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Unify all your data sources and extract powerful insights using self-service data prep capabilities in Microsoft Power BI. This session by Miguel Llopis and Matthew Roche provides a quick review of modern BI challenges such as fragmented data, complex integration, and data inconsistency. Learn how Power BI addresses these challenges through self-service capabilities, dataflows, cloud connectors, and more. Discover how to leverage Azure Data Platform for analytics and simplify data preparation. This session also covers the dynamics between Power BI, Azure services, and data scientists, engineers, and analysts. Take a deeper look at dataflows, data models, and storage options, and see how Power BI Dataflows streamline data preparation tasks similar to Excel. Explore the session to understand the importance of data unification, self-service capabilities, and modern BI tools to enhance your organization's analytics workflow.
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Microsoft Power BI: Unify all your data and deliver powerful insights with self-service data prep capabilities for big data Miguel Llopis Principal Program Manager Matthew Roche Senior Program Manager BRK2061
Reviewing Business Intelligence solutions Self-Service BI Data Warehouse Reports and dashboards OLTP systems Data Lake OLAP / Analytics Models Data Preparation / ETL
Modern BI challenges • Fragmented, incomplete data • Pulling together data from traditional and cloud data sources, and figuring out how to enrich it is extremely difficult. Requires a team of specialists Creating E2E BI solutions requires multiple BI tools. This requires specific knowledge of each of the tools and complex integration to build and maintain an E2E BI solution. Business data has no structural or semantic consistency Different applications, departments, and analysts define data in different ways, which makes data exploration, and reuse of data and apps extremely challenging. Complex system integration Traditional BI solutions span multiple applications and services. Sharing data across systems requires each system to understand the location, structure and meaning of the data.
Demo A quick tour of self-service data prep in Power BI
Self-service low code/no code • Integral part of Power BI stack Cloud and on-premises connectors Power BI introduces self-service data-prep capabilities Standard schema (Common Data Model) In-laketransformations Data reuse
Power BI introduces self-service data-prep capabilities Self-service low code/no code • Integral part of Power BI stack Cloud and on-premises connectors Standard schema (Common Data Model) Dataflows • In-laketransformations Data reuse
Power BI introduces dataflows Reports & dashboards Visualizations Datasets BI models Dataflows Data prep Storage Data (Azure Data Lake) Gateways and connectors
Modern BI challenges • Fragmented, incomplete data • Pulling together data from traditional and cloud data sources, and figuring out how to enrich it is extremely difficult. Requires a team of specialists Creating E2E BI solutions requires multiple BI tools. This requires specific knowledge of each of the tools and complex integration to build and maintain an E2E BI solution. Business data has no structural or semantic consistency Different applications, departments, and analysts define data in different ways, which makes data exploration, and reuse of data and apps extremely challenging. Complex system integration Traditional BI solutions span multiple applications and services. Sharing data across systems requires each system to understand the location, structure and meaning of the data.
Demo A deeper look at dataflows
Recap Reports & dashboards • Datasets • Dataflows • Storage
But what if… …we want to leverage the data created by the dataflow in applications or services outside of Power BI? Reports & dashboards • Datasets • Dataflows • Storage
Data + AI professionals can use the full power of the Azure Data Platform Reports & dashboards Azure SQL DW Azure Data Factory Azure Databricks Azure ML Datasets Dataflows • Azure Data Lake Storage Gen2 CDM folder CDM folder CDM folder Data scientists Data engineers Low to high code Business analysts Low/no code
But what if… …we want to leverage the data created by the dataflow in applications or services outside of Power BI? …or to provide an easy way to consume analytical data created outside of Power BI, in Power BI? Reports & dashboards • Datasets • Dataflows • Storage
Deliver ready-made insights to Power BI users from Azure Dynamics 365 for Finance & Operations Self service customizations in Power BI Azure SQL DW Azure Data Factory Azure Databricks Azure ML • Dynamics 365 data • Dataflow • Azure Data Lake Storage Gen2 CDM folder
But what if… …we want to leverage the data created by the dataflow in applications or services outside of Power BI? …or to provide an easy way to consume analytical data created outside of Power BI, in Power BI? …or to eliminate the complexity of data prep orchestration? Reports & dashboards • Datasets • Dataflows • Storage
Power BI Dataflows are like Excel The difficult part of data preparation is usually not the individual job, package, or query – it is ensuring all of the jobs, packages, and queries run as a logical and reliable system Each Dataflow Entity has a formula The Power BI Dataflow calculation engine understands the relationships between all entities Update one, the rest update automatically and consistently – just like Excel
Power BI Dataflows are like Excel Sources 20 Ingest from Dynamics Sales 22 entities Clean and enrich sales data 10 entities 8 CRM – Production Dynamics 365 10 Final Business View 11 entities 4 1 1 Product Telemetry in Azure 5 entities Add Telemetry Customer Attributes 6 entities IoT Signal Azure Data Lake Storage 4 1 Azure Analysis Services Sales/Telemetry Reference Data Dataflow
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