Azure analysis services next step to bi in the cloud

The document outlines an agenda for a presentation on formulating a Power BI enterprise strategy. The agenda includes introductions, presentations on how Power BI empowers businesses and planning a data access strategy, a question and answer session, and information about an upcoming two-day Power BI workshop. It also provides background information about the presenters and describes various Power BI tools and capabilities for business users, data analysts, BI professionals, and developers.

This document summarizes how businesses can transform through business intelligence (BI) and advanced analytics using Microsoft's modern BI platform. It outlines the Power BI and Azure Analysis Services tools for visualization, data modeling, and analytics. It also discusses how Collective Intelligence and Microsoft can help customers accelerate their move to a data-driven culture and realize benefits like increased productivity and cost savings by implementing BI and advanced analytics solutions in the cloud. The presentation includes demonstrations of Power BI and Azure Analysis Services.

This document contains contact information for Marcos Freccia, a SQL Server DBA and Data Platform MVP at Zalando SE. It also lists some common challenges for BI professionals such as managing data in the cloud, ease of use and adoption, keeping data current, integration with existing environments, and managing BI systems. Finally, it provides an overview of Power BI including its key benefits, data sources, visualization capabilities, and integration with other Microsoft products.

Azure Purview is Microsoft's cloud-native data governance service that provides unified data discovery, cataloging, and classification across hybrid and multi-cloud environments. It automates the extraction of metadata at scale and identifies data lineage between sources. The service includes a data map, data catalog, and data insights. The data map automates metadata scanning and lineage tracking. The data catalog enables effortless discovery and browsing of classified data. Data insights provides governance reporting across the data estate.

Azure Synapse is the evolution of Azure SQL Data Warehouse, combining big data, data storage and data integration into a single service for end-to-end cloud scale analytics. It provides unlimited analytics with unparalleled speed to gain insights. Azure Synapse brings together enterprise data warehousing and big data analytics to give a unified experience with the advantages of both worlds.

James Serra is a Big Data Evangelist at Microsoft with over 28 years of experience in IT. He has worked in various roles including as a desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, and PDW developer. Serra is an author, blogger, and presenter who shares his expertise in business intelligence and big data. In his presentation, he provides an overview of the Microsoft BI stack, career opportunities in BI, and lessons from his own transition from DBA to a BI focus.

Talk Description: The Modern Data Warehouse architecture is a response to the emergence of Big Data, Machine Learning and Advanced Analytics. DevOps is a key aspect of successfully operationalising a multi-source Modern Data Warehouse. While there are many examples of how to build CI/CD pipelines for traditional applications, applying these concepts to Big Data Analytical Pipelines is a relatively new and emerging area. In this demo heavy session, we will see how to apply DevOps principles to an end-to-end Data Pipeline built on the Microsoft Azure Data Platform with technologies such as Data Factory, Databricks, Data Lake Gen2, Azure Synapse, and AzureDevOps. Resources: https://aka.ms/mdw-dataops

An introduction to the concepts and requirements to get started developing data pipelines in Azure Data Factory version 1.

Azure Analysis Services is a tabular multidimensional cloud service for deploying and consuming analysis models. It allows users to create an Azure Analysis Services server to host analysis models in the cloud, deploy models to the server, and then consume the models. The presenter provided an overview of Azure Analysis Services and demonstrated how to create a server, deploy models, and consume them.

--session donnée lors du SQL Saturday Lisbon 2015-- Data Management Gateway (and also AS Connector) is what make modern Microsoft BI stack hybrid. Power BI and Azure Data Factory use that component to interact with On-Prem Data assets. That session is a Deep dive into the DMG and the hybrid architecture involved by Power BI and ADF. How does it work ? Security, Firewall, Certificates, Multiple gateways, Admin delegation, Scale out, Disaster Recovery…. All that topics will be covered during that technical session.

The document discusses how companies can use big data analytics and Azure Databricks to improve their customer experiences and grow their business. It provides an overview of how Wide World Importers seeks to expand its customers through an omni-channel strategy using analytics from data across its retail stores, website, and mobile apps. The document also outlines logical architectures for ingesting, storing, preparing, training models on, and serving data using Azure Databricks and other Azure services.

This document compares Azure Data Factory (ADF) and SQL Server Integration Services (SSIS) for data integration tasks. It outlines the core concepts and architecture of ADF, including datasets, pipelines, activities, scheduling and execution. It then provides an overview of what SSIS is used for and its benefits. The document proceeds to compare ADF and SSIS in terms of development, administration, deployment, monitoring, supported sources and destinations, security, and pricing. It concludes that while both tools are not meant for the same purposes, organizations can benefit from using them together in a hybrid approach for different tasks.

Machine learning allows us to build predictive analytics solutions of tomorrow - these solutions allow us to better diagnose and treat patients, correctly recommend interesting books or movies, and even make the self-driving car a reality. Microsoft Azure Machine Learning (Azure ML) is a fully-managed Platform-as-a-Service (PaaS) for building these predictive analytics solutions. It is very easy to build solutions with it, helping to overcome the challenges most businesses have in deploying and using machine learning. In this presentation, we will take a look at how to create ML models with Azure ML Studio and deploy those models to production in minutes.

The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.

This document discusses Microsoft Azure and its capabilities. It highlights that Azure has over 100 datacenters globally, with 19 regions currently online. It also notes that Azure has one of the top 3 networks in the world and offers larger VM sizes than AWS or Google Cloud. The document then summarizes some of Azure's core capabilities like compute, storage, databases, analytics and more. It provides examples of how customers can use Azure's tools and services.

The developer world is changing as we create and generate new data patterns and handling processes within our applications. Additionally, with the massive interest in machine learning and advanced analytics how can we as developers build intelligence directly into our applications that can integrate with the data and data paths we are creating? The answer is Azure Databricks and by attending this session you will be able to confidently develop smarter and more intelligent applications and solutions which can be continuously built upon and that can scale with the growing demands of a modern application estate.

This document discusses Azure Data Factory (ADF) and how it can be used to build and orchestrate data pipelines without code. It describes how ADF is a hybrid data integration service that improves on its previous version. It also explains how existing SSIS packages can be "lifted and shifted" to ADF to modernize solutions while retaining investments. The document demonstrates creating pipelines and data flows in ADF, handling schema drift, and best practices for development.

In this webinar recording, we evaluate Traditional BI tools like SAP Business Objects (Web Intelligence and SAP Lumira Designer) and compare them against Self-Service BI and Data Discovery capabilities of the top players in the market, namely SAP Analytics Cloud, Microsoft Power BI, Tableau, Qlik Sense & TIBCO Spotfire.

This document discusses Microsoft's business intelligence (BI) solution stack, including the roles of Analysis Services, Reporting Services, Master Data Services, and Integration Services. It focuses on Analysis Services and describes how it provides scalable and performant data modeling, predictive analytics, and self-service BI capabilities through integration with tools like Excel, SharePoint, and SQL Server. Real-world customer examples are also presented to demonstrate how Analysis Services can help organizations improve decision making and reduce costs.

The document discusses introducing Power BI in a company. It describes how the presenter evaluated Power BI for their needs at a bank with 24,000 employees across 8 banks. Key areas evaluated included data sources, modeling, sharing/delivery, and licensing. The presenter discusses Power BI features for connecting to various data sources, modeling capabilities in Power BI, and sharing options like groups, content packs, and subscriptions that can be used to deliver reports and dashboards to different user groups.

15 janvier 2018 Groupe Excel & Power BI Sujet : Fail fast, iterate quickly with power bi and google analytics

Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.

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.

New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution.  In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution.  We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.