Power BI for Big Data and the New Look of Big Data Solutions

Power BI is a business analytics service that allows users to analyze data and share insights. It includes dashboards, reports, and datasets that can be viewed on mobile devices. Power BI integrates with various data sources and platforms like SQL Server, Azure, and Office 365. It provides self-service business intelligence capabilities for end users to explore and visualize data without assistance from IT departments.

Part of proper governance in Power BI means taking proper care of what goes on in your tenant. Here's a list of areas you need to watch for and some helpful telemetry to start collecting.

The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.

SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.

#LetitBI Calgary, introducing #PowerBI as a storyteller for #BI professionals. Slides deck from #Calgary BI user group.

The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.

View this presentation to learn about how to automate data pipelines for analytics from the experts at Microsoft and Attunity.

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

A talk presented by Max Schultze from Zalando and Arif Wider from ThoughtWorks at NDC Oslo 2020. Abstract: The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability. At Zalando - europe’s biggest online fashion retailer - we realised that accessibility and availability at scale can only be guaranteed when moving more responsibilities to those who pick up the data and have the respective domain knowledge - the data owners - while keeping only data governance and metadata information central. Such a decentralized and domain focused approach has recently been coined a Data Mesh. The Data Mesh paradigm promotes the concept of Data Products which go beyond sharing of files and towards guarantees of quality and acknowledgement of data ownership. This talk will take you on a journey of how we went from a centralized Data Lake to embrace a distributed Data Mesh architecture and will outline the ongoing efforts to make creation of data products as simple as applying a template.

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.

The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

Explain about power BI Overview from Power BI Desktop, Power BI Service, Power BI Report Server and Power BI Mobile that consume all BI Data from Dataset and datamodel

Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.

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.

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.

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.

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.

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.

The document discusses how organizations can leverage cloud, data, and AI to gain competitive advantages. It notes that 80% of organizations now adopt cloud-first strategies, AI investment increased 300% in 2017, and data is expected to grow dramatically. The document promotes Microsoft's cloud-based analytics services for harnessing data at scale from various sources and types. It provides examples of how companies have used these services to improve customer experience, reduce costs, speed up insights, and gain operational efficiencies.

In this conference I made an interesting laboratory using Power BI Data Flow and Power BI Automated Machine Learning. But, before the workshop we had an interesting speak about Artificial Intelligence and Machine Learning on Azure

This was a very interesting conference, TIC students oriented where I take him to the azure ecosystem for data warehousing architecture and best practices to reach powerful Business Intelligence Solutions according to the new era

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

Get your data to life with Power BI visualization and insights! With the changing landscape of Power BI features it is essential to get hold of configuration and deployment practices within your data platform that will ensure you are on-par with compliance & security practices. In this session we will overview from the basics leading into advanced tricks on this landscape: How to deploy Power BI? How to implement configuration parameters and package BI features as a part of Office 365 roll out in your organisation? What are newest features and enhancements on this Power BI landscape? How to manage on-premise vs on-cloud connectivity? How can you help and support the Power BI community as well? Having said that within the objectives of this session, cloud computing is another aspect of this technology made is possible to get data within few clicks and ticks to the end-user. Let us review how to manage & connect on-premise data to cloud capabilities that can offer full advantage of data catalogue capabilities by keeping data secure as per Information Governance standards. Not just with nuts and bolts, performance is another aspect that every Admin is keeping up, let us look into few settings on how to maximize performance to optimize access to data as required. Gain understanding and insight into number of tools that are available for your Business Intelligence needs. There will be a showcase of events to demonstrate where to begin and how to proceed in BI world. - D BI A Consulting consulting@dbia.uk

This document discusses using Azure services for big data analytics and data insights. It provides an overview of Azure services like Azure Batch, Azure Data Lake, Azure HDInsight and Power BI. It then describes a demo solution that uses these Azure services to analyze job posting data, including collecting data using a .NET application, storing in Azure Data Lake Store, processing with Azure Data Lake Analytics and Azure HDInsight, and visualizing results in Power BI. The presentation includes architecture diagrams and discusses implementation details.

Azure provides cloud computing services including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) that allow users to rapidly setup environments, scale resources to meet demands, and increase efficiency. Azure offers a wide range of services such as compute, storage, databases, analytics, machine learning, IoT, and security to help users migrate existing applications or build new cloud-native applications. The document outlines key scenarios for using Azure such as development/testing, lift and shift of existing applications, big data analytics, and identity management to provide a starting point for leveraging the cloud platform

Microsoft Power BI is a business analytics tool that allows users to access, analyze, and visualize data from various sources. It offers self-service BI capabilities that enable end users to explore and gain insights from data. Power BI provides live dashboards, natural language querying, and content packs for popular SaaS solutions. It integrates with Microsoft products and allows sharing and collaboration on reports and dashboards. Signing up for a free Power BI account is quick and only requires a work or school email address.

This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.

Big Data Analytics in the Cloud using Microsoft Azure services was discussed. Key points included: 1) Azure provides tools for collecting, processing, analyzing and visualizing big data including Azure Data Lake, HDInsight, Data Factory, Machine Learning, and Power BI. These services can be used to build solutions for common big data use cases and architectures. 2) U-SQL is a language for preparing, transforming and analyzing data that allows users to focus on the what rather than the how of problems. It uses SQL and C# and can operate on structured and unstructured data. 3) Visual Studio provides an integrated environment for authoring, debugging, and monitoring U-SQL scripts and jobs. This allows

We will take a look at an introduction and overview of Azure Analysis Services: Microsoft‘s cloud-based analytical engine and Platform as a Service (PaaS) offerings and how to leverage SQL Server Data Tools to build and deploy a tabular data model to Azure Analysis Services. We will then connect with Power BI Desktop and the Power BI portal to build visualizations. We will discuss Azure Analysis Services features and capabilities, use cases, provisioning and deployment, managing and monitoring, tools, and report creation. Azure Analysis Service became Globally Available in April 2017, and Power BI has released several major updates as well.

The document discusses new features in SQL Server Analysis Services (SSAS) "Denali" release including a new unified BI Semantic Model that brings together relational and multidimensional data models. It provides more flexibility and choices in building BI applications using either tabular or multidimensional approaches. Denali also improves performance and scalability with new in-memory and compression technologies. New tools are introduced for data modeling and management.

Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.

The document summarizes new features and upcoming projects in SQL Server and BI. Key points include: improved SQL engine and management tools; Master Data Services; self-service analysis and reporting projects; reference architectures for petabyte data warehouses; and Project Madison for appliance-like data warehouses running on industry standard hardware. Project Gemini will provide self-service analysis in Excel with in-memory analytics and sharing capabilities.

This document discusses building cubes in SQL Server Analysis Services (SSAS) and PowerPivot. It covers cubes created manually in SSAS, auto-cubes created in PowerPivot, and cubes in the upcoming Denali release. PowerPivot allows users to analyze massive data volumes with Excel. Reporting Services and SharePoint can be used to publish and share PowerPivot reports. SSAS provides an advanced feature set for scalable cube design. Denali will converge cube technologies with its new BI Semantic Model.

This document discusses building IoT and big data solutions on Microsoft Azure. It provides an overview of common data types and challenges in integrating diverse data sources. It then describes several Azure services that can be used to ingest, process, analyze and visualize IoT and other large, diverse datasets. These services include IoT Hub, Event Hubs, Stream Analytics, HDInsight, Data Factory, DocumentDB and others. Examples and demos are provided for how to use these services to build end-to-end IoT and big data solutions on Azure.

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.

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!

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

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric.  What do all these terms mean and how do they compare to a modern data warehouse?  In this session I’ll cover all of them in detail and compare the pros and cons of each.  They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.

Over the last decade, the 3Vs of data - Volume, Velocity & Variety has grown massively. The Big Data revolution has completely changed the way companies collect, analyze & store data. Advancements in cloud-based data warehousing technologies have empowered companies to fully leverage big data without heavy investments both in terms of time and resources. But, that doesn’t mean building and managing a cloud data warehouse isn’t accompanied by any challenges. From deciding on a service provider to the design architecture, deploying a data warehouse tailored to your business needs is a strenuous undertaking. Looking to deploy a data warehouse to scale your company’s data infrastructure or still on the fence? In this presentation you will gain insights into the current Data Warehousing trends, best practices, and future outlook. Learn how to build your data warehouse with the help of real-life use-cases and discussion on commonly faced challenges. In this session you will learn: - Choosing the best solution - Data Lake vs. Data Warehouse vs. Data Mart - Choosing the best Data Warehouse design methodologies: Data Vault vs. Kimball vs. Inmon - Step by step approach to building an effective data warehouse architecture - Common reasons for the failure of data warehouse implementations and how to avoid them

Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.

Power BI has become a product with a ton of exciting features. This presentation will give an overview of some of them, including Power BI Desktop, Power BI service, what’s new, integration with other services, Power BI premium, and administration.

The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.

Discover, manage, deploy, monitor – rinse and repeat.  In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators.  We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you.  Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.

In three years I went from a complete unknown to a popular blogger, speaker at PASS Summit, a SQL Server MVP, and then joined Microsoft.  Along the way I saw my yearly income triple.  Is it because I know some secret?  Is it because I am a genius?  No!  It is just about laying out your career path, setting goals, and doing the work. I'll cover tips I learned over my career on everything from interviewing to building your personal brand.  I'll discuss perm positions, consulting, contracting, working for Microsoft or partners, hot fields, in-demand skills, social media, networking, presenting, blogging, salary negotiating, dealing with recruiters, certifications, speaking at major conferences, resume tips, and keys to a high-paying career. Your first step to enhancing your career will be to attend this session! Let me be your career coach!

With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.

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.

Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer.  It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance).  Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc.  So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.