Microsoft Azure BI Solutions in the Cloud
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This document provides an overview of several Microsoft Azure cloud data and analytics services: - Azure Data Factory is a data integration service that can move and transform data between cloud and on-premises data stores as part of scheduled or event-driven workflows. - Azure SQL Data Warehouse is a cloud data warehouse that provides elastic scaling for large BI and analytics workloads. It can scale compute resources on demand. - Azure Machine Learning enables building, training, and deploying machine learning models and creating APIs for predictive analytics. - Power BI provides interactive reports, visualizations, and dashboards that can combine multiple datasets and be embedded in applications.
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Microsoft Azure BI Solutions in the Cloud
- 1. Building Cloud BI Solutions Microsoft Azure Cortana Intelligence Suite Mark Kromer Microsoft Azure Cloud Data Architect @kromerbigdata @mssqldude
- 2. Microsoft Azure Data Services for Data Engineers, Data Scientists, Data Analytics Cortana Intelligence Suite Azure Data Platform-at-a-glance
- 3. Action People Automated Systems Apps Web Mobile Bots Intelligence Dashboards & Visualizations Cortana Bot Framework Cognitive Services Power BI Information Management Event Hubs Data Catalog Data Factory Machine Learning and Analytics HDInsight (Hadoop and Spark) Stream Analytics Intelligence Data Lake Analytics Machine Learning Big Data Stores SQL Data Warehouse Data Lake Store Data Sources Apps Sensors and devices Data
- 5. What it is: When to use it: A pipeline system to move data in, perform activities on data, move data around, and move data out • Create solutions using multiple tools as a single process • Orchestrate processes - Scheduling • Monitor and manage pipelines • Call and re-train Azure ML models
- 8. Example – Customer Churn Call Log Files Customer Table Call Log Files Customer Table Customer Churn Table Azure Data Factory: Data Sources Customers Likely to Churn Customer Call Details Transform & Analyze PublishIngest
- 9. What it is: When to use it: A Scaling Data Warehouse Service in the Cloud • When you need a large-data BI solution in the cloud • MPP SQL Server in the Cloud • Elastic scale data warehousing • When you need pause-able scale-out compute
- 10. Elastic scale & performance Real-time elasticity Resize in <1 minute On-demand compute Expand or reduce as needed Pause Data Warehouse to Save on Compute Costs. I.e. Pause during non-business hours
- 11. Storage can be as big or small as required Users can execute niche workloads without re-scanning data Elastic scale & performance Scale
- 12. Logical overview
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- 14. What it is: When to use it: A multi-platform environment and engine to create and deploy Machine Learning models and API’s • When you need to create predictive analytics • When you need to share Data Science experiments across teams • When you need to create call-able API’s for ML functions • When you also have R and Python experience on your Data Science team
- 15. Creating an Experiment Get/Prepare Data Build/Edit Experiment Create/Update Model Evaluate Model Results Build and ModelCreate Workspace Deploy Model Consume Model
- 16. Basic Azure ML Elements Import Data Preprocess Algorithm Train Model Split Data Score Model
- 18. What it is: When to use it: Interactive Report and Visualization creation for computing and mobile platforms • When you need to create and view interactive reports that combine multiple datasets • When you need to embed reporting into an application • When you need customizable visualizations • When you need to create shared datasets, reports, and dashboards that you publish to your team
- 22. What is ETL? • Acronym for “Extract, Transform and Load” • Classic form of data movement, aggregation, summarization, cleansing and loading a Data Warehouse • More loosely defined as data management processes that clean, move and aggregate data • Formal ETL processes are typically scheduled (i.e. hourly, nightly, monthly) • Not real-time, although micro-batch ETL systems are quite common • Full story: http://sqlmag.com/sql-server-integration-services/etl-cloud-azure
- 23. Microsoft and ISV Marketplace common offerings (Examples) Spin-up SQL Server VM image from the Azure Portal to run SSIS in the cloud via Azure IaaS Informatica is an Enterprise- grade ETL product suite that offers an Azure VM available in the ISV Marketplace Microsoft partner with Azure ISV Marketplace offerings including CDC. Attunity Compose can provide additional ELT/ELT capabilities.
- 24. ADF provides Extract, Transform and Load in the Cloud
- 25. Transforming Data is a common task for Data Scientists and Data Engineers Data Transformations
- 28. Business apps Custom apps Sensors and devices Bulk Load Azure Data Factory
- 29. Business apps Custom apps Sensors and devices Events Events Azure Data Lake Store Transformed Data Raw Events Azure Event Hubs Kafka
- 30. Data Transformation Data Collection Presentation and action Queuing System Data Storage Azure Search Data analytics (Excel, Power BI, Looker, Tableau) Web/thick client dashboards Devices to take action Event hub Event & data producers Applications Web and social Devices Live Dashboards DocumentDB MongoDB SQL Azure ADW Hbase Blob StorageKafka/RabbitMQ/ ActiveMQ Event hubs Azure ML Storm / Stream Analytics Hive / U-SQL Data Factory Sensors Pig Cloud gateways (web APIs) Field gateways
- 32. Cortana Intelligence Solutions: Discover http://aka.ms/cisolutions
- 33. Cortana Intelligence Solutions: Try
- 34. Cortana Intelligence Solutions: Deploy