Power BI Modeling Use Cases: Desktop to Enterprise
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Learn the different ways Power BI lets you source and prep data for analysis. We examine four different scenarios and their impacts on scalability, security and performance. Scenarios include: directly querying operational systems, querying the data warehouse, dataflows and datasets. Senturus offers a full spectrum of services in business intelligence and training on Tableau, Power BI and Cognos. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
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Power BI Modeling Use Cases: Desktop to Enterprise
- 1. Power BI modeling use cases: Desktop to enterprise Four different ways to source and prepare your data 1
- 2. Agenda • Introduction • Power BI overview • Query methods with pros and cons • Senturus overview • Additional resources • Q&A 2
- 3. 3 Hundreds of resources Visit the Resource Library on the Senturus website to download this presentation and explore other assets: senturus.com/resources 3
- 4. 4 Introduction Michael Weinhauer Director of Training and Content Senturus, Inc. 4
- 5. Like what you see? This slide deck is from the webinar Power BI Modeling Use Cases: Desktop to Enterprise To view the FREE video recording and download this deck, go to https://senturus.com/resources/power-bi-modeling-use- cases-desktop-to-enterprise/ 5
- 6. The rise of self-service StorePrepare Model Visualize Share 6 Can one tool do it all????
- 7. Microsoft BI offerings – through the years Excel SQLServer Access Integration Services AnalysisServices(M D) ReportingServices Perform ancePoint PowerQuery PowerPivot PowerView AnalysisServices(Tab) PowerBI 87 89 92 98 98 04 07 10 10 10 12 15 Prepare ? ? x x x x Store ? x x x x x x Model ? ? x x x x Visualize (reports) ? x x x x Visualize (dashboards) x x x Share x x x In-memory x x x x Self-service x x x x x x 7
- 8. • A self-service analytics solution that lets you prepare and present data for your organization • Power BI serves five essential functions: • Power BI is actually a set of integrated tools: •Power BI Desktop •Power BI Service •Power BI Report Server •Power BI Mobile What is Power BI? ©2019 Senturus, Inc. All rights reserved. 8 Query engine In-memory data storage Modeling tool Visualization tool Deployment portal •Power BI Embedded •Power BI Data Gateway •…and Excel (???)
- 9. Power BI core concepts and workflow 9 Data Sources Model Report Power BI Desktop Queries • Datasets consist of two object types: – Queries - defines how to extract and transform data sources, accessed via connectors – Model - defines tables, relationships, columns and measures • Reports present data from datasets on one or more pages of visuals • Dataflows pull data from sources, performs transforms and loads into Azure (ADLG2) • Datasets are published, analysis-ready models that are reusable and certifiable • Dashboards present visuals, pinned from reports • On-Premise Data Gateways let the service connect back to data sources Power BI service Dataset Gateways
- 10. Tradeoffs and considerations 10 • Real-time vs. latent • Store once vs. multiple copies • Analysis ready vs. transactional • Cost • Performance • Scalability • On-prem vs. cloud • Governance vs. agility • Security
- 11. Query methods - definitions 11 • DirectQuery – live query to data modeled in Power BI • Live – live query to pre-modeled data (datasets or SSAS) • Import – data is pulled from source into Power BI data model • Composite/dual – aggregate data is pulled into an import; query engine determines if Import or DirectQuery is used
- 12. Option #1 – direct connection to transactional data 12 Data Sources Power BI service Model Report Power BI Desktop Queries Dataset You are HERE Gateways
- 13. Option #1 – direct connection to transactional data 13 OR Model Report Power BI Desktop Queries Dataset Model Report Power BI Desktop Queries Dataset
- 14. Option #1 – direct connection to transactional data 14 • Pros: real-time, no data redundancy, low cost, agile, powerful • Cons: performance, system impact, effort, low reuse, point in time (lacks history, trending, change data capture) • Ideal for: small groups, real-time use cases, prototypes • Methods: DirectQuery, import or composite/dual • BI maturity: beginner
- 15. Option #2 – connect to EDW 15 Data Sources Model Report Power BI Desktop Queries Power BI service Dataset You are HERE Gateways
- 16. Option #2 – connect to EDW 16 Model Report Power BI Desktop Queries Dataset
- 17. Option #2 – connect to EDW 17 • Pros: easy, designed for analysis, governed, performant, scales, history, trends, change capture, reusable, aligned business metrics • Cons: latency, recency, cost, unagile, completeness • Ideal for: medium/large enterprises, governed data needs • Methods: DirectQuery, import or composite/dual • BI maturity: intermediate
- 18. Enjoy the full webinar presentation To view the video recording and download the slide deck go to https://senturus.com/resources/power-bi- modeling-use-cases-desktop-to-enterprise/ Visit our website to access our library of free BI knowledge resources including events, blogs, demos, whitepapers, other on-demand webinars and our dashboard gallery https://senturus.com/senturus- resources/ 18
- 19. Option #2a – connect to SSAS 19 Model Report Power BI Desktop Queries Dataset
- 20. Option #2a – connect to SSAS 20 • Pros: easy, designed for analysis, governed, performant, scales, history, trends, change capture, reusable, aligned business metrics • Cons: latency, recency, cost, unagile, completeness • Ideal for: medium/large enterprises, governed data needs • Methods: live • BI maturity: intermediate
- 21. Option #3 – shared datasets 21 Data Sources Model Report Power BI Desktop Queries Power BI service Dataset You are HERE Gateways
- 22. Option #3 – shared datasets 22
- 23. Option #3 – shared datasets 23 • Pros: governed, certifiable/promotable, performant, reusable, reduced impact on sources, aligned business metrics • Cons: governance need/consequences of lack of governance, agility • Ideal for: sharing data across similar reporting use cases • Methods: live • BI maturity: intermediate
- 24. 24 Data Sources Model Report Power BI Desktop Queries Power BI service Dataset You are HERE Gateways Option #4 – dataflows
- 25. Option #4 – dataflows 25 Power BI service Azure Data Lake Gen 2 Storage Model Report Power BI Desktop Queries Dataset Gateways
- 26. 26 • Pros: transformation reuse, governance, performance, agility • Cons: recency, data duplication • Ideal for: orgs wanting to move from desktop to cloud/enterprise, governed reporting use cases • Method: import • BI maturity: pro Option #4 – dataflows
- 27. Summary comparison matrix 27 Your mileage may vary QueryMethod ModelResides Performance Cost Recency DataRedundancy Reusability Security Governance Agility Ease Transactional DirectQuery Power BI Desktop Varies Low High No Low Low Low High Low Import/Composite Power BI Desktop High Low Med Yes Low Low Low High Low ODS/Data Lake DirectQuery Power BI Desktop Varies Med Med Yes Low High Low Med Low Import/Composite Power BI Desktop High Med Med Yes Low Low Low Med Low EDW DirectQuery Power BI Desktop High High Low Yes Med High High Low Med Import/Composite Power BI Desktop High High Low Yes Med Med Med Med Med SSAS Live SSAS High Med Low Yes Med High High Low High Import Power BI Desktop High Med Low Yes Med Med High Med High Datasets Live Power BI Service High Low Varies No High Med Med Low High Dataflow Import Power BI Service High Low Low Yes High Med High Med Med
- 28. Use cases by approach 28 QueryMethod BestPractice UseCase Direct DirectQuery 1 no budget, real time Import/Composite 2 no budget, reduce impact ODS/Data Lake DirectQuery 3 frequently landed data or detail level Import/Composite 4 prototyping or no EDW EDW DirectQuery 5 frequent ETL or detail level reporting Import/Composite 5 high quality models SSAS Live 6 enterprise solution, scaling, optimization Datasets Live 6 sharing datasets across reports Dataflow Import 6 sharing data sources across datasets
- 29. Which is best for you? 29 • Some/all of the above • Depends on size, use case(s) • Balance of factors • Iterative process – morphs over time Answer (Check all that apply): A, D, and E A, D, E, and F A, D, E, F, G All of the Above Other
- 30. Enjoy the full webinar presentation To view the video recording and download the slide deck go to https://senturus.com/resources/power-bi- modeling-use-cases-desktop-to-enterprise/ Visit our website to access our library of free BI knowledge resources including events, blogs, demos, whitepapers, other on-demand webinars and our dashboard gallery https://senturus.com/senturus- resources/ 30
- 31. Power BI mentoring session •Focus on the issues that have the biggest impact to your organization, including • Speed up dashboard creation • Reuse data sets • Share dashboards across departments • Perform rapid, on-the-fly recalculations • Reduce errors from manual data manipulation • Restrict access to sensitive data •Learn more •https://Senturus.com/power-bi-desktop-to-enterprise 31
- 32. The authority in Business Intelligence Exclusively focused on BI, Senturus is unrivaled in its expertise across the BI stack 32
- 33. Decisions & actionsBusiness needs Bridging the data & decisioning gap 33 Analysis-ready data
- 34. Full spectrum of BI services • Dashboards, reporting and visualizations • Data preparation and modern data warehousing • Hybrid BI environments (migrations, security, etc.) • Software to enable bimodal BI and platform migrations • BI support retainer (expertise on demand) • Training and mentoring 34
- 35. A long, strong history of success • 19+ years • 1300+ clients • 2500+ projects 35
- 36. Expand your knowledge Find more resources on the Senturus website: senturus.com/senturus-resources 36
- 37. Upcoming events •Complex Aggregations in Tableau •Leveraging table and level of detail calculations •Thursday, Mar. 26, 2020, 11am PT/2pm ET •What’s New in Cognos 11.1.6 •Live Q&A with IBM offering manager •Thursday, Apr. 16, 2020, 11am PT/2pm ET •Tableau Prep: A Visualization's BFF •Overview of self-service data prep for Tableau •Thursday, Apr. 23, 2020, 11am PT/2pm ET 37
- 38. Complete BI training offerings 38 Instructor-led online courses Self-paced learning MentoringTailored group sessions
- 39. Additional resources from Senturus 39 Insider viewpointsTechnical tipsUnbiased product reviews Product demos Upcoming eventsMore on this subject
- 40. © 2020 by Senturus, Inc. This presentation may not be reused or distributed without the written consent of Senturus, Inc. www.senturus.com 888 601 6010 info@senturus.com Thank You Contact Kay Knowles kknowles@senturus.com 303 330 7321
Notas do Editor
- Welcome! Thanks for joining us in the latest installment of the Senturus business analytics education series. I’m Michael Weinhauer your host for today…give background…
- Today’s agenda. After a brief introduction of the speakers, we’ll get right into the topic. Stay on after the presentation when we will We’ll then share useful links, provide a quick overview of Senturus and let you know of upcoming events. We’ll wrap with a live Q&A.
- The first question we usually get is “Can I get a copy of the presentation?” Absolutely! It’s available on Senturus.com. Select the Resources tab and then Resources Library. Or you can click the link that was just posted in the GoToWebinar Control panel. Be sure to bookmark the resource library. It has tons of valuable content addressing a wide variety of business analytics topics.
- I’m an eager analyst in a small company (business unit within a larger company). I have business data sitting in a bunch of flat files and I’m not going to bother IT with any of this yet (e.g. resources, politics, competence). I have a lot to figure out! How am I going to: Prepare, Store, Model, Visualize, and Share? On top of all that, how am I going to: Have this perform super fast (perhaps by storing data in-memory)? Do it myself (self-service)?? Can one tool do It all? This used to be a disjointed process, with upstream data prep and modeling handled by multiple people, often in IT.
- Note iterative capability where historically ETL, Modeling, Reporting/Visualization and Deployment were separate and distinct roles, many often handled by IT. Power BI and modern tools allow for rapid iteration. Concepts can be prototypes, changes made quickly and incorporated at the report, then into the model layer, and ultimately deployed and/or pushed further upstream into EDW.
- Cost vs Performance vs Governance Like an Orchestra Bulls eye? Why you should care? The true cost Transportation analogy RIGHT TOOL FOR THE JOB
- Distinction between DirectQuery, In-Memory, Import etc for the SOURCE (i.e., SSAS, Dataset) vs Power Query usage. For purposes of this webinar, we generally are referring to the query as it is executed by Power BI desktop/report authoring environment Composite models currently combine import w DirectQuery, however MSFT is enhancing this capability to allow more than one DirectQuery connection in a model.
- Direct connection to the transactional data not modeled for analysis not suggested/best practice will possibly affect the transactional system Upwards of 20-30 THOUSAND Tables,3NF, etc Ideal For: small organizations or teams, small data volumes, prototypes Method: DirectQuery, Import, or Dual DirectQuery can be against transactional DB or against SSAS cube configured as DirectQuery against transactional DB Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the transactional DB during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.)
- Direct connection to the transactional data not suggested/best practice will possibly affect the transactional system typically not modeled for analysis can be DirectQuery or import DirectQuery can be against transactional DB or against SSAS cube configured as DirectQuery against transactional DB Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the transactional DB during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Ideal For: smalerl organizations or teams, real-time/operational use cases, rapid prototypes Method: DirectQuery, Import, or Dual (Can also be “extract/export”, i.e., run a report from source system) Maturity Level? Beginner
- Transactional data --> DW better practice Defined as anything other than txnl – actual EDW, snapshot, ODS, Data Lake – your mileage may vary! Doesn’t affect the transactional DB Modeled for analysis can be DirectQuery or import DirectQuery can be against DW or against SSAS cube configured as DirectQuery against DW Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the DW during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Ideal for: medium/large enterprise Methods: DirectQuery, Import, or Dual
- Transactional data --> DW better practice Defined as anything other than txnl – actual EDW, snapshot, ODS, Data Lake – your mileage may vary! Doesn’t affect the transactional DB Modeled for analysis can be DirectQuery or import DirectQuery can be against DW or against SSAS cube configured as DirectQuery against DW Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the DW during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Ideal for: medium/large enterprise Methods: DirectQuery, Import, or Dual
- Option #2 – Connect to EDW better practice will not affect the transactional DB typically modeled for analysis can be DirectQuery or import DirectQuery can be against DW or against SSAS cube configured as DirectQuery against DW Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the DW during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Ideal for: medium/large enterprise Methods: DirectQuery, Import, or Dual MENTION ITERATIVE PROCESS – feedback loop – bring in other data source, proving value, harden, pull upstream into flows, ETL etc.
- Transactional data --> SSAS Live Query from PBI, i.e., zero modeling, only creation of measures using existing fields. However, Microsoft is working on composite models to allow DirectQuery against SSAS or Power BI service models + the ability to import and append to the model. SSAS DirectQuery SSAS cube can be configured as DirectQuery from DW SSAS In-Memory better use case than DirectQuery due to only hitting the DW during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Import into PBI Allows for additional modeling in PBI, but you are now pulling all the data over locally and it is very resource intensive. Ideal for: medium/large enterprise, governed reporting needs, ease of use Methods: DirectQuery or Import
- Option #2 – Connect to EDW better practice will not affect the transactional DB typically modeled for analysis can be DirectQuery or import DirectQuery can be against DW or against SSAS cube configured as DirectQuery against DW Import can be imported into Power BI model or SSAS model better use case than DirectQuery due to only hitting the DW during refresh better use case due to typical import vs DirectQuery pros/cons (i.e. performance, modeling, etc.) Ideal for: medium/large enterprise Methods: DirectQuery, Import, or Dual MENTION ITERATIVE PROCESS – feedback loop – bring in other data source, proving value, harden, pull upstream into flows, ETL etc.
- Shared Dataset can be based on DirectQuery (to any data source - i.e. transactional, DW) can be based on import (Power BI or SSAS) can point to dataflows as a data source Ideal for: shared datasets across Power BI reports Methods: Datasets can be DirectQuery or Import, but from Power BI are Live only INTENT of data set is to provide clean data, BUT can be wild wild west Old way was top down, governed. These tools have turned upside down, so governance is critical!
- Shared Dataset can be based on DirectQuery (to any data source - i.e. transactional, DW) can be based on import (Power BI or SSAS) can point to dataflows as a data source Can be promoted/certified Ideal for: shared datasets across Power BI reports Methods: Datasets can be DirectQuery or Import, but from Power BI are Live only INTENT of data set is to provide clean data, BUT can be wild wild west Old way was top down, governed. These tools have turned upside down, so governance is critical!
- Shared Dataset can be based on DirectQuery (to any data source - i.e. transactional, DW) can be based on import (Power BI or SSAS) can point to dataflows as a data source Ideal for: shared datasets across Power BI reports Methods: Datasets can be DirectQuery or Import, but from Power BI are Live only INTENT of data set is to provide clean data, BUT can be wild wild west Old way was top down, governed. These tools have turned upside down, so governance is critical!
- Dataflows - think “reusing data objects (one or more ENTITIES) across different reporting MODELS, i.e., TIME” Always imported into Azure Data Lake Gen2 (ADLG2) can reduce the number of data extracts from source systems can be configured as incremental refreshes can then be configured as DirectQuery from Power BI to the dataflow (coming soon) or import from Power BI to the dataflow can BYOS (bring your own storage) and point dataflows to your own Azure Data Lake Gen2 that data in your own ADLSG2 storage can then be utilized by other downstream services that data can also be configured to utilize CDM Ideal for: shared data sources across Power BI datasets Methods: Import
- Dataflows - think “reusing data objects (one or more ENTITIES) across different reporting MODELS, i.e., TIME” Always imported into Azure Data Lake Gen2 (ADLG2) can reduce the number of data extracts from source systems can be configured as incremental refreshes can then be configured as DirectQuery from Power BI to the dataflow (coming soon) or import from Power BI to the dataflow can BYOS (bring your own storage) and point dataflows to your own Azure Data Lake Gen2 that data in your own ADLSG2 storage can then be utilized by other downstream services that data can also be configured to utilize CDM Ideal for: shared data sources across Power BI datasets Methods: Import
- Dataflows are always import into Azure Data Lake Gen2 can reduce the number of data extracts from source systems can be configured as incremental refreshes can then be configured as DirectQuery from Power BI to the dataflow (coming soon) or import from Power BI to the dataflow can BYOS (bring your own storage) and point dataflows to your own Azure Data Lake Gen2 that data in your own ADLSG2 storage can then be utilized by other downstream services that data can also be configured to utilize CDM Ideal for: shared data sources across Power BI datasets Methods: DirectQuery Premium – 100TB limit, Pro 10GB limit
- Assumes well-designed EDW or data model “Query Method” refers to query connection method used by Power BI Desktop
- Assumes well-designed EDW or data model “Query Method” refers to query connection method used by Power BI Desktop
- BI reports Methods: DirectQuery or Import Iterative process - Starts w desktop as prototype – suss out requirements, optimize, harden, deploy, iterate
- Focus on the issues that have the biggest impact to your organization, including Speed up dashboard creation—reduce time spent gathering data Reuse data sets—eliminate repetitive work Share dashboards across departments Perform rapid, on-the-fly recalculations Reduce errors from manual data manipulation Restrict access to sensitive data
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