Raghu Ramakrishnan. Trends and challenges in big data analytics, and an outline of the Microsoft big data stack. Go to https://channel9.msdn.com/ to find the recording of this session.
Azure Synapse is Microsoft's new cloud analytics service offering that combines enterprise data warehouse and Big Data analytics capabilities. It offers a powerful and streamlined platform to facilitate the process of consolidating, storing, curating and analysing your data to generate reliable and actionable business insights.
Azure Synapse is Microsoft's new cloud analytics service offering that combines enterprise data warehouse and Big Data analytics capabilities. It offers a powerful and streamlined platform to facilitate the process of consolidating, storing, curating and analysing your data to generate reliable and actionable business insights.
Explore Your Data Using Amazon QuickSight and Build Your First Machine Learni...Amazon Web Services
In this session we will demonstrate how non-experts in machine learning, can easily analyze their data with QuickSight and build scalable and production-ready predictive models with Amazon machine learning. After the session you will have a good understanding how to define problems from your business, in terms of data and predictive models, and you will be able to apply analytics and machine learning concepts as a competitive advantage.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
With Azure Data Lake Store, analyze all of your data in one place with no artificial constraints. Data Lake Store can store trillions of files.
Azure Data Lake Analytics: Easily develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, and .NET over petabytes of data. With no infrastructure to manage, you can process data on demand, scale instantly, and only pay per job.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
Data Analytics Week at the San Francisco Loft
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Big Data Day LA 2016/ NoSQL track - Architecting Real Life IoT Architecture, ...Data Con LA
Learn how to benefit from IoT (internet of things) to reduce costs and spur transformation for your company and clients. Attendees will learn about building blocks to create an IoT solution, and walk through real life architectural decisions in building a solution.
Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
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.
Cortana Analytics Workshop: Azure Data LakeMSAdvAnalytics
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
Explore Your Data Using Amazon QuickSight and Build Your First Machine Learni...Amazon Web Services
In this session we will demonstrate how non-experts in machine learning, can easily analyze their data with QuickSight and build scalable and production-ready predictive models with Amazon machine learning. After the session you will have a good understanding how to define problems from your business, in terms of data and predictive models, and you will be able to apply analytics and machine learning concepts as a competitive advantage.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
With Azure Data Lake Store, analyze all of your data in one place with no artificial constraints. Data Lake Store can store trillions of files.
Azure Data Lake Analytics: Easily develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, and .NET over petabytes of data. With no infrastructure to manage, you can process data on demand, scale instantly, and only pay per job.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
Data Analytics Week at the San Francisco Loft
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Big Data Day LA 2016/ NoSQL track - Architecting Real Life IoT Architecture, ...Data Con LA
Learn how to benefit from IoT (internet of things) to reduce costs and spur transformation for your company and clients. Attendees will learn about building blocks to create an IoT solution, and walk through real life architectural decisions in building a solution.
Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
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.
Cortana Analytics Workshop: Azure Data LakeMSAdvAnalytics
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Azure Data Platform Services
HDInsight Clusters in Azure
Data Storage: Apache Hive, Apache Hbase, Azure Data Catalog
Data Transformations: Apache Storm, Apache Spark, Azure Data Factory
Healthcare / Life Sciences Use Cases
Build 2017 - P4010 - A lap around Azure HDInsight and Cosmos DB Open Source A...Windows Developer
Recently, we released the Spark Connector for our distributed NoSQL service – Azure Cosmos DB (formerly known as Azure DocumentDB). By connecting Apache Spark running on top Azure HDInsight to Azure Cosmos DB, you can accelerate your ability to solve fast-moving data science problems and machine learning. The Spark to Azure Cosmos DB connector efficiently exploits the native Cosmos DB managed indexes and enables updateable columns when performing analytics, push-down predicate filtering against fast-changing globally-distributed data, ranging from IoT, data science, and analytics scenarios. Come learn how you can perform blazing fast planet-scale data processing with Azure Cosmos DB and HDInsight.
Building a scalable analytics environment to support diverse workloadsAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Building a scalable analytics environment to support diverse workloads
Tom Panozzo, Chief Technology Officer (Aunalytics)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Big Data Expo 2015 - Microsoft Transform you data into intelligent actionBigDataExpo
Er zijn veel beloftes rondom Big Data. Iedereen praat erover maar hoe begin je zonder meteen een grote business case op te moeten stellen. Cortana Analytics Suite is laagdrempelig en een makkelijk toegankelijk Advanced Analytics platform om je ideeën op haalbaarheid te testen maar daarna ook door te groeien naar (grote) productie implementaties. In deze sessie krijg je een overzicht van de scenario’s die Cortana Analytics biedt. Denk daar bij aan IOT, Machine Learning maar ook Churn Analysis, Forecasting en Predictive Maintenance.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Similar to Cortana Analytics Workshop: Big Data @ Microsoft (20)
Cortana Analytics Workshop: Predictive Maintenance in the IoT EraMSAdvAnalytics
Danielle Dean. Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Data-driven predictive maintenance, in particular, is gaining increasing attention in the industry along with the emerging demand of the Internet of Things (IoT) applications and the maturity of the supporting technologies. In this session we will present a real-world predictive maintenance example where the problem is formulated into three related questions via different machine learning models. A demonstration of how data flows through an end-to end-system, from ingesting the data to aggregating in real time to predicting based on historical data, will be done using tools such as Azure Machine Learning, Azure Stream Analytics, and Power BI. These technologies allow companies such as ThyssenKrupp Elevator to go from reactive to proactive and even predictive analysis of maintenance problems. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Cortana Analytics for RetailMSAdvAnalytics
Xueshan Zhang. For retailers, price and demand are strongly correlated and are at the core of any successful strategy. When pursuing a revenue or profit maximization strategy, setting the right price level to the right products allows accurate prediction of future demand. In the session we will explore how Cortana Analytics Suite supports producing price elasticity curves, predicting demand for a given price point, and forecasting the demand for a product over time. We will also demonstrate how to perform these tasks on an ongoing basis so that pricing strategies are based on fresh data and kept up to date. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Cortana Analytics for MarketingMSAdvAnalytics
Tao Wu. Microsoft has helped many customers like Mendeley realize the full potential of marketing using cloud-based machine learning. In this session, we will present how Cortana Analytics brings key capabilities to next-generation marketing. Examples include: (1) Accurate lead scoring that helps substantially improve effectiveness of marketing and sales; (2) Early identification of core users that allows brands to engage with users better; and (3) Real-time marketing that enables personalized and contextualized marketing decisions. With Cortana Analytics, you can quickly deploy marketing solutions, gain user insights, and engage with customers with improved accuracy and efficiency, all backed by Azure's scale and elasticity. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Real-Time Data Processing -- How Do I Choose the ...MSAdvAnalytics
Benjamin Wright-Jones, Simon Lidberg. Are you interested in near real-time data processing but confused about Azure capabilities and product positioning? Spark, StreamInsight, Storm (HDInsight) and Stream Analytics offer ways to ingest data but there is uncertainty about when and how we should use these capabilities. For example, what are the differences and key solution design decision points? Come to this session to learn about current and new near real-time data processing engines. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Operationalizing Your End-to-End Analytics SolutionMSAdvAnalytics
Wee Hyong Tok. With Azure Data Factory (ADF), existing data movement and analytics processing services can be composed into data pipelines that are highly available and managed in the cloud. In this demo-driven session, you learn by example how to build, operationalize, and manage scalable analytics pipelines. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Azure Data CatalogMSAdvAnalytics
Julie Strauss. This session introduces the newest services in the Cortana Analytics family. The Azure Data Catalog is an enterprise-wide metadata catalog that enables self-service data source discovery. Data Catalog is a fully managed service that stores, describes, indexes, and provides information on how to access any registered data source in your organization. This session presents an overview of the Data Catalog and how – by using it to register, enrich, discover, understand and consume data sources – you can close the gap between those seeking information and those creating it.
Cortana Analytics Workshop: Connecting Cortana Analytics Faster -- Any Source...MSAdvAnalytics
Craig Stewart. Cortana Analytics will deliver the most meaningful insights when structured and unstructured data from across the enterprise can be accessed, integrated, and delivered effectively. With 300+ pre-built connectors and native Hadoop processing, SnapLogic's modern integration platform as a service (iPaaS) is built for today's hybrid data architecture. The SnapLogic Elastic Integration Platform will support the range of Cortana Analytics from Azure Blob to HDInsight and Power BI with powerful streaming data ingestion, self-service data preparation, and batch and real-time data delivery. This session will review how SnapLogic's data integration platform allows enterprise customers to leverage the full power Microsoft's latest BI technology to the max. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Real-World Data Collection for Cortana AnalyticsMSAdvAnalytics
Spyros Sakellariadis, Joshua Peschel. To collect the right data for a complex machine learning experiment might take months, as you plan out choices and placements of sensors, data schemas, data logging intervals, and so on to get optimal data sets for running analytical models that produce the insights you need. In this session we'll cover the planning architecture and end-to-end experiments for measuring and analyzing hydrologic data (e.g., soil moisture) for two ongoing operations in the greater Chicagoland area: (1) determining water balances for agricultural lands; and (2) predicting and preventing urban flooding. This talk will elucidate the complexities and provide recommendations and best practices for working with environmental data in the wild. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Insights and Predictions -- Integrating and Deplo...MSAdvAnalytics
Jeremy Reynolds.
This technical talk will describe how you can leverage Revolution R Enterprise to model Big Data and then leverage a new R package (AzureML) in order to deploy a production-level web service for scoring and consumption. Go to https://channel9.msdn.com/ to find the recording of this session.
Eric Golpe. Security, privacy, and compliance concerns can be significant hurdles to cloud adoption. Azure can help customers move to the cloud with confidence by providing a trusted foundation, demonstrating compliance with security standards, and making strong commitments to safeguard the privacy of customer data. This presentation will educate you in the fundamentals of Azure security as they pertain to the Cortana Analytics Suite, including capabilities in place for threat defense, network security, access control, and data protection as well as data privacy and compliance. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Developing for Power BIMSAdvAnalytics
Faisal Mohamood. Power BI is leading a market change to SaaS BI. Come learn how Power BI's APIs and Developers will transform how Enterprises and ISVs will go to market in the coming year. You'll get to see great demos showing what's possible, and you'll get a sneak peak of all of our upcoming capabilities. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Milliman Integrate for Cortana AnalyticsMSAdvAnalytics
Paul Maher. Milliman Integrate is a SaaS offering from Milliman for the life insurance market to democratize actuarial modeling and financial reporting. Actuaries use mathematics, statistics, financial models, and simulations on a customer's profile to evaluate short- and long-term risks. The goal of Milliman Integrate is to allow actuaries to focus on business processes and analysis rather than complex infrastructure details. Come join this session to learn how Milliman is using Cortana Analytics and Azure to power Milliman Integrate, which reimagines the relationships between people, process, and technology to manage risk and maximize efficiencies. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Intelligent Retail -- The Machine Learning ApproachMSAdvAnalytics
Luis Cabrera. What if you could predict which products your customers are likely to purchase? What if you knew which of your customers are not happy with your service? What if you knew which of those customers are not going to come back? Machine learning and Advanced Analytics can help solve these and many other problems in retail. Come learn about many of our turn-key solutions, including Customer Churn Prediction, Recommendations, and Text Analytics. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Using the Cortana Analytics ProcessMSAdvAnalytics
Sachin Chouksey, Wee Hyong Tok. In this talk, we will describe a cloud-centric data science process to go from raw data on premise or in the cloud to operationalized machine learning models using only a browser and elastic resources in Azure using various tools like SQL Server on Azure, Azure VMs, Azure Machine Learning, and Azure HDInsight (Hadoop). Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Building Next-Generation Smart GridsMSAdvAnalytics
Yijing Chen, Davide Roverso. Over the past few years, the areas of IoT, alternative energy sources, and big data have emerged to create vast opportunities within the utility/energy domain. Also, with demand flattening out, these sectors are now forced to reinvent themselves, hence utility and energy companies have started pioneering "smart grid" solutions. In this session, we will discuss a customer use case through a demo that illustrates an end-to-end (E2E) energy forecasting solution. This session will delve into the details of the underlying model development and deployment in the cloud using Azure Machine Learning, Azure Data Factory, and Azure Stream Analytics, as well as Power BI for visualization. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: Deep Neural NetworksMSAdvAnalytics
Misha Bilenko, Alexey Kamenev, Ye Xing. This talk provides an overview of deep learning, a powerful family of models and algorithms that construct meaningful representations from low-level features, yielding state-of-the-art performance for many prediction tasks in vision and speech. Following a refresher of machine learning and neural network basics, we will go through a series of fun yet instructive demonstrations of deep learning on vision tasks, and showcase how it can be used in Cortana Analytics. Go to https://channel9.msdn.com/ to find the recording of this session.
Cortana Analytics Workshop: AI -- Assistive IntelligenceMSAdvAnalytics
Marcus Ash. The early lessons Microsoft learned in designing and developing Cortana, a personal digital assistant for Windows, and future thoughts on the coming golden age of AI: Assistive Intelligence. Go to https://channel9.msdn.com/ to find the recording of this session.
James Phillips. In this session learn how Microsoft is re-inventing Business Intelligence (BI) with Microsoft Power BI, a rapidly growing business analytics service with groundbreaking data analysis and visualization capabilities. Go to https://channel9.msdn.com/ to find the recording of this session.
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.
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).
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:
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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.
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.
5. Data Storage Scarcity Data Storage Abundance
Operational Data Operational and Observational Data
Highly Modeled Schema Flexible storage, Exploratory Analysis
Reporting Insight, Predictions, Actions
6. Store any data
relations
Do any analysis
SQL queries
Hive,
At any speed
Batch
Hive
At any scale
Elastic
Big Data
7. Capture any data, react
instantaneously, mix with data
stored anywhere
Tiered storage management
Federated access within and
across clouds
Use any analysis tool (anywhere,
mix and match, interactively)
Shared compute fabric
Extensible suite of tools
Tiered Analytics Store
Business
Intelligence
Machine
Learning
Stream
Analytics
Batch
SQL / Hive /MR
Other
Stores
Data Aware
Compute Fabric
DATA INGEST
8. Cloud first
All data accessible
Choice & open standards (HDFS, YARN)
Storage and processing scale independently
Secure and compliant
Simple to use, productive from Day 1
9. Cosmos – Exascale Big Data (Internal to Microsoft)
Azure Data Lake – Managed store for analytics
Azure HDInsight – Managed Hadoop Clusters
Azure SQL DW – Managed Relational Warehouse
10. Windows
SMSG
Live
Ads
CRM/Dynamics
Windows Phone
Xbox Live
Office365
STB Malware Protection
Microsoft Stores
STBCommerceRisk
Messenger
LCA
Exchange
Yammer
Skype
Bing
data managed: EBs
cluster sizes: 10s of Ks
# machines: 100s of Ks
daily I/O: >100 PBs
# internal developers: 1000s
# daily jobs: 100s of Ks
11. Fully managed cloud data store designed for analytics
Supports HDFS compliant analytics applications and tools
Enterprise grade security, compliance & management
Petabyte files, unlimited account size
High throughput for analytics performance
Low latency ingestion with read as you write
AAD-based authentication, access auditing
File and folder-level ACLs
Encryption at rest
12. • 100% open source Apache
Hadoop
• Full Hadoop ecosystem as a
managed service, supported
and backed by Microsoft on
• Linux
• Windows
• Harness .Net or Java to write
customer extensions
• Supports broad ecosystem of
ISVs (Hadoop and Traditional)
14. Fully managed relational data warehouse-as-a-service
First elastic cloud data warehouse with proven SQL Server capabilities
Support your smallest to your largest data storage needs
Scales to petabytes of data
Massively Parallel Processing
Instant-on compute scales in seconds
Query Relational / Non-Relational
Saas
Azure
Public
Cloud
Office 365Office 365
Get started in minutes
Integrated with Azure ML, PowerBI & ADF
Simple billing compute & storage
Pay for what you need, when you
need it with dynamic pause
AzureAzure
16. Capture any data, react
instantaneously, mix with data
stored anywhere
Tiered storage management
Federated access within and
across clouds
Use any analysis tool (anywhere,
mix and match, interactively)
Shared compute fabric
Extensible suite of tools
Tiered Analytics Store
Business
Intelligence
Machine
Learning
Stream
Analytics
Batch
SQL / Hive /MR
Other
Stores
Data Aware
Compute Fabric
DATA INGEST