These slides gives an overview on MS Azure Data Architecture and Services, including Data Lake Analytics, Data Factory, Azure SQL DW, Stream Analytics, Azure Machine learning tools, and Data Catalog. This is also known as Cortana Analytical Suite
Operational Machine Learning: Using Microsoft Technologies for Applied Data S...Khalid Salama
Sqlbits 2017 session - Data Science concerns with the activities of processing and analysing data in a particular domain, as well as applying machine learning algorithms to automatically discover insights and interesting patterns from the data. While data scientist needs tools to explore and visualize data, along with performing machine learning experiments and evaluating candidate models, operational platforms are required to productionize and maintain the resultant models, and integrate them into the operational systems. In this session, we explore the main features and capabilities of various Microsoft technologies that enable such end-to-end data science exercise, including SQL Server and Azure PaaS Services, along with practical scenarios and demos.
These slides gives an overview of NoSQL in the context of Big Data processing. We start by defining SQL vs NoSQL concepts, the CAP theorem, and why NoSQL technologies are needed. Then we discuss the various NoSQL technology breeds, including Key/Value stores, Document stores, Column Family (wide-column) stores, memory cache stores, and graph stores, along with related tools and examples. After that we present various solution architecture patterns, in which NoSQL data stores play viable roles. Next we delve into Microsoft Azure implementation of some of these NoSQL technologies, including Redis Cache, Azure Table Storage, HBase on HDInsight, and Azure DocumentDB. Finally, we conclude with some useful resource, before we give a sneak peek on how to use neo4j for Graph Processing.
Microsoft R enable enterprise-wide, scalable experimental data science and operational machine learning, by providing a collection of servers and tools that extend the capabilities of open-source R In these slides, we give a quick introduction to Microsoft R Server architecture, and a comprehensive overview of ScaleR, the core libraries to Microsoft R, that enables parallel execution and use external data frames (xdfs). A tutorial-like presentation covering how to: 1) setup the environments, 2) read data, 3) process & transform, 4) analyse, summarize, visualize, 5) learn & predict, and finally 6) deploy and consume (using msrdeploy).
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Real-Time Event & Stream Processing on MS AzureKhalid Salama
These slides discuss the main concepts of event & stream processing, as well as the related technologies on Microsoft Azure. We start by giving and overview of what Event & Stream Processing is. Then we describe the canonical architecture of a Stream Processing solution. We will delve into Message Queuing part of the solution. After that, we Introduce Apache Storm on HDInsight, as well as Azure Stream Analytics. We compare Apache Storm to Azure Stream Analytics, and finally conclude with useful resources
The Future of Data Warehousing, Data Science and Machine LearningModusOptimum
Watch the on-demand recording here:
https://event.on24.com/wcc/r/1632072/803744C924E8BFD688BD117C6B4B949B
Evolution of Big Data and the Role of Analytics | Hybrid Data Management
IBM, Driving the future Hybrid Data Warehouse with IBM Integrated Analytics System.
Operational Machine Learning: Using Microsoft Technologies for Applied Data S...Khalid Salama
Sqlbits 2017 session - Data Science concerns with the activities of processing and analysing data in a particular domain, as well as applying machine learning algorithms to automatically discover insights and interesting patterns from the data. While data scientist needs tools to explore and visualize data, along with performing machine learning experiments and evaluating candidate models, operational platforms are required to productionize and maintain the resultant models, and integrate them into the operational systems. In this session, we explore the main features and capabilities of various Microsoft technologies that enable such end-to-end data science exercise, including SQL Server and Azure PaaS Services, along with practical scenarios and demos.
These slides gives an overview of NoSQL in the context of Big Data processing. We start by defining SQL vs NoSQL concepts, the CAP theorem, and why NoSQL technologies are needed. Then we discuss the various NoSQL technology breeds, including Key/Value stores, Document stores, Column Family (wide-column) stores, memory cache stores, and graph stores, along with related tools and examples. After that we present various solution architecture patterns, in which NoSQL data stores play viable roles. Next we delve into Microsoft Azure implementation of some of these NoSQL technologies, including Redis Cache, Azure Table Storage, HBase on HDInsight, and Azure DocumentDB. Finally, we conclude with some useful resource, before we give a sneak peek on how to use neo4j for Graph Processing.
Microsoft R enable enterprise-wide, scalable experimental data science and operational machine learning, by providing a collection of servers and tools that extend the capabilities of open-source R In these slides, we give a quick introduction to Microsoft R Server architecture, and a comprehensive overview of ScaleR, the core libraries to Microsoft R, that enables parallel execution and use external data frames (xdfs). A tutorial-like presentation covering how to: 1) setup the environments, 2) read data, 3) process & transform, 4) analyse, summarize, visualize, 5) learn & predict, and finally 6) deploy and consume (using msrdeploy).
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Real-Time Event & Stream Processing on MS AzureKhalid Salama
These slides discuss the main concepts of event & stream processing, as well as the related technologies on Microsoft Azure. We start by giving and overview of what Event & Stream Processing is. Then we describe the canonical architecture of a Stream Processing solution. We will delve into Message Queuing part of the solution. After that, we Introduce Apache Storm on HDInsight, as well as Azure Stream Analytics. We compare Apache Storm to Azure Stream Analytics, and finally conclude with useful resources
The Future of Data Warehousing, Data Science and Machine LearningModusOptimum
Watch the on-demand recording here:
https://event.on24.com/wcc/r/1632072/803744C924E8BFD688BD117C6B4B949B
Evolution of Big Data and the Role of Analytics | Hybrid Data Management
IBM, Driving the future Hybrid Data Warehouse with IBM Integrated Analytics System.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Cloud Innovation Day - Commonwealth of PA v11.3Eric Rice
Enhance and accelerate your path to digital innovation and transformation with IBM Cloud. Develop a roadmap to get started with cloud and incorporate best practices from other organizations just like yours.
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.
Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production ?
In this session learn the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of DSX with HDP with the focus on integration, security and model deployment and management.
Speakers:
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Vikram Murali, Program Director, Data Science and Machine Learning, IBM
How Apache Spark and Apache Hadoop are being used to keep banking regulators ...DataWorks Summit
The global financial crisis showed that traditional IT systems at banks were ill equiped to monitor and manage the daily-changing risk landscape during the global financial crisis. The sheer amount of data that needed to be crunched meant that many of the banks were day(s) behind in calculating, understanding and reporting their risk positions. Post crisis, a review by banking regulator, led the regulators to introduce a new legislation BCBS 239: Principles for effective risk data aggregation and reporting, that requires banks to meet more stringent (timeliness) requirement, in their ability to aggregate and report on their quickly-changing risk positions or risk fines to the tune of $millions. To meet these new requirements, banks have been forced to re-think their traditional IT architectures, which are unable to cope with sheer volume of risk data, and are instead turning to Apache Hadoop and Apache Spark to build out next generation of risk systems. In this talk you will discover, how some of the leading banks in the world are leveraging Apache Hadoop and Apache Spark to meet BCBS 239 regulation.
Speaker
Kunal Taneja
Top Trends in Building Data Lakes for Machine Learning and AI Holden Ackerman
Presentation by Ashish Thusoo, Co-Founder & CEO at Qubole, on exploring the big data industry trends in moving from data warehouses to cloud-based data lakes.This presentation will cover how companies today are seeing a significant rise in the success of their big data projects by moving to the cloud to iteratively build more cost-effective data pipelines and new products with ML and AI.
Uncovering how services like AWS, Google, Oracle, and Microsoft Azure provide the storage and compute infrastructure to build self-service data platforms that can enable all teams and new products to scale iteratively.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
Journey to the Data Lake: How Progressive Paved a Faster, Smoother Path to In...DataWorks Summit
Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.
Multi-tenant Hadoop - the challenge of maintaining high SLASDataWorks Summit
In shared configuration, the same Hadoop environment supports many applications. Each has
specific requirements and criticality (SLA). Yet they all rely on an assembly of shared application
bricks.
At the same time, the life cycle of a cluster is not static in time. It evolves horizontally, with the
arrival of new applications, but also vertically, as the applications grow in load or evolve in
terms of functionality.
With this in mind, a multi-tenant production cluster presents several challenges including and
not limited to:
- Maintain a high level of SLA for a set of use cases with heterogeneous needs
- Plan and implement the architecture evolution of a cluster in production to ensure the
maintenance of SLA throughout the integration of new use cases on it
EDF will present how it manages this heterogeneity of SLA inherent of any Big Data cluster. EDF
is focusing on how it is renovating its cluster, its organization, its processes and its approach in
order to deliver a platform with strong SLA throughout its life cycle.
Speaker
Edouard Rousseaux, Tech Lead, EDF
Oracle PL/SQL 12c and 18c New Features + RADstack + Community SitesSteven Feuerstein
Slides presented at moug.org's August 2018 conference. Covers the RADstack (REST - APEX - Database) + our community sites (AskTOM, LiveSQL and Dev Gym) + a whole bunch of cool new PL/SQL features. Search LiveSQL.oracle.com for scripts to match up with the features presented.
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
How to deploy SQL Server on an Microsoft Azure virtual machinesSolarWinds
Running apps on Microsoft Azure Virtual Machines is tempting; promising faster deployments and lower overall TCO. But how easy is it really to configure and run SQL Server in an Azure VM environment? Learn what you should know about tuning, optimizing, and key indicators for monitoring performance, as well as special considerations for High-Availability and Disaster Recovery.
SQL Saturday #313 Rheinland - MapReduce in der PraxisSascha Dittmann
Das Programmiermodel MapReduce, welches vor einigen Jahren von Google veröffentlicht wurde, hat Einzug in zahlreiche Systeme erhalten. Dabei wurde es sowohl als eigenständiges System, wie beispielweise bei Hadoop, Disco oder Amazon Elastic MapReduce, aber auch als Abfragesprache innerhalb größerer Systeme, wie beispielweise bei MongoDB, Greenplum DB oder Aster Data, implementiert. Diese Session stellt gängige Problemstellungen aus der Praxis vor und wie diese mit dem MapReduce Framework von Microsoft HDInsight umgesetzt werden können.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Cloud Innovation Day - Commonwealth of PA v11.3Eric Rice
Enhance and accelerate your path to digital innovation and transformation with IBM Cloud. Develop a roadmap to get started with cloud and incorporate best practices from other organizations just like yours.
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.
Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production ?
In this session learn the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of DSX with HDP with the focus on integration, security and model deployment and management.
Speakers:
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Vikram Murali, Program Director, Data Science and Machine Learning, IBM
How Apache Spark and Apache Hadoop are being used to keep banking regulators ...DataWorks Summit
The global financial crisis showed that traditional IT systems at banks were ill equiped to monitor and manage the daily-changing risk landscape during the global financial crisis. The sheer amount of data that needed to be crunched meant that many of the banks were day(s) behind in calculating, understanding and reporting their risk positions. Post crisis, a review by banking regulator, led the regulators to introduce a new legislation BCBS 239: Principles for effective risk data aggregation and reporting, that requires banks to meet more stringent (timeliness) requirement, in their ability to aggregate and report on their quickly-changing risk positions or risk fines to the tune of $millions. To meet these new requirements, banks have been forced to re-think their traditional IT architectures, which are unable to cope with sheer volume of risk data, and are instead turning to Apache Hadoop and Apache Spark to build out next generation of risk systems. In this talk you will discover, how some of the leading banks in the world are leveraging Apache Hadoop and Apache Spark to meet BCBS 239 regulation.
Speaker
Kunal Taneja
Top Trends in Building Data Lakes for Machine Learning and AI Holden Ackerman
Presentation by Ashish Thusoo, Co-Founder & CEO at Qubole, on exploring the big data industry trends in moving from data warehouses to cloud-based data lakes.This presentation will cover how companies today are seeing a significant rise in the success of their big data projects by moving to the cloud to iteratively build more cost-effective data pipelines and new products with ML and AI.
Uncovering how services like AWS, Google, Oracle, and Microsoft Azure provide the storage and compute infrastructure to build self-service data platforms that can enable all teams and new products to scale iteratively.
Azure Data Factory | Moving On-Premise Data to Azure Cloud | Microsoft Azure ...Edureka!
** Microsoft Azure Certification Training : https://www.edureka.co/microsoft-azure-training **
This Edureka "Azure Data Factory” tutorial will give you a thorough and insightful overview of Microsoft Azure Data Factory and help you understand other related terms like Data Lakes and Data Warehousing.
Following are the offering of this tutorial:
1. Why Azure Data Factory?
2. What Is Azure Data Factory?
3. Data Factory Concepts
4. What is Azure Data Lake?
5. Data Lake Concepts
6. Data Lake Vs Data Warehouse
7. Demo- Moving On-Premise Data To Cloud
Check out our Playlists: https://goo.gl/A1CJjM
Journey to the Data Lake: How Progressive Paved a Faster, Smoother Path to In...DataWorks Summit
Progressive Insurance is well known for its innovative use of data to better serve its customers, and the important role that Hortonworks Data Platform has played in that transformation. However, as with most things worth doing, the path to the Data Lake was not without its challenges. In this session, I’ll share our top use cases for Hadoop – including telematics and display ads, how a skills shortage turned supporting these applications into a nightmare, and how – and why – we now use Syncsort DMX-h to accelerate enterprise adoption by making it quick and easy (or faster and easier) to populate the data lake – and keep it up to date – with data from across the enterprise. I’ll discuss the different approaches we tried, the benefits of using a tool vs. open source, and how we created our Hadoop Ingestor app using Syncsort DMX-h.
Multi-tenant Hadoop - the challenge of maintaining high SLASDataWorks Summit
In shared configuration, the same Hadoop environment supports many applications. Each has
specific requirements and criticality (SLA). Yet they all rely on an assembly of shared application
bricks.
At the same time, the life cycle of a cluster is not static in time. It evolves horizontally, with the
arrival of new applications, but also vertically, as the applications grow in load or evolve in
terms of functionality.
With this in mind, a multi-tenant production cluster presents several challenges including and
not limited to:
- Maintain a high level of SLA for a set of use cases with heterogeneous needs
- Plan and implement the architecture evolution of a cluster in production to ensure the
maintenance of SLA throughout the integration of new use cases on it
EDF will present how it manages this heterogeneity of SLA inherent of any Big Data cluster. EDF
is focusing on how it is renovating its cluster, its organization, its processes and its approach in
order to deliver a platform with strong SLA throughout its life cycle.
Speaker
Edouard Rousseaux, Tech Lead, EDF
Oracle PL/SQL 12c and 18c New Features + RADstack + Community SitesSteven Feuerstein
Slides presented at moug.org's August 2018 conference. Covers the RADstack (REST - APEX - Database) + our community sites (AskTOM, LiveSQL and Dev Gym) + a whole bunch of cool new PL/SQL features. Search LiveSQL.oracle.com for scripts to match up with the features presented.
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
How to deploy SQL Server on an Microsoft Azure virtual machinesSolarWinds
Running apps on Microsoft Azure Virtual Machines is tempting; promising faster deployments and lower overall TCO. But how easy is it really to configure and run SQL Server in an Azure VM environment? Learn what you should know about tuning, optimizing, and key indicators for monitoring performance, as well as special considerations for High-Availability and Disaster Recovery.
SQL Saturday #313 Rheinland - MapReduce in der PraxisSascha Dittmann
Das Programmiermodel MapReduce, welches vor einigen Jahren von Google veröffentlicht wurde, hat Einzug in zahlreiche Systeme erhalten. Dabei wurde es sowohl als eigenständiges System, wie beispielweise bei Hadoop, Disco oder Amazon Elastic MapReduce, aber auch als Abfragesprache innerhalb größerer Systeme, wie beispielweise bei MongoDB, Greenplum DB oder Aster Data, implementiert. Diese Session stellt gängige Problemstellungen aus der Praxis vor und wie diese mit dem MapReduce Framework von Microsoft HDInsight umgesetzt werden können.
[JSS2015] Azure SQL Data Warehouse - Azure Data LakeGUSS
• Présentation du service MPP dans le Cloud SQL Data Warehouse : DWU, Polybase, ...
• Présentation des nouveaux services Big Data dans Azure : Data Lake Store, Data Lake Analytics Service (U-SQL)
• Plein de démos :-)"
En lugar de aprovisionar grandes recursos para tu DW, Azure ofrece una versión especial de SQL Server como DataWarehouse. Si está familiarizado con el appliance APS, SQLDW en Azure viene a ser su versión como servicio. Usted crea su DW desde el portal de Azure y ya puede empezar a cargar datos y explotarlos. En esta sesión veremos cómo habilitar el servicio y cómo empezar a explotar SQLDW como tu DW en la nube.
Finally, you can use elastic, relational, and data warehouse in the same sentence. Azure SQL Data Warehouse is a scale out database service designed to answer your ad hoc queries across petabyte scale data-sets through massively parallel processing. See how you can optimize costs by independently scaling compute and storage resources in seconds.
Presented in DDD Melbourne on on Sat Aug 8th 2015
Himanshu Desai, Ahmed El-Harouny & Daniel Janczak
DocumentDB, Mongo or RavenDB? If you are starting out on a new project and considering NoSQL database as an option, which one should you do choose? What if the option you choose today may not work out to be the best one for your needs?
Come and join us for this session, we will take you on a journey where we will explain each of these database on their merits and compare them and also share War stories.
http://dddmelbourne.com
Azure DocumentDB for Healthcare Integration - Part 2BizTalk360
This is the second of a three-part series. The following is the agenda for Part 2 –
Review of DocumentDB REST API
Understanding the overall problem
High-level Design
How Swagger fits in
Design and development
Next steps
BI: new of the buzz words that everyone is talking about but what is it? How can it be used to make a impact in my organization? How do I get started? This session was delivered for SharePoint Saturday Reston.
SQL Server vs. Azure DocumentDB – Ein Battle zwischen XML und JSONSascha Dittmann
Seit dem SQL Server 2000 hielt Stück für Stück die XML-Unterstützung Einzug in die Microsoft RDBMS Welt.
Mit der Azure DocumentDB kam die zweite, hauseigene NoSQL-Datenbank in der Microsoft Cloud hinzu, welche die Daten im JSON-Format verarbeitet.
In dieser Session werden wir anhand eines Praxisbeispiels step-by-step, d.h. von den vorbereitenden Schritten, über das Schreiben bis hin zum Lesen der Daten, diese beiden Technologien gegenüberstellen.
Dabei arbeiten wir die Vor- und Nachteile der einzelnen Ansätze heraus und zeigen Best Practices auf.
The slides give an overview of how Spark can be used to tackle Machine learning tasks, such as classification, regression, clustering, etc., at a Big Data scale.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
SendGrid Improves Email Delivery with Hybrid Data WarehousingAmazon Web Services
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
SendGrid was looking to evolve its data warehouse architecture in order to improve decision making and optimize customer experience. They needed a scalable and reliable architecture that would allow them to move nimbly and efficiently with a relatively small IT organization, while supporting the needs of both business and technical users at SendGrid.
SendGrid’s Director of Enterprise Data Operations will be joining architects from Amazon Web Services (AWS) and Informatica to discuss SendGrid’s journey to a hybrid cloud architecture and how a hybrid data warehousing solution is optimized to support SendGrid’s analytics initiative. Speakers will also review common technologies and use cases being deployed in hybrid cloud today, common data management challenges in hybrid cloud and best practices for addressing these challenges.
Join us to learn:
• How to evolve to a hybrid data warehouse with Amazon Redshift for scalability, agility and cost efficiency with minimal IT resources
• Hybrid cloud data management use cases
• Best practices for addressing hybrid cloud data management challenges
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
We live in a world of unprecedented change. To be successful in this world of change, you will need to develop a data culture, creating an environment where every team and every individual is empowered to do great things because of the data at their fingertips. In this event you will learn how to create a culture of data and how the Microsoft Modern BI platform and tools can help you to can harness the power of data once only reserved for data scientists. Learn about how to tap into the power of natural language, self-service business insights and visualization capabilities – and make insights available to anyone, anywhere, at any time.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Data Integration for Both Self-Service Analytics and IT Users Senturus
See a cloud solution that enables data integration for applications such as Salesforce, NetSuite, Workday, Amazon Redshift and Microsoft Azure. View the webinar video recording and download this deck: http://www.senturus.com/resources/data-integration-tool-for-both-business-and-it-users/.
The rapid growth in self-service business analytics has created tremendous value for organizations, but in many cases has created tension between technical and business users. Technical teams have built solid data warehouses filled with trusted data from source systems such as sales, finance, and operations. Business teams are gaining tremendous insights by analyzing data warehouse information with traditional and new data discovery tools such as Cognos, Business Objects, Tableau, and Power BI.
The Informatica Cloud is a best-of-both-worlds solution that combines data integration for both business and IT users. It allows the following: 1) IT incorporates the business analyst’s data integration routines into the core, trusted data warehouse, 2) Business analysts can do data integration from both cloud-based and on-premise data sources, 3) Business analyst can use the industrial-strength data integration engine that IT teams have loved for years and 4) Integration for apps such as Salesforce, NetSuite, Workday, Amazon Redshift, Microsoft Azure, Marketo, SAP, Oracle and SQL Server.
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/.
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
Introduces the Microsoft’s Data Platform for on premise and cloud. Challenges businesses are facing with data and sources of data. Understand about Evolution of Database Systems in the modern world and what business are doing with their data and what their new needs are with respect to changing industry landscapes.
Dive into the Opportunities available for businesses and industry verticals: the ones which are identified already and the ones which are not explored yet.
Understand the Microsoft’s Cloud vision and what is Microsoft’s Azure platform is offering, for Infrastructure as a Service or Platform as a Service for you to build your own offerings.
Introduce and demo some of the Real World Scenarios/Case Studies where Businesses have used the Cloud/Azure for creating New and Innovative solutions to unlock these potentials.
The cloud is all the rage. Does it live up to its hype? What are the benefits of the cloud? Join me as I discuss the reasons so many companies are moving to the cloud and demo how to get up and running with a VM (IaaS) and a database (PaaS) in Azure. See why the ability to scale easily, the quickness that you can create a VM, and the built-in redundancy are just some of the reasons that moving to the cloud a “no brainer”. And if you have an on-prem datacenter, learn how to get out of the air-conditioning business!
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuickSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
Similar to Enterprise Cloud Data Platforms - with Microsoft Azure (20)
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.
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.