Big Data Analytics from Azure Cloud to Power BI Mobile

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.

The difference between a Data Lake and a Data Vault is the difference between a stethoscope and a radar.

This white paper will present the opportunities laid down by data lake and advanced analytics, as well as, the challenges in integrating, mining and analyzing the data collected from these sources. It goes over the important characteristics of the data lake architecture and Data and Analytics as a Service (DAaaS) model. It also delves into the features of a successful data lake and its optimal designing. It goes over data, applications, and analytics that are strung together to speed-up the insight brewing process for industry’s improvements with the help of a powerful architecture for mining and analyzing unstructured data – data lake.

This document provides an overview of a speaker and their upcoming presentation on Microsoft's data platform. The speaker is a 30-year IT veteran who has worked in various roles including BI architect, developer, and consultant. Their presentation will cover collecting and managing data, transforming and analyzing data, and visualizing and making decisions from data. It will also discuss Microsoft's various product offerings for data warehousing and big data solutions.

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.

I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?”  Yes!  The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one.  Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives.  I will help you to identify the business value opportunity from Big Data and how to operationalize it.  Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them.  In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!”  Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.

With this support you would be able to have the basic of Azure Data slack and it will help you to pass the DP-200 and DP-201. If you need some basics on Azure, you can download this support : https://www.slideshare.net/AlexandreBERGERE/azure-fundamentals-153339148. This support is a summary from the paths: Azure for the Data Engineer Store data in Azure Work with relational data in Azure Large Scale Data Processing with Azure Data Lake Storage Gen2 Implement a Data Streaming Solution with Azure Streaming Analytics Implement a Data Warehouse with Azure SQL Data Warehouse in Microsoft Learn.

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.

This document discusses techniques for optimizing Power BI performance. It recommends tracing queries using DAX Studio to identify slow queries and refresh times. Tracing tools like SQL Profiler and log files can provide insights into issues occurring in the data sources, Power BI layer, and across the network. Focusing on optimization by addressing wait times through a scientific process can help resolve long-term performance problems.

Check out this presentation to learn the basics of using Attunity Replicate to stream real-time data to Azure Data Lake Storage Gen2 for analytics projects.

This session will focus on how to get from 'Minimum Viable Product' (MVP) to scale. It will also explain how to deal with unpredictable demand and how to build a scalable business. Attend this session to learn how to: Scale web servers and app services with Elastic Load Balancing and Auto Scaling on Amazon EC2 Scale your storage on Amazon S3 and S3 Reduced Redundancy Storage Scale your database with Amazon DynamoDB, Amazon RDS, and Amazon ElastiCache Scale your customer base by reaching customers globally in minutes with Amazon CloudFront

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.

Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp. Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6

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.

- The document discusses building a data lake in Azure using Spark and Databricks. It begins with an introduction of the presenter and their experience. - The rest of the document is organized into sections that discuss decisions around why to use a data lake and Azure/Databricks, how to build the lake by ingesting and organizing data, using Delta Lake for integrated and curated layers, securing the lake, and enabling analytics against the lake. - The key aspects covered include getting data into the lake from various sources using custom Spark jobs, organizing the lake into layers, cataloging data, using Delta Lake for transactional tables, implementing role-based security, and allowing ad-hoc queries.

An overview of AAS and the slides presented during Azure Bootcamp Day 2018 in the SafeNet offices, downtown Milwaukee, WI.

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.

A sharing in a meetup of the AWS Taiwan User Group. The registration page: https://bityl.co/7yRK The promotion page: https://www.facebook.com/groups/awsugtw/permalink/4123481584394988/

Data orchestration is the lifeblood of any successful data analytics solution. Take a deep dive into Azure Data Factory's data movement and transformation activities, particularly its integration with Azure's Big Data PaaS offerings such as HDInsight, SQL Data warehouse, Data Lake, and AzureML. Participants will learn how to design, build and manage big data orchestration pipelines using Azure Data Factory and how it stacks up against similar Big Data orchestration tools such as Apache Oozie. Video of presentation: https://channel9.msdn.com/Events/Ignite/Australia-2017/DA332

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.

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.

Cloud computing is here to stay, and it is never too soon to begin understanding the impact it will have on application architecture. In this talk we will discuss the two most significant architectural mind-shifts, discussing the key patterns changes generally and seeing how these new cloud patterns map naturally into specific programming practices in Windows Azure. Specifically this relates to (a) Azure Roles and Queues and how to combine them using cloud-friendly design patterns, and (b) the combination of relational data and non-relational data, how to decide among them, and how to combine them. The goal is for mere mortals to build highly reliable applications that scale economically. The concepts discussed in this talk are relevant for developers and architects building systems for the cloud today, or who want to be prepared to move to the cloud in the future. This talk was delivered by Bill Wilder at the Vermont Code Camp 2 on 11-Sept-2010.

Learn about the only solution to instantly provision a full-featured ETL environment running on AWS for less than your Sunday newspaper!

The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/

The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/

This document summarizes Atlassian's adoption of Databricks to manage their growing data pipelines and platforms. It discusses the challenges they faced with their previous architecture around development time, collaboration, and costs. With Databricks, Atlassian was able to build scalable data pipelines using notebooks and connectors, orchestrate workflows with Airflow, and provide self-service analytics and machine learning to teams while reducing infrastructure costs and data engineering dependencies. The key benefits included reduced development time by 30%, decreased infrastructure costs by 60%, and increased adoption of Databricks and self-service across teams.

Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.

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

This document discusses connecting Oracle Analytics Cloud (OAC) Essbase data to Microsoft Power BI. It provides an overview of Power BI and OAC, describes various methods for connecting the two including using a REST API and exporting data to Excel or CSV files, and demonstrates some visualization capabilities in Power BI including trends over time. Key lessons learned are that data can be accessed across tools through various connections, analytics concepts are often similar between tools, and while partnerships exist between Microsoft and Oracle, integration between specific products like Power BI and OAC is still limited.

For our next ArcReady, we will explore a topic on everyone’s mind: Cloud computing. Several industry companies have announced cloud computing services . In October 2008 at the Professional Developers Conference, Microsoft announced the next phase of our Software + Services vision: the Azure Services Platform. The Azure Services Platforms provides a wide range of internet services that can be consumed from both on premises environments or the internet. Session 1: Cloud Services In our first session we will explore the current state of cloud services. We will then look at how applications should be architected for the cloud and explore a reference application deployed on Windows Azure. We will also look at the services that can be built for on premise application, using .NET Services. We will also address some of the concerns that enterprises have about cloud services, such as regulatory and compliance issues. Session 2: The Azure Platform In our second session we will take a slightly different look at cloud based services by exploring Live Mesh and Live Services. Live Mesh is a data synchronization client that has a rich API to build applications on. Live services are a collection of APIs that can be used to create rich applications for your customers. Live Services are based on internet standard protocols and data formats.

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

Join us for a deep dive into Windows Azure. We’ll start with a developer-focused overview of this brave new platform and the cloud computing services that can be used either together or independently to build amazing applications. As the day unfolds, we’ll explore data storage, SQL Azure™, and the basics of deployment with Windows Azure. Register today for these free, live sessions in your local area.

This is a slide deck from QuerySurge's Big Data Testing webinar. Learn why Testing is pivotal to the success of your Big Data Strategy . Learn more at www.querysurge.com The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth. Learn why testing your enterprise's data is pivotal for success with big data, Hadoop and NoSQL. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool. This information is geared towards: - Big Data & Data Warehouse Architects, - ETL Developers - ETL Testers, Big Data Testers - Data Analysts - Operations teams - Business Intelligence (BI) Architects - Data Management Officers & Directors You will learn how to: - Improve your Data Quality - Accelerate your data testing cycles - Reduce your costs & risks - Provide a huge ROI (as high as 1,300%)