pl300-1.json

[
  {
    "title": "Summary - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/6-summary",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nSummary\n3 minutes\n\nUnderstanding data analytics concepts enables you to plan for a successful data analytics project. Knowing that the sales manager is asking for descriptive, diagnostic, and prescriptive analytics helps the data team understand who and what technologies need to be involved.\n\nIn this module you've learned how to:\n\nDefine the five types of data analytics\nDescribe the data analytics process\nIdentify data types and storage\nLearn more\nExplore Azure database and analytics services\nExplore fundamentals of modern data warehousing\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Knowledge check - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/5-knowledge-check",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nKnowledge check\n3 minutes\n\nChoose the best response for each of the questions below. Then select Check your answers.\n\nCheck your knowledge\n1. \n\nWhich of the following is an example of descriptive analytics?\n\n \n\nA monthly sales report looking at sales data over the last year\n\nA social media algorithm that recommends curated content\n\nAn annual HR report that forecasts predicted attrition for the next year\n\n2. \n\nWhat are the first three steps in the data analytics process?\n\n \n\nData exploration, data analysis, and deploy analytics solution\n\nData analysis, deploy analytics solution, and request feedback\n\nRequirements gathering, data ingestion and processing, and data exploration\n\n3. \n\nWhy is it important to understand the difference between structured and unstructured data?\n\n \n\nThere is no difference between structured and unstructured data\n\nUnderstanding the difference can determine where data should be stored and what kind of analysis is most appropriate\n\nUnderstanding the difference will determine if the business requirements have been met\n\nCheck your answers\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Understand types of data and data storage - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/4-understand-types-of-data-data-storage",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nLearn  Training  Browse  Introduction to data analytics on Azure  Understand concepts of data analytics \nAdd\nPrevious\nUnit 4 of 6\nNext\nUnderstand types of data and data storage\nCompleted\n100 XP\n3 minutes\n\nUnderstanding how data is structured and stored is a critical step that occurs at the beginning of every analytics project, during requirements gathering. Both structured and unstructured data are suitable for analysis, but the tools the data team will use to ingest, transform, and store data will differ according to the data type.\n\nStructured data\n\nStructured data is familiar to most of us. Letters and numbers are organized into columns and rows for simplified search and processing. Structured data is typically quantitative in nature and stored in relational databases and data warehouses. Structured data may reside in something familiar, a Microsoft Excel table. Structured data storage on a larger scale may be stored in a relational database, like an Azure SQL database.\n\nStructured data lends well to all types of analytics and is the most accessible. Structured Query Language (SQL) is used to query relational databases and is commonly used by data analysts, data engineers, and data scientists alike.\n\nPresentation of annual financial data is a common example of using structured data, whether that data is stored in Excel spreadsheets or a relational database like Azure SQL database.\n\nUnstructured data\n\nUnstructured data is information that isn't organized in any discernable manner. Unstructured data is often more suitable for qualitative analysis and is stored in non-relational databases and data lakes.\n\nThe formats of unstructured data vary widely, from Word documents, .csv files, json files, images, and PDFs, to audio and video files. These files would be stored in an Azure Data Lake.\n\nNext unit: Knowledge check\n\nContinue\n\nHaving an issue? We can help!\n\nFor issues related to this module, explore existing questions using the #azure training tag or Ask a question ​on Microsoft Q&A .\nFor issues related to Certifications and Exams, post on Credentials Support Forum or visit our Credentials Help.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Explore the data analytics process - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/3-explore-data-analytics-process",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nLearn  Training  Browse  Introduction to data analytics on Azure  Understand concepts of data analytics \nAdd\nPrevious\nUnit 3 of 6\nNext\nExplore the data analytics process\nCompleted\n100 XP\n10 minutes\n\nData analytics is the process of collecting, transforming, and presenting data to inform decision making. Developing an analytics solution begins before any technology is involved, with a requirements gathering exercise. From there the process continues to ingesting, processing, and exploring data. Analysis and solution deployment are followed by requesting feedback from the business. Finally, the analytics solution is optimized and the process begins again. The analytics process is never done.\n\nHere, you'll learn what steps are included in the data analytics process.\n\nRequirements gathering\n\nData teams work with the business to understand business needs and intended outcomes of an analytics project. Requirements gathering includes identification of:\n\nWhat are the key business questions?\nWhat data are available? Will available data respond to business needs or does more data need to be collected?\nWhat are the essential dimensions - how will stakeholders want to slice and dice the data?\nWhat are the key performance indicators or performance metrics?\nHow will users consume the analysis?\nWhat is the frequency of data ingestion?\nWhat is the frequency of reporting?\n\nIt's a common misunderstanding that a data team will be able to extract insights from volumes of data without having discussed any of the questions above. A data team won't be able to determine the appropriate type of analysis and/or the correct solution without having followed a structured requirements gathering process.\n\nRequirements gathering may take many forms depending on team structure, data volume and velocity, and the type of analysis required.\n\nData ingestion and processing\n\nUsing the requirements gathered from the business, a data team will begin to ingest and transform data.\n\nAzure data services available for ingestion and transformation include, but aren't limited to Azure Cosmos DB, Azure SQL Database, Azure Synapse Analytics, Azure Databricks, Azure Data Lake, Azure Event Hubs, and Azure Stream Analytics.\n\nA data engineer is often responsible for the initial ingestion and transformation of data. Data is then surfaced to other members of the data team for exploration and analysis. Azure data services commonly used by enterprise data analysts and data scientists may be limited to specific databases or data lakes.\n\nThe terms Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) refer to the process of ingesting and processing data.\n\n Note\n\nLearn more about the ETL process.\n\nData exploration\n\nData exploration is the effort to understand what you're working with, and how that data can respond to the needs of the business. Data exploration can be done in many different tools. At a basic level, the data team might use Excel to look at the contents of a .csv to view the number of records and/or the specific variables they have to explore. Each member of the data team may conduct data profiling in a different tool. An analyst may profile data using Power Query in Power BI, while a data scientist may use Apache Spark in Azure Synapse.\n\nData exploration helps inform required data transformation and cleaning steps, which can be communicated back upstream to the data engineer to build into the analytics solution.\n\nThe analyst may also begin dashboard or report prototyping in the data exploration phase. Understanding how the business wants to see and use the results of the analysis will inform the prototype, along with trends and or insights uncovered during data exploration.\n\nData analysis\n\nAfter data have been explored, data analysis can begin. Analysis can be descriptive, predictive, prescriptive, or even cognitive and can be conducted in many different tools. Results should respond to identified business needs and upon initial review, will likely lead to more questions and analysis.\n\nThere is a difference between a one-off analysis and an analytics solution. Both have their place, and the need for one or the other will be determined during the requirements gathering process.\n\nDeploy analytics solution\n\nResults will be presented to stakeholders in a reporting or data visualization tool like Microsoft Power BI, where people can interact with and use the results of the analysis for decision making.\n\nKey considerations in the deployment of an analytics solution will help determine the right tools, licensing, and permissions needed to get data into the hands of everyone that needs it. Access to timely insights will ultimately lead to data-informed decisions.\n\nRequest and process feedback\n\nDeployment of an analytics solution may feel like a finish line, but it's important to understand the answers to a few key questions.\n\nIs the data product being used?\nDoes the analysis truly respond to the business needs?\nAre there any unforeseen technical issues with the solution?\nIs the data product accessible?\nWhat new business questions does this analysis raise?\n\nThe individuals using your analytics solution are your customers, and if the product you have built doesn't adequately respond to their needs, there's work to be done.\n\nThere are multiple mediums of soliciting feedback. The first launch of a solution may require regular review meetings, whereas monitoring usage metrics of an ongoing project will help you understand usage over time and even areas of your solution that are and aren't useful.\n\nOptimize solution\n\nImplementing the feedback of your users is a logical first step to optimize your analytics solution. There may also be opportunities to remove latency in the process, for example, ensuring the data refresh occurs in the allotted time. Optimization could also mean more accurately reflecting user needs by tweaking visual design or ensuring report visuals render quickly.\n\nBegin again\n\nThe analytics process is cyclical by nature. Exposing data and insights often leads to requests for more analysis, which leads to more feedback, and so on. On a large data team, the analytics process may occur in short sprints, where different team members work simultaneously to achieve small goals before moving onto the next step in the process. On smaller teams, one person may be acting in multiple roles, which would make the process look different.\n\nRegardless of what the process looks like for you, communication is a critical component throughout. The data team must communicate with each other and be in dialogue with the business, to ensure solution development is responding to business needs and needs that may appear in the data.\n\nNext unit: Understand types of data and data storage\n\nContinue\n\nHaving an issue? We can help!\n\nFor issues related to this module, explore existing questions using the #azure training tag or Ask a question ​on Microsoft Q&A .\nFor issues related to Certifications and Exams, post on Credentials Support Forum or visit our Credentials Help.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Understand data analytics types - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/2-understand-data-analytics-types",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nLearn  Training  Browse  Introduction to data analytics on Azure  Understand concepts of data analytics \nAdd\nPrevious\nUnit 2 of 6\nNext\nUnderstand data analytics types\nCompleted\n100 XP\n7 minutes\n\nData analytics is used for exploring data, extracting insights, and acting on those insights.\n\nData analytics covers a range of activities, each with its own focus and goals. These activities are categorized as descriptive, diagnostic, predictive, prescriptive, and cognitive analytics.\n\nIn this unit, you'll learn about these five categories of data analytics and what they are used for.\n\nDescriptive analytics\nWhat happened?\n\nDescriptive analytics answers questions about what happened, based on historical data, to inform decisions about the future. Descriptive analytics techniques summarize large datasets to present insights to stakeholders. Descriptive analytics is the most common type of analytics and is often performed by a data analyst.\n\nThe development of key performance indicators (KPIs) and other performance measures helps track the success or failure of business objectives. KPIs and performance metrics are often set by the business to track key initiatives. The presentation of data related to those KPIs is descriptive analytics.\n\nDescriptive analytics outputs can take many forms, including reports and dashboards. The Sales and Marketing report below displays sales and marketing data for a year by product, channel, and over time.\n\nDiagnostic analytics\nWhy did it happen?\n\nDiagnostic analytics helps answer questions about why things happened and is often the next step in data analytics after descriptive analytics. Analysts take findings from descriptive analytics and dig deeper to find the cause. Metrics and indicators of interest are further investigated to discover why they got better or worse. Diagnostic analytics is often performed by data analysts and data scientists.\n\nDiagnostic analytics generally occurs in three steps:\n\nIdentify anomalies in the data. Anomalies may be unexpected changes in a metric or a particular market.\nCollect data that's related to these anomalies.\nUse statistical techniques to discover relationships and trends that explain these anomalies.\n\nIn Contoso's sales report below, we want to understand why Contoso is or isn't winning bids for new business. Diagnostic analytics help decision makers see that applying discounts of 2% increases the likelihood of winning a bid.\n\nPredictive analytics\nWhat will happen in the future?\n\nPredictive analytics helps answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they're likely to recur, providing insight into what may happen in the future. Techniques include statistical and machine learning techniques such as forecasting, neural networks, decision trees, and regression. Predictive analytics is often performed by data scientists.\n\nThe line chart below looks at revenue won by year and month, which shows historical decline. Forecasting predicts that revenue won will continue to decrease. Decision makers may use this forecast to change course in an effort to increase the amount of revenue won.\n\nPrescriptive analytics\nWhat actions should be taken?\n\nPrescriptive analytics takes predictive analytics one step further and helps answer questions about what actions should be taken to achieve a goal or target. This technique allows businesses to make data-informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies to find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated. Prescriptive analytics is often performed by data scientists. Microsoft also provides low-code tools that can be used by analysts to conduct prescriptive analytics, like using machine learning in Power BI.\n\nAlgorithmic content recommendations are a common implementation of prescriptive analytics. Using the recommendation algorithm in Azure Machine Learning studio, data scientists can recommend the best actions Contoso should take based on a customer's past habit and characteristics. The screenshot below displays the recommendation algorithm in Azure Machine Learning designer, in which customer data is being used to prescribe a specific recommendation rating.\n\n Note\n\nTo learn more about prescriptive analytics using Azure Machine Learning, review Build a content-based recommendation system.\n\nCognitive analytics\nHow can the problem be solved best?\n\nCognitive analytics combines artificial intelligence, machine learning, and data analytics approaches to guide decision making. Cognitive analytics draws inferences from existing data and patterns, derives conclusions based on existing knowledge bases, and adds findings back into the knowledge base for future inferences--a self-learning feedback loop. This feedback loop enables cognitive applications to become more precise over time.\n\nBy tapping the benefits of massive parallel/distributed computing and the falling costs of data storage and computing power, there's no limit to the cognitive development that these systems can achieve. Microsoft's Azure AI Services enables users to take advantage of cognitive analytics by extracting insights from various types of data, including things like text and images.\n\n Note\n\nTo learn more about data cognitive analytics using Azure AI Services, review Get started with Azure AI Services.\n\nNext unit: Explore the data analytics process\n\nContinue\n\nHaving an issue? We can help!\n\nFor issues related to this module, explore existing questions using the #azure training tag or Ask a question ​on Microsoft Q&A .\nFor issues related to Certifications and Exams, post on Credentials Support Forum or visit our Credentials Help.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Introduction - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/1-introduction",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nIntroduction\n2 minutes\n\nUnderstanding key concepts of data analytics will help you and your team begin to explore and make the best use of your data.\n\nSuppose that the sales manager at Contoso wants to understand sales and marketing trends for the year. They've asked you to analyze data from the last two years to guide their next steps. What are the data showing? Why are sales trending upward? What should the sales team do to continue to increase sales? Before you can begin your analysis, your team reviews data analytics concepts to be sure you're using the right type of analytics for the job.\n\nBy the end of this module, you'll understand how different types of analytics may respond to the sales manager's questions. You'll also be able to describe the process of exploring and using data, along with types of data and how they're stored.\n\nLearning objectives\n\nIn this module, you will:\n\nDefine the five types of data analytics\nDescribe the data analytics process\nIdentify data types and storage\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Understand concepts of data analytics - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/understand-concepts-of-data-analytics/",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nUnderstand concepts of data analytics\nModule\n6 Units\nFeedback\nIntermediate\nData Analyst\nAzure\nAzure SQL Database\nAzure Synapse Analytics\nPower BI\n\nExplore key concepts of data analytics, including types of analytics, data, and storage. Explore the analytics process and tools used to discover insights.\n\nLearning objectives\n\nAfter completing this module, you will be able to:\n\nDescribe types of data analytics\nUnderstand the data analytics process\nAdd\nPrerequisites\nYou should be familiar with basic data concepts and terminology.\nIntroduction\nmin\nUnderstand data analytics types\nmin\nExplore the data analytics process\nmin\nUnderstand types of data and data storage\nmin\nKnowledge check\nmin\nSummary\nmin\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Summary - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/5-summary",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nSummary\n2 minutes\n\nYou've now been introduced to the Azure data ecosystem and modern enterprise analytics solutions.\n\nAs an analyst on the Relecloud team, you have a solid understanding of data analytics in Azure for data ingestion, processing, and modern analytics. You're confident that you'll be able to make appropriate recommendations as the data team begins to build its analytics solution.\n\nIn this module you've learned how to:\n\nDescribe the Azure data ecosystem for analytics\nLearn more\nExplore fundamentals of modern data warehousing\nAzure data architecture guide\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Explore modern analytics solution architecture - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/3-explore-modern-analytics-solution-architecture",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nLearn  Training  Browse  Introduction to data analytics on Azure  Explore Azure data services for modern analytics \nAdd\nPrevious\nUnit 3 of 5\nNext\nExplore modern analytics solution architecture\nCompleted\n100 XP\n5 minutes\n\nAfter data ingestion and processing, data are now in a format that can be used for analysis and presentation to decision makers. Presentation of insights for decision making is the end goal of a larger analytics project. Data analysts present findings to decision makers in the form of a data product, like a dashboard or report.\n\nAzure data analytics and reporting\n\nThere are many options for analytics and reporting in Azure, depending on the needs of the business. Analysts can explore and visualize data directly in the Azure ecosystem, using tools like Synapse notebooks in Azure Synapse Analytics. Analysts can also build and deploy solutions for use by others using robust reporting tools like Microsoft Power BI.\n\nExplore and visualize data in Azure Synapse Analytics\n\nAzure Synapse Analytics provides a suite of tools to process and analyze an organization's data. It incorporates SQL technologies, Transact-SQL query capabilities, and open-source Spark tools to enable you to quickly process very large amounts of data. Data exploration in Azure Synapse Studio may be the first step in the analytics process, where you can profile and examine data.\n\nData can be explored and visualized directly in Azure Synapse Studio using the Azure Synapse SQL results pane and using native visuals in Spark notebooks. Simple visualizations of your data make it easier to detect patterns, trends, and outliers, and may help you understand what your next steps in analysis will be.\n\nSynapse Studio provides a SQL script web interface for you to author SQL queries. You can also visualize your SQL script results in a chart by selecting the Chart button.\n\nSynapse notebooks enable you to analyze data across raw formats (CSV, txt, JSON, etc.), processed file formats (parquet, Delta Lake, ORC, etc.), and SQL tabular data files against Spark and SQL.\n\n Note\n\nLearn more about large-scale data analytics in Azure Synapse Analytics.\n\nVisualize data and create reporting solutions using Microsoft Power BI\n\nPower BI is an enterprise analytics tool that can help you discover and distribute insights from data stored in Azure and beyond. Azure and Power BI can be used together to connect, combine, and analyze your entire data estate. With Power BI, you can create reports with interactive visualizations to drive decision making.\n\nData visualization in Power BI helps you turn large amounts of granular data into easily understood, visually compelling, and useful business information. The use of Power BI takes the data exploration you may have done in Azure Synapse Analytics a step further. In addition to the enhanced visualization capability, Power BI also provides a platform for secure distribution of dashboards and reports.\n\nPower BI also has native connectors to many Azure data services. Using Power BI, you can connect to data in Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and more.\n\n Note\n\nLearn more about analytics and reporting with Power BI.\n\nNext unit: Knowledge check\n\nContinue\n\nHaving an issue? We can help!\n\nFor issues related to this module, explore existing questions using the #azure training tag or Ask a question ​on Microsoft Q&A .\nFor issues related to Certifications and Exams, post on Credentials Support Forum or visit our Credentials Help.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Knowledge check - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/4-knowledge-check",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nKnowledge check\n3 minutes\n\nChoose the best response for each of the questions below. Then select Check your answers.\n\nCheck your knowledge\n1. \n\nWhat type of data is best suited for batch processing?\n\n \n\nIoT gas sensor data that monitors and detects the presence of toxic or hazardous gasses.\n\nReal-time personalized advertising data.\n\nDaily sales report data.\n\n2. \n\nWhat is one advantage of using Power BI over a Synapse notebook for data visualization?\n\n \n\nThere is no advantage, Power BI and Azure Synapse Analytics are comparable for data visualization\n\nPower BI was designed for distribution of data products outside of the data team.\n\nPower BI displays simple visualizations compared to Synapse notebooks.\n\nCheck your answers\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Understand the Azure data ecosystem - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/2-understand-azure-data-ecosystem",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nUnderstand the Azure data ecosystem\n6 minutes\n\nModern analytics requires tools that can store and transform data from many sources. In this unit, you'll learn about Azure data storage solutions, data ingestion, and data processing.\n\nBefore presenting an analytics solution to Relecloud's CEO, the data team needs a clear understanding of where data will be coming from, what forms data will be in, and the anticipated scale and frequency of incoming data. Before conducting structured requirements gathering, you sit down with the team to review key data concepts.\n\nAzure data storage solutions\n\nAzure Storage accounts are the base storage type within Azure. Azure Storage offers a scalable object store for data objects and file system services in the cloud.\n\nIn an analytics solution, data from different sources are combined and prepared for use. Data can be stored as files in a data lake store or in a database. Understanding base storage types within Azure is important for the data engineer, while the data analyst needs to be familiar with an analytical data store that serves processed data in a format that can be queried using analytical tools.\n\nAreas outlined in red in the image above highlight the pieces of the analytics solution that data analysts use to make sense of the data.\n\n Note\n\nLearn more about data storage in Azure and technology choices for analytical data stores.\n\nData ingestion and processing\n\nData ingestion is the process of obtaining and importing data for immediate use or storage in an analytical data store.\n\nData processing is simply the conversion of raw data to meaningful information through a process. Depending on how data is ingested into your system, you could process each data item as it arrives, or buffer the raw data and process it in groups. Processing data as it arrives is called streaming. Buffering and processing the data in groups is called batch processing.\n\nIn batch processing, newly arriving data elements are collected into a group. The whole group is then processed at a future time as a batch. Exactly when each group is processed can be determined in many ways. For example, you can process data based on a scheduled time interval (for example, every hour), or it could be triggered when a certain amount of data has arrived. Relecloud's monthly billing process is a good example of batch processing, as account transactions are processed and billed on a monthly basis.\n\n Note\n\nBatch processing is the most common type of data processing, best suited for large datasets or data coming from legacy data systems. Batch processing is not suited for rapid analysis and decision making.\n\nIn stream processing, each new piece of data is processed when it arrives. For example, data ingestion is inherently a streaming process.\n\nStreaming handles data in real time. Unlike batch processing, there's no waiting until the next batch processing interval, and data is processed as individual pieces rather than being processed a batch at a time. Streaming data processing is beneficial in most scenarios where new, dynamic data is generated on a continual basis.\n\nA fraud department would use stream processing to handle real-time fraud and anomaly detection.\n\n Note\n\nStream processing is ideal for projects that require real-time analysis, and is less suited for projects requiring complex analytics.\n\nWhile data processing typically occurs upstream of the analytical data store, it's critical that analysts understand how data are ingested and at what frequency, to build the appropriate analytics solution.\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Introduction - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/1-introduction",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nAdd\nIntroduction\n2 minutes\n\nIn the era of data as currency, companies are looking to do more with their data. Distilling large volumes of data into useful insights is key. There are many ways to store, ingest and process, and analyze data in the Azure ecosystem.\n\nYou've been hired as an analyst on the data team at a Relecloud, a new communications company. Relecloud's CEO has emphasized that they'll only make data-informed decisions; you're going to be busy! Because the company is new, the existing volume of data feels manageable, but you know from experience it will grow quickly. The CEO has tasked the data team with implementing a modern analytics solution, as soon as possible.\n\nThis module introduces the Azure data ecosystem and modern enterprise analytics solutions. You'll learn about data storage, data ingestion and processing, and data analytics in Azure.\n\nLearning objectives\n\nIn this module, you will:\n\nExplore the Azure data ecosystem for analytics\n\nNeed help? See our troubleshooting guide or provide specific feedback by reporting an issue.\n\nFeedback\n\nWas this page helpful?\n\nYes\nNo\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  },
  {
    "title": "Explore Azure data services for modern analytics - Training | Microsoft Learn",
    "url": "https://learn.microsoft.com/en-us/training/modules/explore-azure-data-services-for-modern-analytics/",
    "html": "Skip to main content\n\tWe use optional cookies to improve your experience on our websites, such as through social media connections, and to display personalized advertising based on your online activity. If you reject optional cookies, only cookies necessary to provide you the services will be used. You may change your selection by clicking “Manage Cookies” at the bottom of the page. Privacy Statement Third-Party Cookies\nAccept Reject Manage cookies\nLearn\nDocumentation\nTraining\nCredentials\nQ&A\nCode Samples\nAssessments\nShows\nSign in\nTraining\nProducts\nCareer Paths\nBrowse all training\nEducator Center\nStudent Hub\nFAQ & Help\nExplore Azure data services for modern analytics\nModule\n5 Units\nFeedback\nIntermediate\nData Analyst\nAzure\nAzure Synapse Analytics\nPower BI\n\nUnderstand analytics solutions in the Azure data ecosystem. Explore the architecture of a scalable analytics solution to meet business needs.\n\nLearning objectives\n\nAfter completing this module, you will be able to:\n\nDescribe the Azure data ecosystem for analytics\nAdd\nPrerequisites\nYou should be familiar with basic data concepts and terminology.\nIntroduction\nmin\nUnderstand the Azure data ecosystem\nmin\nExplore modern analytics solution architecture\nmin\nKnowledge check\nmin\nSummary\nmin\nEnglish (United States)\nYour Privacy Choices\nTheme\nManage cookies\nPrevious Versions\nBlog\nContribute\nPrivacy\nTerms of Use\nTrademarks\n© Microsoft 2024"
  }
]