The document is a business intelligence portfolio for Chris Bull containing examples of his skills and experience in areas such as data modeling, SQL programming, SQL Server Integration Services, SQL Server Analysis Services, MDX programming, SQL Server Reporting Services, PerformancePoint Server, and SharePoint Server. It includes samples of his work developing ETL processes, cubes, reports, and other BI solutions. It also provides a summary of his 14 years of IT experience and 2 recommendations from academic references.
2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologi...Jürgen Ambrosi
L’obiettivo è quello di fare una panoramica dello stato dell’arte sulle tecnologie a supporto dei database. Alcuni esempi sono la tecnologia in-memory integrata con le funzionalità di analisi operative in tempo reale e della tecnologia Always Encrypted per la protezione dei dati utilizzati in locale o durante gli spostamenti. La tecnologia in-memory consente di migliorare di 30 volte le performance delle transazioni utilizzando hardware standard di settore. Inoltre i Big Data e l'analisi sono diventati un importante fattore di differenziazione competitivo, ma la gestione delle enormi quantità di dati correlate a un tempo di attività 24 ore su 24 continua a essere una sfida per l'IT. Oggi è più importante che mai soddisfare a livello aziendale l'esigenza di prestazioni, disponibilità e sicurezza efficace per gestire carichi di lavoro mission-critical a un costo contenuto. Le soluzioni Microsoft fissano un nuovo standard nelle performance mission-critical.
Desk reference for data transformation in Stata. Co-authored with Tim Essam (@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03.
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologi...Jürgen Ambrosi
L’obiettivo è quello di fare una panoramica dello stato dell’arte sulle tecnologie a supporto dei database. Alcuni esempi sono la tecnologia in-memory integrata con le funzionalità di analisi operative in tempo reale e della tecnologia Always Encrypted per la protezione dei dati utilizzati in locale o durante gli spostamenti. La tecnologia in-memory consente di migliorare di 30 volte le performance delle transazioni utilizzando hardware standard di settore. Inoltre i Big Data e l'analisi sono diventati un importante fattore di differenziazione competitivo, ma la gestione delle enormi quantità di dati correlate a un tempo di attività 24 ore su 24 continua a essere una sfida per l'IT. Oggi è più importante che mai soddisfare a livello aziendale l'esigenza di prestazioni, disponibilità e sicurezza efficace per gestire carichi di lavoro mission-critical a un costo contenuto. Le soluzioni Microsoft fissano un nuovo standard nelle performance mission-critical.
Desk reference for data transformation in Stata. Co-authored with Tim Essam (@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03.
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
Manufacturer of Commercial Chairs, Meeting Room Chairs & Executive Series Chairs offered by Divine Chairs Pvt. Ltd. from Navi Mumbai, Maharashtra, India.
Online Customer Order Booking Portal (eCommerce Solution)Mayank Chanlawala
Application suggest vendor product like if we consider Tata steel then this portal will display all the product and it’s descriptions. Once the user select product and it’s his requirement then system will start searching available inventory from all service center , vendor , all plants. If system finds required material it will suggest the free material and for remaining material suggest lead time(delivery time).
Manufacturer of Commercial Chairs, Meeting Room Chairs & Executive Series Chairs offered by Divine Chairs Pvt. Ltd. from Navi Mumbai, Maharashtra, India.
Online Customer Order Booking Portal (eCommerce Solution)Mayank Chanlawala
Application suggest vendor product like if we consider Tata steel then this portal will display all the product and it’s descriptions. Once the user select product and it’s his requirement then system will start searching available inventory from all service center , vendor , all plants. If system finds required material it will suggest the free material and for remaining material suggest lead time(delivery time).
Migrating on premises workload to azure sql databasePARIKSHIT SAVJANI
Azure SQL Database is a fully managed cloud database service with built-in intelligence, elastic scale, performance, reliability, and data protection that enables enterprises and ISVs to reduce their total cost of ownership and operational cost and overheads. In this session, I will share real-world experience of successfully migrated existing SaaS application and on-premises workload for some our tier 1 customers and ISV partners to Azure SQL Database service. The session walks through planning, assessment, migration tools and best practices from the proven experiences and practices of migrating real world applications to Azure SQL Database service.
SQL Server 2008
In this event we'll take a look at what's coming down the line for database developers with SQL Server 2008. We'll look at changes to the core engine and the T-SQL language, any changes in the toolset and we'll also take a good look at what's coming with ADO.NET V3.0 in terms of the new Entity Framework and the new Microsoft Synchronisation Framework. If you're a SQL Server developer come along and see what's in store for 2008.
For more details and the original slidedeck visit http://www.microsoft.com/uk/msdn/events/new/Detail.aspx?id=322
Running Intelligent Applications inside a Database: Deep Learning with Python...Miguel González-Fierro
In this talk we present a new paradigm of computation where the intelligence is computed inside the database. Standard software systems must get the data from the database to execute a routine. If the size of the data is big, there are inefficiencies due to the data movement. Store procedures tried to solve this issue in the past, allowing for computing simple functions inside the database. However, only simple routines can be executed.
To showcase the capabilities of our new system, we created a lung cancer detection algorithm using Microsoft’s Cognitive Toolkit, also known as CNTK. We used transfer learning between ImageNet dataset, which contains natural images, and a lung cancer dataset, which contains scans of horizontal sections of the lung for healthy and sick patients. Specifically, a pretrained Convolutional Neural Network on ImageNet is used on the lung cancer dataset to generate features. Once the features are computed, a boosted tree is applied to predict whether the patient has cancer or not.
All this process is computed inside the database, so the data movement is minimized. We are even able to execute the algorithm using the GPU of the virtual machine that hosts the database. Using a GPU, we can compute the featurization in less than 1h, in contrast to using a CPU, that would take up to 32h. Finally, we set up an API to connect the solution to a web app, where a doctor can analyze the images and get a prediction of a patient.
A talk given by Julian Hyde at DataCouncil SF on April 18, 2019
How do you organize your data so that your users get the right answers at the right time? That question is a pretty good definition of data engineering — but it is also describes the purpose of every DBMS (database management system). And it’s not a coincidence that these are so similar.
This talk looks at the patterns that reoccur throughout data management — such as caching, partitioning, sorting, and derived data sets. As the speaker is the author of Apache Calcite, we first look at these patterns through the lens of Relational Algebra and DBMS architecture. But then we apply these patterns to the modern data pipeline, ETL and analytics. As a case study, we look at how Looker’s “derived tables” blur the line between ETL and caching, and leverage the power of cloud databases.