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MS_Azure_Data_Share_L300_Customer_Deck.pptx

  1. Azure Data Share Simple and secure service for sharing big data
  2. Cross organization big data collaboration Retail Sales, inventory, demographics data for demand forecasting and price optimization Finance Financial market data for quantitative analytics Utilities Utility data for research on conservation, alternative energies Farming Field sensors, crop yields, weather data for smart agriculture Automotive Connected car IOT data for personalized experiences and failure analysis Healthcare Healthcare, student data for research Government Education Patient data for research and prevention Traffic, crime data for planning and justice
  3. How data is shared today Data consumer #1 Data consumer #2 Data consumer #3 Sends via email or USB Copies to FTP server APIs or web app Extracts data Data provider Difficult to manage, track, and not suitable for big data
  4. Azure Data Share Secure and controlled Manage what data is shared and with who. No exchange of credentials between provider and consumer Flexible Share by snapshot or in- place, from and to different Azure data store Code free data sharing with just a few clicks. No infrastructure to set up Enhance analytics Use the power of Azure analytics tools to enhance insights with shared data Easily share data
  5. Easily share data Simple • Share data cross tenant with a few clicks • Intuitive user experience • No infrastructure to setup and no code to write Productive • Focus on data, not infrastructure • Schedule automated incremental updates with granular control (hourly, daily) • Automate sharing through REST API Designed for big data • Scales to handle big datasets • Share terabytes of data in a single share with multiple recipients
  6. Flexible Snapshot and in-place • Snapshot-based sharing for batch processing • In-place sharing for real time access Different Azure data stores • Blob storage, ADLS Gen1 and Gen2, Azure SQL DB, SQL DW, Azure Data Explorer • Heterogenous source and target (e.g. table to file) Various data formats • Share both structured and unstructured data • File systems, folders, files • Containers, blobs • Databases, tables, views
  7. Secure and controlled Manage • Manage all data sharing relationships in one place • Visibility into what data is shared, who it is shared with and when data is sent Control • Set terms of use, data consumer must accept to receive data • Revoke access and stop sharing at any time • Logs and metrics to track sharing activities Secure • Leverages underlying Azure security measures to help protect data • AAD-based authentication • Data is encrypted in transit; metadata is encrypted at rest and in transit • No exchange of credentials between data provider and consumer
  8. Expand analytics Enrich • Enhance insights in the modern data warehouse with data from partners and customers Collaborate • Form industry specific consortium to pool data among members Innovate • Integrate into custom solutions; expand market via new service capabilities Azure Data Factory Azure Databricks (Data Prep) Azure Data Lake Storage Azure Synapse Analytics Power BI Ingest & Prep Store Model & Serve Visualize Azure Data Share Company A Azure Data Share Share Azure Data Share Company B Azure Data Share Company C Azure Data Share Azure Data Share
  9. How data share works Source store Target store Data provider Data consumer Invitation In-place access Snapshot Intra and cross tenant Data provider Data consumer accepts share​ Starting with Blob, ADLS, Azure SQL DB, Azure Synapse Analytics, and Azure Data Explorer
  10. Supported Azure data stores Source Target Blob Storage ADLS Gen1 ADLS Gen2 Azure SQL DB Azure Synapse Analytics Azure Data Explorer Blob Storage Snapshot Snapshot ADLS Gen1 Snapshot Snapshot ADLS Gen2 Snapshot Snapshot Azure SQL DB Snapshot Snapshot Snapshot Snapshot Azure Synapse Analytics dedicated SQL pool Snapshot Snapshot Snapshot Snapshot Azure Data Explorer In-place
  11. How customers are using Azure Data Share
  12. Cross organization big data analytics Sales data Sales data Sales data from other partners Inventory data Sales data Analyze Sales data
  13. Share data collected on behalf of customer Connected devices Stream Share
  14. Analytics outsourcing Raw data Insights Insights Proprietary Analytics App Raw data
  15. Industry specific data consortium Patient data
  16. Data monetization and marketplace Browse and purchase data Data distributed to customer Automate sharing via Data Share API
  17. Inter-departmental data sharing within an organization
  18. Accelerate innovation via open ecosystem “Our decision to integrate Azure Data Share with Finastra’s FusionFabric.cloud platform is now a great way to further accelerate innovation via an expanded open ecosystem.” Eli Rosner, Chief Product and Technology Officer, Finastra Finastra, one of the worlds' leading FinTechs, is fully integrating Azure Data Share with their open platform, FusionFabric.cloud, to enable seamless distribution of premium datasets to a wider ecosystem of application developers across the FinTech value chain. Azure Data Share significantly reduces the go to market timeframe and unlocking net new revenue potential for Finastra.
  19. Streamline buy-side data analysis “Our clients love that ability to easily, seamlessly, and securely connect to their data and then build their own custom reports and analytics. And near real-time sharing with Azure Data Explorer and Azure Data Share permits cross- organizational data collaboration without compromising data security.” Paul Stirpe, Chief Technology Officer, Financial Fabric
  20. Improve analytics agility across the company “We are currently managing 9 trillion rows of data with a total original size of 938TB in Azure Data Explorer. With many different efforts going on within DTNA, the challenge has been making the data available to each group without making multiple copies or extracts. For these large sensitive datasets, this would not only increase cost, but risk losing control of the data. Azure Data Share has solved the problem for us, allowing us to maintain one instance, our single source of truth, then add decoupled compute clusters provisioned to just the data needed by each group. This avoids over sharing as well as competition for resources from other users accessing the same data.” Sammi Li, Data Analyst, Daimler Trucks North America (DTNA)
  21. Next steps 1. Visit the Azure Data Share product page 2. Access documentation, quick starts, and tutorials 3. Get started with Azure Data Share
  22. Features and capabilities
  23. Azure Data Share features
  24. Blob and ADLS snapshot-based sharing
  25. SQL snapshot-based sharing
  26. Azure Data Explorer in-place sharing architecture Data Provider Cluster (source) DB1 DB2 DB3 DB3 DB4 Data Consumer Cluster (target)
  27. Azure Data Explorer in-place sharing features
  28. Logging and Metrics • Create/update/delete Data Share resources • Create/update/delete shares, datasets, snapshot schedules, invitations • Revoke access Activity Log • Share • Share subscriptions • Snapshot history Diagnostic Log • Share Count • Share Subscription Count • Succeeded/failed snapshots Metrics
  29. Service limits Azure subscription limits and quotas - Azure Resource Manager | Microsoft Docs Resource Limit Maximum number of Data Share resources per Azure subscription 100 Maximum number of sent shares per Data Share resource 200 Maximum number of received shares per Data Share resource 100 Maximum number of invitations per sent share 200 Maximum number of share subscriptions per sent share 200 Maximum number of datasets per share 200 Maximum number of snapshot schedules per share 1
  30. Snapshot-based sharing pricing https://azure.microsoft.com/pricing/details/data-share/
  31. © Microsoft Corporation Supported Azure locations https://azure.microsoft.com/global-infrastructure/services/?products=data-share where snapshot execution compute resource is located
  32. Access control and Security
  33. Role-based Access Control (RBAC) Who can create Data Share resource? Owner or Contributor of an Azure subscription Who can access Data Share resource? Owner, Contributor or Reader of the Data Share resource • Owner or Contributor can manage any share within a Data Share resource • Reader can view shares within a Data Share resource Who can share data from a source Azure data store? User/service principal with Add Role Assignment and Write permission (e.g. Owner of storage account) Who can receive data into a target Azure data store? User/service principal with Add Role Assignment and Write permission (e.g. Owner of storage account) Reference: https://docs.microsoft.com/azure/data-share/concepts-roles-permissions
  34. © Microsoft Corporation No exchange of credentials between data provider and consumer • Data Share resource Managed Identities are used to read/write data Role assigned to Data Share resource Managed Identity Data Store Type Data Provider Source Data Store Data Consumer Target Data Store Azure Blob Storage Storage Blob Data Reader Storage Blob Data Contributor Azure Data Lake Gen1 Owner Not Supported Azure Data Lake Gen2 Storage Blob Data Reader Storage Blob Data Contributor Azure Data Explorer Cluster Contributor Contributor
  35. View role assigned to Data Share resource Go to Access Control->Role Assignments of Azure data store
  36. © Microsoft Corporation Snapshot-based sharing data flow Provider’s Data Share resource Azure Data Share service Data stores Read Write Consumer’s Data Share resource Data stores Data path Snapshot
  37. © Microsoft Corporation In-place sharing data flow Provider’s Data Share resource Azure Data Share service Data stores Consumer’s Data Share resource Data stores Data path In-place access
  38. Snapshot-based sharing user experience
  39. Control Flow Data Flow Data Provider Data Consumer Source Data Azure Data Store Data Provider Azure subscription Data Consumer Azure subscription Received Data 1 Azure Data Store Data Share Resource Create share, add source datasets, schedule and recipients 2 View email Click on email link to login to Azure, create or select Data Share resource, accept invitation and configure target data store Login to Azure and create Data Share resource 2 Monitor invitation and snapshot status 9 Monitor snapshot status 4 5 6 8 Data Share Resource Create share, add source datasets, schedule and recipients Send email invite to data consumer admin Copy data per snapshot schedule 7 Receive share, configure target data store 6 Azure Data Share Service Copy data per snapshot schedule 7 Snapshot-based sharing user experience
  40. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  41. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  42. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  43. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview storageaccount/product storageaccount/product
  44. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  45. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  46. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  47. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received shares Monitors snapshots Sally receives invitation Overview John@contoso.com John@contoso.com
  48. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received shares Monitors snapshots Sally receives invitation Overview
  49. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received shares Monitors snapshots Sally receives invitation Overview Sally@fabrikam.com Fabrikam Sally@fabrikam.com Fabrikam
  50. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview john@contoso.com
  51. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  52. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received shares Monitors snapshots Sally receives invitation Overview
  53. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview John@contoso.com Contoso This is terms of use Sally@fabrikam.com
  54. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  55. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received share Monitors snapshots Sally receives invitation Overview
  56. John sets up Data Share Account Creates new share Views list of sent shares Views details of sent share Monitors usage of shared data Views and accepts invitation Configures received shares Monitors snapshots Sally receives invitation Overview

Notes de l'éditeur

  1. In a world where data volume, variety and type are exponentially growing, organizations need to collaborate using large datasets. In many cases data is at its most powerful when it can be shared and combined with data that resides outside organizational boundaries with business partners and third parties. Microsoft is investing in data sharing because customers all over the industries are looking to share data with their partners and customers. We have customers approaching us from retail, automotive, utilities, farming, finance, healthcare, education and government sectors. A typical scenario is to share data with partners or customers, so that their partners or customers can combine this data with their own data, or other data from third party to run analytics to derive insights.
  2. For customers, sharing this data in a simple and governed way is challenging. Common data sharing approaches using FTP or web APIs are complex and require infrastructure to manage and knowledge of code. These tools do not provide the security or governance required to meet enterprise standards, and they are not suitable for sharing large datasets. To enable enterprise collaboration we are unveiling Azure Data Share, a new data management service for sharing big data across external organizations in Azure. Majority of our customers are looking to share time series data, which gets updated on regular basis (e.g. on daily basis, new files are generated). If you look at how data is shared today, FTP, secure FTP, APIs or web apps are the most popular way of sharing data today. However, they require set up and maintenance. Some customers are sharing data through email, USB stick, tapes, which are not trackable, and not efficient for on-going data sharing. All these technologies are not suitable for sharing large amount of data.
  3. heterogenous support – i.e a data provider may share data in ADLS but the consumer may opt to receive it in Azure Blob Storage
  4. Collaborate with large datasets Combine existing data with shared data to enrich analytics use cases for deeper insights Enhance insights in the modern data warehouse Azure storage integrates with other Azure analytics services for preparing, processing, and analyzing data In many cases data is at its most powerful when it can be shared and combined with data that resides outside organizational boundaries with business partners and third parties. Azure Data Share enables enterprise collaboration across organizational boundaries.
  5. Cross Organization Big Data analytics. In all industries we have seen needs for data sharing between partners. For example, retailers share sales data with consumer goods suppliers who then use the data to do demand forecasting. In automotive space, we have seen car OEMs sharing IOT data with service providers. Oil and gas companies are sharing data with equipment and infrastructure providers. In precision agriculture, service providers deploy sensors to the field and share soil data with farmers to make watering/fertilizing decisions. In financial industries, index data, transaction data are shared to financial institutions and hedge funds, and sometimes monetized. In health care and education sector, anonymous patient data is shared to research cure for diseases. Governments are sharing data between agencies and with commercial companies. Analytics outsourcing is another scenario we have heard from our customers. Some of our customers do not have the expertise or other datasets required to analyze the data to derive insights, so they outsource it to a service provider, who will analyze the data and provide results back. This resulted in two data sharing. One from the data owner to the service provider, and one from the service provider to the data owner. Another scenario we have heard from our customer is industry-specific data consortium. For example, in the healthcare/education sector, data is shared with the members of the consortium to conduct research on disease. The data can also potentially be sold to pharmaceutical companies for a fee. Data marketplace is a scenario we have heard from a number of customers. These companies will create their own marketplace storefront, where their customers will discover the datasets. Once the purchase is made, Data Share service will be used to automate the process of data distribution and tracking. In this case, the company who owns the marketplace will be leveraging the Data Share API for bulk data sharing.
  6. Cross industry, data sharing through supply chain
  7. One example is IOT data collected by the service provider. Another example is transaction data in financial industry collected by the bank.
  8. e.g. Patient data
  9. Data share resource is where you can create a Data Share resource in Azure. This is where metadata about the resource is located. Snapshot execution is where the compute resource is located to copy data from source storage account to target storage account
  10. Data Share resource and storage accounts do not need to collocate in the data center. For example, data share source can locate in East US 2, where storage account is in West US.
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