work.
Anywhere access to insights. Whether in the office or on-the-go, Power BI provides anywhere access to insights with dashboards accessible via the desktop, on the web, or across mobile devices. Inside Excel, embedded—we have hundreds of ISVs embedding Power BI in their offerings.
Building a Data Science as a Service Platform in Azure with DatabricksDatabricks
Machine learning in the enterprise is rarely delivered by a single team. In order to enable Machine Learning across an organisation you need to target a variety of different skills, processes, technologies, and maturities. To do this is incredibly hard and requires a composite of different techniques to deliver a single platform which empowers all users to build and deploy machine learning models.
In this session we discuss how Azure & Databricks enables a Data Science as a Service platform. We look at how a DSaaS platform is empowering users of all abilities to build models, deploy models and enabling organisations to realise and return on investment earlier.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Creating a Tabular Model Using SQL Server 2012 Analysis ServicesCode Mastery
At Code Mastery Boston Steve Hughes, Principal Consultant at Magenic, highlights: Basics of SQL Server 2012 Analysis Services, Multidimensional Model, VS PowerPivot, Creating a Tabular Model
QuerySurge - the automated Data Testing solutionRTTS
QuerySurge is the leading Data Testing solution built specifically to automate the testing of Data Warehouses & Big Data. QuerySurge ensures that the data extracted from data sources remains intact in the target data store by analyzing and pinpointing any differences quickly.
And QuerySurge makes it easy for both novice and experienced team members to validate their organization's data quickly through Query Wizards while still allowing power users the flexibility they need.
All with deep dive reporting and data health dashboards that quickly provides you with a holistic view of your project’s data.
Types of Automated Data Testing
--------------------------------------------
QuerySurge provides data testing solutions for all of your automated data testing needs
- Data Warehouse testing & ETL testing
- Big Data (Hadoop, NoSQL) testing
- Data Interface testing
- Data Migration testing
- Database Upgrade testing
FREE TRIAL
www.QuerySurge.com
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
A Fortune 100 company recently introduced Hadoop into their data warehouse environment and ETL workflow to save $30 Million. This session examines the specific use case to illustrate the design considerations, as well as the economics behind ETL offload with Hadoop. Additional information about how the Hadoop platform was leveraged to support extended analytics will also be referenced.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Power BI has in its DNA the goal of enabling everybody to experience their data any way, anywhere—in seconds and at global scale.
Power BI offers a set of capabilities that are uniquely enabled by its global and cloud nature:
The ability to harness data from Excel spreadsheets, on-premises data sources through the data gateway, big data, streaming data, and cloud services. It doesn’t matter what type of data you want or where it lives, Power BI allows you to connect to hundreds of data sources.
Out-of-the box SaaS content packs that deliver a curated experience with pre-built dashboards to get you up and running quickly. We have hundreds of ISVs building content packs to cater to the needs of millions of Power BI users.
Unmatched, unique ways for users to experience their data with speed and agility:
Live dashboards that maintain a real-time pulse on the business and provide critical insights.
Natural language query that enables users to simply and intuitively ask questions of their data, including through Cortana.
Custom visuals that bring data to life and surface intelligence hidden in the sea of data, with our community leveraging the Power BI visualization stack to create new ways to visualize data in a way that makes more sense. (Now available in the Office store.)
Integration of Power BI with the Microsoft stack. Power BI is part of larger ecosystem that integrates with services like Microsoft Teams, Office 365, and Dynamics 365. These services are aware of Power BI, are wired to Power BI, and enable you to use Power BI in the context of your work.
Anywhere access to insights. Whether in the office or on-the-go, Power BI provides anywhere access to insights with dashboards accessible via the desktop, on the web, or across mobile devices. Inside Excel, embedded—we have hundreds of ISVs embedding Power BI in their offerings.
Building a Data Science as a Service Platform in Azure with DatabricksDatabricks
Machine learning in the enterprise is rarely delivered by a single team. In order to enable Machine Learning across an organisation you need to target a variety of different skills, processes, technologies, and maturities. To do this is incredibly hard and requires a composite of different techniques to deliver a single platform which empowers all users to build and deploy machine learning models.
In this session we discuss how Azure & Databricks enables a Data Science as a Service platform. We look at how a DSaaS platform is empowering users of all abilities to build models, deploy models and enabling organisations to realise and return on investment earlier.
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
All projects need documentation and many companies provide templates as part of a methodology. This document describes the templates, tools and source documents used by Data Management & Warehousing. It serves two purposes:
• For projects using other methodologies or creating their own set of documents to use as a checklist. This allows the project to ensure that the documentation covers the essential areas for describing the data warehouse.
• To demonstrate our approach to our clients by describing the templates and deliverables that are produced.
Documentation, methodologies and templates are inherently both incomplete and flexible. Projects may wish to add, change, remove or ignore any part of any document. Some may also believe that aspects of one document would sit better in another. If this is the case then users of this document and these templates are encouraged to change them to fit their needs.
Data Management & Warehousing believes that the approach or methodology for building a data warehouse should be to use a series of guides and checklists. This ensures that small teams of relatively skilled resources developing the system can cover all aspects of the project whilst being free to deal with the specific issues of their environment to deliver exceptional solutions, rather than a rigid methodology that ensures that large teams of relatively unskilled staff can meet a minimum standard.
Creating a Tabular Model Using SQL Server 2012 Analysis ServicesCode Mastery
At Code Mastery Boston Steve Hughes, Principal Consultant at Magenic, highlights: Basics of SQL Server 2012 Analysis Services, Multidimensional Model, VS PowerPivot, Creating a Tabular Model
Microsoft SSAS: Should I Use Tabular or Multidimensional?Senturus
Learn the right version Microsoft SQL Server Analysis services to use to easily migrate the work to the other version. View the webinar video recording and download this deck: http://www.senturus.com/resources/microsoft-ssas/.
During this webinar, Senturus discussed how to choose between the tabular and multi-dimensional versions of SSAS for your analytic needs and the various features and benefits that each version provides.
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/.
SSAS, MDX , Cube understanding, Browsing and Tools information Vishal Pawar
Why we need SSAS Cube
What is SSAS Cube
Way to access Cube
What is Dimension and Attributes
QHP Dimension and Attributes
Process Flow and QHP Cube Browsing
MDX Basics
MDX Tools
Comparison of Queries Written in T-SQL and MDX with Construct
MDX –How to add where condition
Developing with SQL Server Analysis Services 201310Mark Tabladillo
SQL Server Analysis Services (SSAS) allows for integrating cubes, tabular model databases, and data mining with your developed applications and services. This talk provides a developer’s framework for understanding SSAS and its core ADOMD .NET and AMO classes. These development options will be demonstrated through application demos. This session is great for developers already working with SQL Server, who want to take their development skills to the next level.
Many People know FastTrack as a reference architecture for relational databases. The goal of this guideline is to provide a reference architecture for scalable and fast Analysis Services solutions.
SQL PASS 2017 - Building one million predictions per second using SQL Server ...Amit Banerjee
Using the power of OLTP and data transformation in SQL 2016 and advanced analytics in Microsoft R Server, various industries that really push the boundary of processing higher number of transaction per second (tps) for different use cases. In this talk, we will walk through the use case of predicting loan charge off (loan default) rate, architecture configuration that enable this use case, and rich visual dashboard that allow customer to do what-if analysis. Attend this session to find out how SQL + R allows you to build an “intelligent data warehouse”.
This is a talk which builds on my previous talk on how SQL Server 2016 helps build an intelligent data warehouse.
Why you should be mining your data and how to actually do it. Every company needs a rock star. We want it to be you. This session will give real world examples of data mining successes as well as walk you through how to get started down the path of data enlightenment, so that you too can say "I Am A Data Miner℠".
24 Hours of PASS -- Enterprise Data Mining with SQL ServerMark Tabladillo
This presentation introduces SQL Server Data Mining (SSDM) for SQL Server Professionals based on the speaker's past presentation for Microsoft TechEd. Starting with SQL Server Management Studio (SSMS), the demo includes the interfaces important for professional development, including Business Intelligence Development Studio (BIDS), highlighting Integration Services, and PowerShell. The interactive demos are based on Microsoft's Contoso Retail sample data. Finally we will evaluate where Microsoft data mining can help you in a practical business environment, which may include Oracle and SAS.
CTE Ottawa Seminar Day - September 7th, 2012
This clinic introduces the key features and enhancements in SQL Server 2012. It is designed to provide a high-level overview of the product and the key new capabilities in this release.
This course is intended for technology managers and database professionals who want to understand the key capabilities of SQL Server 2012. In many cases, it is assumed that senior technical managers may attend this clinic in order to assess the further training that their technology-focused employees will need in order to adopt SQL Server 2012.
Msbi Online Training is Offering at Glory IT Technologies. We have Certified Working Professionals on this Modules. They trained so many Global Students. We also Provides Corporate Training, Job/Project Support Services to msbi . We are Only Institute Delivering Best Online Training Services to this Module.
BI Team @ LinkedIn hosted a user group meeting for MicroStrategy customers in bay area. Presentation includes information about LinkedIn, concepts of metadata driven model for business dashboards, customizations using SDK, JSP and JQUERY.
A Complete BI Solution in About an Hour!Aaron King
In this presentation Aaron will cover how to collect data from multiple sources using SQL Server 2012 Integration Services (SSIS). Then he will use SQL Server Reporting Services (SSRS) to report detail on that data. After that he will use SQL Server Analysis Services (SSAS) to create a KPI. Finally he’ll present that KPI on a dashboard via a web page. The goal of this presentation is to show how seamless the Microsoft Business Intelligence products are. If you’ve only used a few of these products, you’ll appreciate seeing them together all at once. Code will be provided.
Angel Abundez of DesignMind explains how to build and automate data sets and data models in Excel using the Power BI toolset. You'll see how to pull data from a variety of on-premise and cloud data sources to familiarize yourself with the latest capabilities of Power Query and Power Pivot. Then you'll learn about the software required to automate your Power BI analysis whether you are trying to refresh your Excel workbooks on a file server, in SharePoint Online, or SharePoint. on-premise.
Platfora - An Analytics Sandbox In A World Of Big DataMark Ginnebaugh
As Big Data becomes the norm in dealing with data volume, variety, and velocity, it becomes increasingly harder for the Data Analyst to understand and work with data sets. To overcome this we introduce Platfora, a Hadoop backed data analysis framework which nicely complements more traditional data warehousing and BI solutions. This presentation covers ingestion of new data and building of data sets and visualizations,in a system that requires no more work than interacting with a graphical interface. You'll see examples of peer-to-peer lending and how insights on loan applicants and their risk profiles can be quickly revealed with no ETL development or demanding data transformation.
Microsoft SQL Server Relational Databases and Primary KeysMark Ginnebaugh
SQL Server guru Ami Levin explains some of the fundamental design principles of relational databases: normalization rules, key selection, and the controversies associated with these issues from a practical perspective.
This presentation hits on the benefits and challenges of using different types of keys - natural, surrogates, artificial, and others.
Each key offers benefits from multiple aspects: data consistency, application development, maintenance, portability and performance.
Ami Levin is a Microsoft MVP and a consultant with SolidQ. Last fall he moved to California from Israel, where he led the Israeli SQL Server User Group.
DesignMind Microsoft Business Intelligence SQL ServerMark Ginnebaugh
DesignMind is a custom software firm in San Francisco specializing in SQL Server, SharePoint, .NET, and Microsoft Business Intelligence.
We're a Microsoft Certified Partner with expertise in Business Intelligence, Data Platform, Portals and Collaboration, and Custom Development. Our Business Intelligence team specializes in Enterprise Data Warehouse, Data Mart, Mobile Business Intelligence, and Self-Service BI.
San Francisco Bay Area SQL Server July 2013 meetingsMark Ginnebaugh
San Francisco Bay Area July 2013 Microsoft SQL Server and Business Intelligence meetings.
Learn more:
www.meetup.com/The-San-Francisco-SQL-Server-Meetup-Group
www.meetup.com/The-SiliconValley-SQL-Server-User-Group
www.meetup.com/San-Francisco-Bay-Area-Microsoft-BI-User-Group
Presenter: Ernest Hwang of Practice Fusion > This presentation shows how to simplify your database deployments, ensure that no database changes are overlooked, and implement unit tests using the suite of Red Gate developer tools.
You'll see how Practice Fusion streamlines database deployments in their Integration, Testing, Staging, and Production environments. This frees developers from the burden of maintaining deployment scripts, while reducing the number of overlooked breaking changes to zero.
The demo uses a Windows Azure box as the Jenkins (Continuous Integration) server and several SQL Azure databases (representing Integration and QA environments). The entire repository is hosted on GitHub (https://github.com/CF9/Databases.RGDemo), for anyone to download.
You'll learn how to:
* Add your database to source control in under five minutes
* Create a CI Job to validate your database “build”
* Deploy database changes to your environments with a mouse click
* Set up database unit testing using tSQLt
* Avoid problems when implementing Database CI in the “real-world”
Ernest Hwang is a Principal Software Engineer at Practice Fusion in San Francisco. He uses Red Gate SQL Source Control, SQL Compare, SQL Data Compare, and SQL Test to automate Practice Fusion's Continuous Integration efforts and instrument database deployments.
Presenter: Ofer Mendelevitch of Hortonworks > Learn the benefits of big data for data scientists, and how Hadoop and HDInsight fit into the modern data architecture and enable data-driven products.
You'll learn:
* What data science actually means
* The term "data products"
* The benefits of using big data for data scientists
* How Hadoop helps data scientists work with big data
* About HDInsight, the big data platform from Microsoft and Hortonworks
SQL Server implements three different physical operators to perform joins. In this presentation you'll see how each of these operators work plus its advantages and challenges.
You'll learn:
* The logic behind the optimizer's decisions
* Which operator to use for various joins using (semi) real life examples
* How to avoid common join-related pitfalls
Ami Levin is a Microsoft SQL Server MVP and a Mentor with SolidQ. For the past 14 years, he has been consulting, teaching, writing, and speaking about SQL Server worldwide.
Levin’s areas of expertise are data modeling, database design, T-SQL and performance tuning.
Before moving to California, he led the Israeli SQL Server user group (ISUG) and moderated the Hebrew MSDN SQL Server support forum. Ami is a regular speaker at Microsoft Tech-Ed Israel, Dev Academy, and other SQL Server conferences. He blogs at SQL Server Tuning Blog.
Microsoft PowerPivot & Power View in Excel 2013Mark Ginnebaugh
PowerPivot is an add-in for Excel that empowers business users to create their own tabular data models. Power View is also available in the Excel 2013 client. It was first released as a server-based report authoring tool with SQL Server 2012 and is available in SharePoint Server 2010 Enterprise.
You'll learn:
* How to work with the add-in in the Excel 2013 client
* How compelling interactive reports can be created quickly and easily
* The new PowerPivot features - including pie charts, maps, KPIs, hierarchies, drill down/drill up, and report styles
Peter Myers specializes in Microsoft Business Intelligence, and provides mentoring, technical training and course content authoring for SQL Server and Office. Peter has current SQL Server and MCT certifications, and has been a Microsoft MVP (Most Valued Professional) since 2007.
Microsoft Data Warehouse Business Intelligence Lifecycle - The Kimball ApproachMark Ginnebaugh
Data Warehouse - Business Intelligence Lifecycle Overview by Warren Thronthwaite
This slide deck describes the Kimball approach from the best-selling Data Warehouse Toolkit, 2nd Edition. It was presented to the Bay Area Microsoft Business Intelligence User Group in October 2012.
Starting with business requirements and project definition, the lifecycle branches out into three tracks: Technical, Data and Applications. You will learn:
* The major steps in the Lifecycle and what needs to happen in each one.
* Why business requirements are so important and how they influence all major decisions across the entire DW/BI system.
* Key tools for prioritizing business requirements and creating an enterprise information framework.
* How to break up a DW/BI system into doable increments that add real business value and can be completed in a reasonable time frame.
Fusion-io Memory Flash for Microsoft SQL Server 2012Mark Ginnebaugh
You've heard about Solid State Drives (SSDs), and might be using them now. To get dramatically improved IO performance, you need Flash Memory – storage that can be connected to your server’s Bus, and really maximize IO.
Fusion-io is an industry leader in this area, and Sumeet Bansal explains how to best employ this powerful technology. You'll learn:
* The many ways Flash can help your SQL Server performance, while at the same time lowering costs
* How you can use Flash optimally for your SQL Server deployment
* Easy, low risk ways to introduce ioMemory into SQL Server environments to instantly realize significant benefits.
* How to implement ioMemory optimally for the most pervasive configurations of SQL Server
Author: William Brown, Microsoft BI Specialist > This slide presentation covers Microsoft Data Mining functionality from the developer to the end user. In the past, data mining belonged to the deep technical specialist, but the current Microsoft stack allows anyone to create very powerful data mining models. Data mining allows users to find insights that are difficult or impossible to discover with traditional analysis.
You'll learn
* How to get started with Data mining
* The various data mining models and where they can be applied
* How to create models and surface the data to users
* How to use the new Excel Data mining add-in
This presentation lists upcoming events and summer 2012 virtual chapter meetings of the Professional Association for SQL Server. You will find meetings about data warehousing, Big Data, Master Data, Powershell, and virtualization.
Learn more about PASS at www.sqlpass.org
Business Intelligence Dashboard Design Best PracticesMark Ginnebaugh
Microsoft BI expert Dan Bulos spoke on Dashboard Design Best Practices to the Bay Area Microsoft Business Intelligence User Group.
This presentation shows techniques for displaying data in a dashboard for maximum impact. Dan also discusses various tools available in the Microsoft BI stack – Reporting Services, Excel, PerformancePoint and the new entry, Power View.
Take a look at Mobile BI on iPad, Windows Phone, SQL Server Reporting Services, and SharePoint with emphasis on data visualization best practices. Angel Abundez explains how design approaches change when launching mission-critical dashboards and reports on smaller screen sizes using touch-screen technology.
Presenter Angel Abundez is a Business Intelligence consultant with DesignMind in San Francisco. He focuses on Business Intelligence, Visualization, and improving business processes using Microsoft SQL Server, SharePoint, and ASP.NET. He also works with the new visualizations coming out with PowerPivot, Power View, and SharePoint. Angel is Co-Lead of the Bay Area Business Intelligence User Group and is an active speaker in the SQL Server community.
SQL Server 2012 is the most crucial release of SQL Server to-date. In this slideshow, you'll see how SQL Server 2012 supports mission critical applications 24x7 and gives significant insight into business operations. Presented by Subhash Jawahrani of Microsoft to the Silicon Valley SQL Server User Group in March 2012.
You'll learn about:
* Mission Critical Apps
* New Business Intelligence features
* Improving business agility with Cloud computing
Microsoft SQL Server 2012 Master Data ServicesMark Ginnebaugh
Author: Mark Gschwind, DesignMind
San Francisco, California
Master Data Services had a major upgrade in the SQL Server 2012 release. BI Consultant Mark Gschwind takes you through the new Excel interface, the new Silverlight look and feel, and integration improvements.
Knowing how to use this tool can be a valuable addition to your repertoire as a BI professional, allowing you to address data quality and other challenges.
Mark will show how to create a model, add columns and rows, manage security, and create hierarchies. He demos the new Excel interface and discuss how to allow you to manage master data yourself. He'll touch on how to integrate with a DW, migrating from Dev to Production.
You'll learn:
* How to let users manage dimensions and hierarchies for your DW
* How to create workflows to improve data quality in your DW
* Tips from real-life implementations to help you achieve a successful implementation
Mark Gschwind, Partner at DesignMind, is an expert on data warehousing, OLAP, and ERP migration. He has authored three enterprise data warehouses and over 80 OLAP cubes for 46 clients in a wide range of industries. Mark has certifications in SQL Server and Oracle Essbase.
This slideshow is for IT professionals, data analysts, managers, and anyone looking to drive more productivity from Excel. You will learn how you can effectively leverage the add-ins with your own data and analysis requirements.
One of the pillars of the SQL Server 2008 R2 release is Managed Self-Service BI.
Peter Myers of SolidQ will introduce:
* SQL Server PowerPivot for Excel
* SQL Server PowerPivot for SharePoint
The SQL Server PowerPivot for Excel add-in is a key offering in this pillar, and delivers an entirely new analytic experience to Excel 2010. This add-in allows analysts to load and prepare large volumes of data from various sources to create a multidimensional model. The model can be enriched with sophisticated calculations. Then the model can then be used as the source for PivotTable and PivotChart reports.
With the SQL Server PowerPivot for SharePoint add-in, the Excel workbooks that host the PowerPivot model can be cataloged in SharePoint and exposed as a data source for other Excel and Reporting Services reports. These SharePoint hosted models can then be managed by IT with scheduled data refreshes from the originating data stores.
Creator: Joel Champagne, President of CodeX Enterprises
This presentation covers various issues associated with SQL unit testing. We’ll look at end solutions in demo form using Visual Studio 2010 and other third party tools.
You'll learn:
* The value of pursuing SQL testing, early and continually in the development cycle
* Capabilities in Visual Studio 2010 to support SQL unit testing
* Capabilities in other tools to support SQL unit testing
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction
1.
2. Silicon Valley SQL Server User Group
Tonight’s Agenda May 20, 2014
6:30 – 7:00 pm Food and Networking
7:00 – 7:10 pm Introductions
7:10 – 8:10 pm Patrick Sheehan, Microsoft
8:10 – 8:30 pm Discussion (Q&A)
3. Parent Organizations
Bay Area Association of Database Developers
BAADD www.baadd.org
Professional Association of SQL Server
PASS www.sqlpass.org
5. Upcoming Meetings
San Francisco – Thurs, June 5
Dimensional Modeling, The Basics &
Beyond
Dan Bulos, Symmetry
San Francisco – Wed, June 11
SQL Server Implementation
Frameworks From Dev to Prod
Prakash Heda, Advent Software
7. Patrick Sheehan
Data Platform Architect at Microsoft’s Silicon Valley
Technology Center (SVC MTC)
Previously with Microsoft Consulting Services (MCS)
as an engineer, architect, and enterprise strategist
Specialties:
SQL Server
Business Intelligence
Big Data
Enterprise & Solutions Architecture
Cloud Computing
9. What is Analysis Services?
• Online Analytical Processing (OLAP) engine
• Designed for data analysis, mining, and reporting
• Contains Databases
• Contains Cubes
• Cubes are multi-dimensional data sets
• Sourced from Facts
• Tables which contain measures
• …and Dimensions
• Tables which contain attributes
Reference: http://technet.microsoft.com/en-us/library/bb522625.aspx
10. Use Cases
• Data abstraction layer
• Ad hoc reporting
• Data mining
• Data warehousing
• Kimball Methodology recommended
16. Set up environment
• Move .mdf to SQL Server Data directory (e.g. C:Program FilesMicrosoft SQL
ServerMSSQL11.SQL2012MSSQLDATA)
• Attach .mdf to instance
• NOTE: if error is received, it is due to missing log file. Click on the .ldf file
listed in the Attach Database dialogue box, then click Remove (a new log file will
automatically be created in the default path)
• Extract .sln from .zip
• Open solution and reset connection strings to local db instance
17. Solution structure
• Data Sources node
• Connection strings to source
• Data Source Views node
• XML abstraction layer
• Cubes node
• Define dim/fact relationships, aggregations (calculations), partitions, mode
• Dimensions node
• Contains dimensions
• Roles node
• Define access to cube
18. Cube fundamentals
• Measure groups
• Contain measures
• Set properties (e.g. formatting)
• Dimensions
• Contain attributes
• Set properties (e.g. visibility)
• Define relationships (to key & each other)
• Define hierarchies
• Deployment
• Processing
• Affects of FULL versus
19. Multi-dimensonal Expression Language (MDX)
Example
Syntax
[ WITH <SELECT WITH clause> [ , <SELECT WITH clause> ... ] ]
SELECT [ * | ( <SELECT query axis clause>
[ , <SELECT query axis clause> ... ] ) ]
FROM <SELECT subcube clause>
[ <SELECT slicer axis clause> ]
[ <SELECT cell property list clause> ]
SELECT NON EMPTY { [Measures].[Reseller Order Quantity],
[Measures].[Reseller Gross Profit], [Measures].[Reseller Sales
Amount] } ON COLUMNS, NON EMPTY { ([Product].[Product
Categories].[Product].ALLMEMBERS *
[Date].[Fiscal].[Date].ALLMEMBERS ) } DIMENSION PROPERTIES
MEMBER_CAPTION, MEMBER_UNIQUE_NAME ON ROWS
FROM [Adventure Works] CELL PROPERTIES VALUE,
BACK_COLOR, FORE_COLOR, FORMATTED_VALUE,
FORMAT_STRING, FONT_NAME, FONT_SIZE, FONT_FLAGS
Reference:
http://technet.microsoft.com/en-us/library/ms145595.aspx
20. MDX “Heads up”
• MDX my look like T-SQL, but do not be fooled! It isn’t!
• MDX is a different way to think about data
• MDX is designed to provide tabular output from multidimensional sources
Reference:
http://msdn.microsoft.com/en-us/library/ms145506.aspx
Books:
MDX Solutions: With Microsoft SQL Server Analysis Services 2005 and
Hyperion Essbase – George Spofford (&others)
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
– Ralph Kimball
21. Working with Cubes
• Microsoft Excel is #1 app (>1billion worldwide deployments)
• Native integration with SSAS
• SQL Server Reporting Services (SSRS)
• Native integration with SSAS
• Multidimensional Programming (see: http://technet.microsoft.com/en-us/library/bb500153.aspx)
• ADOMD.NET
• .NET provider for communication with SSAS (via XMLA)
• Analysis Services Scripting Language (ASSL)
• Extends XMLA
• Analysis Management Objects (AMO)
• SSAS management classes