Near-real time reporting Azure
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One of the most common requirement on a projects nowadays is real time monitoring and reporting. Easy to say, expensive to implement and complex to maintain. In this session we'll take a look on the Azure Services that enable us to fulfil this requirements with minimal effort and with maximum benefits. We have on our radar services like Azure Time Series Insights, Analysis Services and PowerBI. After this session you will know what are the services can be used for near-real time and what does near-real time means in the real world.
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- SQL Database service tiers including Basic, Standard, and Premium, which are differentiated by performance levels measured in Database Transaction Units (DTUs) and other features.
- Database size limits and performance metrics for each tier.
- Database replication and high availability capabilities to ensure reliability.
- Support for common SQL Server features while noting some limitations compared to on-premises SQL Server.
- Considerations for database naming, users/logins, migrations, and automation in the SQL Database platform.
- Indexing requirements and compatibility differences to be aware of.
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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.
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Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
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This document discusses using Azure Data Explorer (ADX) for data science workflows. ADX is a fully managed analytics service for real-time analysis of streaming data. It allows for ad-hoc querying of data using Kusto Query Language (KQL) and integrates with various Azure data ingestion sources. The document provides an overview of the ADX architecture and compares it to other time series databases. It also covers best practices for ingesting data, visualizing results, and automating workflows using tools like Azure Data Factory.
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Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce you to SPICE - a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
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This document discusses cloud computing concepts like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It introduces Microsoft's Azure cloud platform, describing its global regions and services for compute, storage, databases, analytics, AI, and more. Examples are provided for deploying virtual machines, mobile backends, and cognitive APIs on Azure using the portal, PowerShell, or CLI.
Build 2015 – Azure overview
azure overview, impressions from build 2015, cloud storage, compute, data lake, analytics, service fabric, containerization, etc.
Opportunity: Data, Analytic & Azure
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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.
Differentiate Big Data vs Data Warehouse use cases for a cloud solution
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.
Designing for Cloud
This document discusses designing a product for deployment on Windows Azure cloud platform. It outlines key cloud computing concepts like IaaS, PaaS and SaaS. It then analyzes the goals and technical challenges of an existing product and proposes a new architectural design using Azure PaaS components like web and worker roles, service bus, caching, databases to address issues of scalability, extensibility and portability.
ABD202_Best Practices for Building Serverless Big Data Applications
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.
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Near-real time reporting Azure
- 3. Insights
- 5. RADU VUNVULEA Technology Enthusiast Dreamer Microsoft Azure MVP Speaker & Trainer Writer & Blogger Idealist Software Architecture Crafter
- 14. RealTime Near-realtime Reporting Consolidation Azure Time Series Insights Power BI Azure Analysis Services
- 21. Ingest • Time-series data Store • Index • Retires Explore • Visualize • Anomaly detection Root cause analyses • Reference data
- 29. Easy to get started Near-real time insights Build custom solution Scalable Drill down into the problem
- 32. Deep dive inside data Multiple perspective 1 to n page visualization One Data Set
- 39. Reports • 1 to n pages • One dataset • Complext filtering support • No support for alerts • Available from dashboard Dashboards • One Page • Multiple datasets • No filter support • Can create alerts • Support for natural language queries
- 43. Define one trusted data model Simply data structure Hide unnecessary information Tabular model Cache data