This document contains information about SQL Server 2016 including that it provides a consistent experience from on-premises to cloud, has in-memory capabilities built-in across all workloads, and can handle real-time and massive scale workloads. It also contains graphics comparing SQL Server's performance to other database management systems and analytics platforms on the TPC-H benchmark and shows SQL Server ranking first or in the top three.
Former Social Media Analyst, future Data Scientist. Passionate about Artificial Intelligence, Machine Learning and Data Science.
Actively finding full time job in these particular areas.
Watch the companion webinar at:http://embt.co/CollabDL
As a data professional, you should be reviewing the enterprise use of common business terms in your organization to assess the breadth of use (and potential misuse) of the enterprise business glossary. While that work by itself does not resolve differences via harmonization or establish standards across the organization, documenting the reference data and data element metadata is the first step in sharing knowledge about business terms and identifying the business impacts of variant use, the numerous places the business terms are interpreted in different processes, and the degradation of corporate value.
In this webinar, David Loshin will discuss the concept of collaboration for establishing a chain of definition that is both transparent to the entire user community yet leverages a virtual approach to “crowdsourcing” metadata standardization that helps to:
+ Map agreed-to definitions to the different uses in the different data sets and applications
+ Enable consistent interpretation of data element meanings from an end-to-end perspective
+ Reduce and eventually eliminate misunderstandings about business term semantics
+ Provide a means for analyzing impacts of evolving or changing definitions
How Big Companies plan to use Our Big Data 201610Mark Tabladillo
Underneath the shiny popular apps on tablets, smartphones, and entertainment channels are typically large cloud-based data centers. App developers leverage the cloud to provide advertisers with targeted sales opportunities, which has been accounting for an ongoing shift from paper to online media. This presentation will provide updated trends and statistics for 2016 on big data usage (based on consumer use), statistical concerns with big data, and the Microsoft big data story.
Delivered to SQL Saturday Columbus, GA
Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.
Microsoft Technologies for Data Science 201612Mark Tabladillo
Delivered to SQL Saturday BI Edition -- Atlanta, GA
Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.
Former Social Media Analyst, future Data Scientist. Passionate about Artificial Intelligence, Machine Learning and Data Science.
Actively finding full time job in these particular areas.
Watch the companion webinar at:http://embt.co/CollabDL
As a data professional, you should be reviewing the enterprise use of common business terms in your organization to assess the breadth of use (and potential misuse) of the enterprise business glossary. While that work by itself does not resolve differences via harmonization or establish standards across the organization, documenting the reference data and data element metadata is the first step in sharing knowledge about business terms and identifying the business impacts of variant use, the numerous places the business terms are interpreted in different processes, and the degradation of corporate value.
In this webinar, David Loshin will discuss the concept of collaboration for establishing a chain of definition that is both transparent to the entire user community yet leverages a virtual approach to “crowdsourcing” metadata standardization that helps to:
+ Map agreed-to definitions to the different uses in the different data sets and applications
+ Enable consistent interpretation of data element meanings from an end-to-end perspective
+ Reduce and eventually eliminate misunderstandings about business term semantics
+ Provide a means for analyzing impacts of evolving or changing definitions
How Big Companies plan to use Our Big Data 201610Mark Tabladillo
Underneath the shiny popular apps on tablets, smartphones, and entertainment channels are typically large cloud-based data centers. App developers leverage the cloud to provide advertisers with targeted sales opportunities, which has been accounting for an ongoing shift from paper to online media. This presentation will provide updated trends and statistics for 2016 on big data usage (based on consumer use), statistical concerns with big data, and the Microsoft big data story.
Delivered to SQL Saturday Columbus, GA
Microsoft provides several technologies which can be used for casual to serious data science. This presentation provides an authoritative overview of two major categories: products and services. The products include: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, SQL Server R Services, Microsoft R Technologies, and F#. The services include Cortana Intelligence and Bing Predicts. These technologies have been used by the presenter in various companies and industries, and he will be speaking toward how Microsoft uses these technologies today for its largest Azure customers.
Microsoft Technologies for Data Science 201612Mark Tabladillo
Delivered to SQL Saturday BI Edition -- Atlanta, GA
Microsoft provides several technologies in and around Azure which can be used for casual to serious data science. This presentation provides an overview of the major Microsoft options for both on-premise and cloud-based data science (and hybrid). These technologies have been used by the presenter in various companies and industries, both as a Microsoft consultant and previously independent consultant. As well, the speaker provides insights into data science careers, information which helps imply where the business will likely be for consultants and partners.
This is a quick overview slides that will give readers an understanding about Microsoft's offering in the Data and Artificial Intelligence space.
For any questions, feel free to schedule a meeting or drop me a line.
Microsoft Power BI is a unified self-service and enterprise business intelligence platform that combines an intuitive user experience with intelligent data visualizations to provide greater depth of data insight. Reports can be shared within Microsoft tools like Teams, SharePoint, PowerPoint, or within other productivity products.
Power BI Overview e la soluzione SCA per gli AteneiJürgen Ambrosi
Presentazione delle potenzialità di PowerBI e demo di creazione di un Report e Dashboard.
SCA (Università degli Studi di Roma “Tor Vergata”) è la soluzione per le Università in grado di fornire un unico punto di accesso alle informazioni degli studenti relative a performance, carriere e amministrazione, dando facile accesso a risultati di potenti query per prendere rapidamente decisioni
Pysyvästi laadukasta masterdataa SmartMDM:n avullaBilot
1.9.2016 aamiaistilaisuuden esitys.
Mitäpä jos valjastaisit koko organisaatio masterdatan ylläpitoon? Hallitsisit hajauttamalla? Uudistunut SmartMDM tuo käyttöösi hallinnan, Microsoft SQL Server Master Data Services (MDS) keskityksen.
Lisää tapahtumiamme sivustollamme: http://www.bilot.fi/en/events/
"Elastic enables the world’s leading organization to exceed their business objectives and power their mission-critical systems by eliminating data silos, connecting the dots, and transforming data of all types into actionable insights.
Come learn how the power of search can help you quickly surface relevant insights at scale. Whether you are an executive looking to reduce operational costs, a department head striving to do more with fewer tools, or engineer monitoring and protecting your IT environment, this session is for you. "
An overview of the new features available in SQL Server 2016 including Stretch Database, Always Encrypted, Data Masking, In Memory Operational Analytics and more.
How to find low-cost or free data science resources 202006Mark Tabladillo
There are many free or low-cost resources to become better trained in data science. None of these options equals a formal degree: but short of that scope, these other resources are helpful at least for keeping up with technology. This presentation will provide specific recommendations on free or low-cost resources based on the Team Data Science Process framework (business understanding, data engineering, modeling, deployment).
This presentation covers some of the major data science and AI announcements from the May 2020 Microsoft Build conference. Included in this talk are 1) Azure Synapse Link, 2) Responsible AI, 3) Project Bonsai & Project Moab, and 4) AI Models at Scale (deep learning with billions of parameters).
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This is a quick overview slides that will give readers an understanding about Microsoft's offering in the Data and Artificial Intelligence space.
For any questions, feel free to schedule a meeting or drop me a line.
Microsoft Power BI is a unified self-service and enterprise business intelligence platform that combines an intuitive user experience with intelligent data visualizations to provide greater depth of data insight. Reports can be shared within Microsoft tools like Teams, SharePoint, PowerPoint, or within other productivity products.
Power BI Overview e la soluzione SCA per gli AteneiJürgen Ambrosi
Presentazione delle potenzialità di PowerBI e demo di creazione di un Report e Dashboard.
SCA (Università degli Studi di Roma “Tor Vergata”) è la soluzione per le Università in grado di fornire un unico punto di accesso alle informazioni degli studenti relative a performance, carriere e amministrazione, dando facile accesso a risultati di potenti query per prendere rapidamente decisioni
Pysyvästi laadukasta masterdataa SmartMDM:n avullaBilot
1.9.2016 aamiaistilaisuuden esitys.
Mitäpä jos valjastaisit koko organisaatio masterdatan ylläpitoon? Hallitsisit hajauttamalla? Uudistunut SmartMDM tuo käyttöösi hallinnan, Microsoft SQL Server Master Data Services (MDS) keskityksen.
Lisää tapahtumiamme sivustollamme: http://www.bilot.fi/en/events/
"Elastic enables the world’s leading organization to exceed their business objectives and power their mission-critical systems by eliminating data silos, connecting the dots, and transforming data of all types into actionable insights.
Come learn how the power of search can help you quickly surface relevant insights at scale. Whether you are an executive looking to reduce operational costs, a department head striving to do more with fewer tools, or engineer monitoring and protecting your IT environment, this session is for you. "
An overview of the new features available in SQL Server 2016 including Stretch Database, Always Encrypted, Data Masking, In Memory Operational Analytics and more.
How to find low-cost or free data science resources 202006Mark Tabladillo
There are many free or low-cost resources to become better trained in data science. None of these options equals a formal degree: but short of that scope, these other resources are helpful at least for keeping up with technology. This presentation will provide specific recommendations on free or low-cost resources based on the Team Data Science Process framework (business understanding, data engineering, modeling, deployment).
This presentation covers some of the major data science and AI announcements from the May 2020 Microsoft Build conference. Included in this talk are 1) Azure Synapse Link, 2) Responsible AI, 3) Project Bonsai & Project Moab, and 4) AI Models at Scale (deep learning with billions of parameters).
Microsoft has released Automated ML technologies for developers through ML.NET, Azure ML Service, and Azure Databricks. This presenter is a data scientist and Microsoft architect, and will give a comprehensive overview of the utility and use case of this automated technology for production solutions. The presentation includes code you can try now.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
This presentation provides an overview of the technology with demos run in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and F# and run in Visual Studio Community 2019. This technology is ready for production implementation and runs on .NET Core.
This presentation is the first of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
NimbusML enables data scientists to use ML.NET to train models in Azure Machine Learning or anywhere else they use Python. NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. NimbusML is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
The trained machine learning model can be used in a .NET application with ML.NET. This presentation will outline the features of NimbusML and provide a notebook-based demonstration using Azure Notebooks.
This presentation is the third of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
201905 Azure Certification DP-100: Designing and Implementing a Data Science ...Mark Tabladillo
Microsoft has several Azure certifications including DP-100 (Designing and Implementing a Data Science Solution on Azure). Until this month, the exam had been in beta: however, the presenter has just passed the exam (first try). The purpose of this event is to share a viewpoint on how to study for the exam. Today, there are multiple ways to develop and deliver and deploy R or Python or Spark or deep learning models on Azure. The differences are important for this exam.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
This presentation anchors best practices for Enterprise Data Science based on Microsoft's "Team Data Science Process". The talk includes introducing the concepts, describing some real-world advice for project planning, and discusses typical titles of professionals who make enterprise data science successful. These techniques also apply for AI (artificial intelligence), deep learning, machine learning, and advanced analytics.
Training of Python scikit-learn models on AzureMark Tabladillo
This intermediate-level presentation covers latest Azure technology for deploying Python sci-kit models on Azure. The presentation is a demo using a Microsoft Data Science Virtual Machine (DSVM), Visual Studio Code, Azure Machine Learning Service, Azure Machine Learning Compute, Azure Storage Blobs, and Azure Container Registry to train a model from a Python 3 Anaconda environment.
The presentation will include an architectural diagram and downloadable code from Github.
YouTube recording at https://www.youtube.com/watch?v=HyzbxHBpAbg&feature=youtu.be
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Power BI has become an increasingly important data analytics tool. This presentation focuses on the advanced analytics options currently available in Power BI. Attendees to this talk will see:
· Microsoft’s perspective on advanced analytics development: the Team Data Science Process
· What the general options are for advanced analytics on Azure
· What the specific native advanced analytics capabilities are in Power BI
· Some ideas on pairing Power BI with other technologies in advanced analytics architectures
Microsoft Cognitive Toolkit (Atlanta Code Camp 2017)Mark Tabladillo
The Microsoft Cognitive Toolkit (CNTK) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs.
The objectives of this presentation is to 1) describe what CNTK is, 2) present a comparative evaluation with similar technologies, 3) outline potential applications, and 4) demonstrate the technology with Jupyter Python examples.
Machine learning services with SQL Server 2017Mark Tabladillo
SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
Window functions are powerful analytic functions built into SQL Server. SQL Server 2005 introduced the core window ranking functions, and SQL Server 2012 added time and statistical percentage window functions. These functions allow for advanced variable creation, and are of direct benefit to people creating features for data science. This talk will also recommend further reading on this topic. The slide deck contains a link to the code on GitHub.
Microsoft Technologies for Data Science 201601Mark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries.
Microsoft Data Science Technologies: Back Office EditionMark Tabladillo
Microsoft provides several technologies in and around SQL Server which can be used for casual to serious data science. This presentation provides an authoritative overview of five major options: SQL Server Analysis Services, Excel Add-in for SSAS, Semantic Search, Microsoft Azure Machine Learning, and F#. Also included are tips on working with Python and R. These technologies have been used by the presenter in various companies and industries. This presentation will emphasize the back office story for supporting big data processing.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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/
16. SQL Server 2016: Everything Built-in
The above graphics were published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. Gartner does not endorse any vendor, product or service depicted in its research
publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties,
expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particularpurpose.
Consistent experience from on-premises to cloud
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TPC-H non-clustered results as of 04/06/15, 5/04/15, 4/15/14 and 11/25/13, respectively. http://www.tpc.org/tpch/results/tpch_perf_results.asp?resulttype=noncluster
Real time and at massive scale
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