Learn how to use Power BI Report Builder to build paginated reports. Paginated reports allow you to control formatting exactly for delivery as a PDF or on paper, constraining tables to fit on a page and adding headers and footers. Watch the demos and download this deck at: https://senturus.com/resources/power-bi-report-builder-paginated-pixel-perfect-reports.
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4. 4
Topics
• Challenges of report authoring
• What are paginated reports?
• Data sources and
paginated reporting
• Building a paginated report
• Static versus interactive reporting
• Publishing reports
• Report Builder licensing
4
Demo:
Creating a paginated report
6. Enjoy the full webinar presentation
This slide deck is from the webinar Power BI Report Builder
& Paginated Reports
To view the FREE video recording and download this deck,
go to https://senturus.com/resources/power-bi-report-builder-
paginated-pixel-perfect-reports/
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7. Challenges of report authoring
• Too often, users want you to recreate what they already are
used to using
• This means figuring out how to replicate Excel spreadsheets,
paper forms, etc
• In most tools, especially modern tools focused on visualizations,
this can be a challenge
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8. Challenges of report authoring
A modern style dashboard can be too graphical to meet the
needs of some users and situations.
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9. What are paginated reports?
• Paginated reports are designed to be printed or shared
• They're called paginated because they're formatted to fit
well on a page
• They display all the data in a table, even if the table spans
multiple pages
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10. What are paginated reports?
• Called pixel perfect because you can
control their report page layout exactly
• Power BI Report Builder is the standalone
tool for authoring paginated reports for
the Power BI service
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11. Use cases of paginated reports
• Paginated reports can solve many problems for an
organization
• Beyond just printing and sharing in a traditional way, these
reports can be used for:
• Kiosk displays
• Reports with non-standard layouts
• Public distribution
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12. Use cases of paginated reports
• Examples of paginated reports can be seen in reports that
provide:
• Summary information
• Publicly shared data
• Fiduciary reports
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13. Data sources and paginated reports
• A single paginated report can have a number of different data sources, it
doesn't have an underlying data model, unlike Power BI reports
• For the initial release of paginated reports in the Power BI service, you
create embedded data sources and datasets in the report itself
• Reports are created in Report Builder on your local computer
• If a report connects to on-premises data, after you upload the report to the
Power BI service, you need to create a gateway and redirect the data
connection
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14. Data sources and paginated reports
Here are the data sources you can connect to at this time:
• Azure SQL Database and Data Warehouse (via Basic and oAuth)
• Azure Analysis Services (via SSO)
• SQL Server via a gateway
• SQL Server Analysis Services via a gateway
• Power BI Datasets
• Oracle
• Teradata
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15. Building a paginated report
• Matrix reports, like cross-tab or
PivotTable reports, are good for
summarized data
• Charts give us data in a graphical
format, and free form list reports
work for everything else
• Free form lists work well with
invoices or similar layouts
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16. Building a paginated report
• You can start with one of the Report Builder wizards
• The Table, Matrix, and Chart wizards walk you through creating
the embedded data source connection and embedded dataset
• Then you drag and drop fields to create a dataset query, select
a layout and style, and customize your report
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17. Building a paginated report
• With the Map wizard, you create reports that display
aggregated data against a geographic or geometric background
• Map data can be spatial data from a Transact-SQL query or an
Environmental Systems Research Institute, Inc. (ESRI)
shapefile
• You can also add a Microsoft Bing map tile background
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18. Static versus interactive reporting
• Remember, this type of report is designed for print or sharing
usage as well as targeted situations
• These types of reports (at this time) cannot be used for things
like drillthrough definitions, document maps or pinning to Power
BI dashboards
• Reports can be subscribed to as well as exported to a number
of formats including HTML, MHTML, PDF, XML, CSV, TIFF,
Word and Excel
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19. Publishing a report
• In the Power BI service, you can use the deployment pipeline
tool with paginated reports
• Deployment pipelines let you develop and test your Power BI
paginated reports before you release them to your users
• The tool is a pipeline with three stages:
• Development
• Test
• Production
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20. Report Builder licensing options
• Publishing a paginated report is a Power BI Premium
• Without a Pro license you can publish and share paginated
reports in your My Workspace or in workspaces, as long as
the workspace is in a Power BI Premium capacity
• The paginated reports workload is available on P1 – P3
SKUs
• You may also use it with A4 – A6 SKUs for embed or
test/dev scenarios
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21. See these Power BI demos
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Creating a paginated report
• Adding a data source
• Designing a layout
• Adding data
at https://senturus.com/resources/power-bi-report-builder-paginated-pixel-
perfect-reports/
22. Moving Power BI beyond the desktop
•Get Automation. Accuracy. Speed. Shareable dashboard
reporting.
•Senturus can help you achieve successful self-service
analytics. We’ll transition you to a scalable Power BI
solution with enterprise-class performance.
•https://senturus.com/power-bi-desktop-to-enterprise/
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23. Your path to modern BI
Accelerating self-service analytics
for the enterprise
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26. A long, strong history of success
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28. Upcoming events
•What’s New in Cognos 11.2.0
•Demos and Q&A with IBM Offering Manager
•Thursday, Apr. 22, 2021, 11am PT/2pm ET
•Power BI Report Builder & Paginated Reports
•Using Power BI for printed, pixel-perfect reporting
•Thursday, May 13, 2021, 11am PT/2pm ET
•Easily Connect Power BI & Tableau to Cognos
•Senturus Analytics Connector Demo
•Thursday, May 20, 2021, 11am PT/2pm ET
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Joining us today is…..Patrick Powers
Patrick has 20 years of experience in business intelligence and data analytics. He’s one of our trainers, delivering classes on Power BI, Tableau and Cognos. His certifications include multiple programming languages, including Java and C++, and database certification (MS SQL).
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
Data is everywhere and you can create a quick Tableau Dashboard using just about anything. But the old garbage in garbage out principal often applies…. And end of the day the data behind your dashboards needs to be accurate, informational and up to date. ???
Good data is an absolute requirement to make accurate projections which in turn drives supply chains and helps people make informed decisions. Today we are really seeing how important good data is.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
What is “Good data” and what is “bad data” ?
Often you don’t even know you have “bad data” until you use a tool like Tableau to expose the data.
Some common issues uncovered when displaying data in Tableau:
Wrong format – You may have you data going across like Jan Feb March sales numbers. Data in this format cant be easily aggregated .
Multiple sources – you might have some HR data in one system and expense report data in another it can be harder than you think to join sources
Granularity – you might have actual sales down to the day but you budget is by quarter this is different granularity and can give you the wrong results in your visualizations if you don’t deal with it properly.
Analysis Friendly - Sometimes field names are not intuitive and dates are not in the correct format to let Tableau do its job in creating automatic date hierarchies. This is especially true if you are dealing with transactional systems.
Outliers and data “mistakes” – Often, it isn’t until you display your data in something like Tableau that you noticed the problems, someone leaves off a few zeros, a date is in the wrong format, abbreviations are not consistent i.e. CA vs Calif vs California.
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