Now we are on a process of converting few of our ssrs reports into Power BI. I wonder if there any option to import an entire report structure(rdl files) into power BI. please help me about this
Import of SSRS reports (.rdl) is supported now in Power BI premium.
This feature is called as Paginated Reports.
Refer https://learn.microsoft.com/en-us/power-bi/paginated-reports-save-to-power-bi-service
I understand this is very late reply, but I guess it could help someone.
There isn't. SSRS & Power BI are fundamentally different tools. If your SSRS reports are simple, then it shouldn't be hard to move the queries over to Power BI and then reproduce the same visuals (tables, charts, etc), though it would still look & feel very different.
Power BI has a focus on interactivity & data exploration that SSRS does not. As you convert reports, you'll likely want to update them to take advantage of features like slicers & cross-filtering & drill-downs.
On the other hand, SSRS has a lot of fine-tuning options that simply don't exist in Power BI (including an entire expression language). There are a lot of things you can do with SSRS that you can't easily recreate in Power BI (which is why a general conversion tool would be very difficult to write).
A super-delayed response, I realize, so I'm curious as to how your conversion project worked out.
Related
I've tried searching around to see what the best practices are when designing a view that will be used for visualization going directly into PowerBI or Tableau.
I don't know the best way to ask this but is there an issue with creating a big query 30+ columns with multiple joins in the DB for export into the visualization platform? I've seen some posts regarding size and about breaking up into multiple queries but those are also in reference to bringing into some program and writing logic in the program to do the joins etc.
I have tried both ways so far, smaller views that I then create relationships in PowerBI or larger views where I'm dealing with one just flat table. I realize that in most respects PowerBI can do a star scheme with data being brought in but I've also run into weird issues with filtering within the PowerBI itself, that I have been able to alleviate and speed up by doing that work in the DB instead.
Database is a Snowflake warehouse.
Wherever possible, you should be using the underlying database to do the work that databases are good at i.e. selecting/filtering/aggregating data. So your BI tools should be querying those tables rather than bringing all the data into the BI tool as one big dataset and then letting the BI tool process it
I am looking to convert an excel spreadsheet into more of a scalable solution for reporting. The volume of the data is not very large. At the moment the spreadsheet around 5k rows and grows by about 10 every day. There are also semi-frequent changes in how we capture information i.e. new columns as we starting to mature the processes. The spreadsheet just stores attributes or dimensions data on cases.
I am just unsure whether I should use a traditional SQL database or NoSQL database (or any other tool). I have no experience in NoSQL but I understand that it is designed to be very flexible which is what I want compared to a traditional DB.
Any thoughts would be appreciated :) !
Your dataset is really small and any SQL database (say, PostgreSQL) will work just fine. Stay away from NoSQL DBs as they are more limited in terms of reporting capability.
However, since your facts schema is still not stable ("new columns as we starting to mature the processes.") you may simply use your Spreadsheet as a data source in BI tools. To keep your reports up-to-date you may use the following process:
Store your Spreadsheet on cloud storage (like Google Drive or OneDrive)
Use codeless automation platform (like Zapier) to setup a job to sync Spreadsheet file with BI tool when it changes. This is easily possible if BI tool is SeekTable, for instance.
I'm importing data via SQL import to a Power BI data model. If I group the data I can save the import 100,000 rows, but it effect the way I write my DAX queries to get the correct answer (and makes them slightly more complex). I'm after the general best practice for where to group data, pre import, or post import and allowing the DAX aggregator DAX functions to work on the whole table.
I've tried both options and can save about 6 seconds on the load if I group in SQL, but I needed to re-write some DAX.
You're sailing into waters of the question being 'too broad' as the correct answer will differ with different data sets etc.
You should always try and feed your dashboards with as little data as possible to answer the question being asked. This will save processing time in the dashboard itself. If you can aggragate in SQL (SQL is good at aggregating) and save yourself some load time then great. However if it makes your DAX unmaintainable (and maintainability is improtant to you) then it might not be the best.
Feeding your dashboards with as little data as possible and making your datasets as simple as possible will ensure your dashboards remain snappy.
If you put a billion rows into a dashboard you might find the engine can handle it, but if you can make that data into 15 rows you know which one is going to be more responsive.
You can find more information about best practices here.
How to create Multi dimensional transformer cube in Microsoft power BI like cognos
as mention in below link
https://www.youtube.com/watch?v=d_rUNLJAUTU&list=PL1UFrxYya46MFZ3TFPpDOzR0WVMZo91gm
Any positive response will be appreciated
Thanks in advance.
I did not watch the video, but you cannot create cubes in Power BI as it is a reporting tool, not an OLAP engine like Cognos Transformer. Power BI is more equivalent to Cognos Workspace Advanced. Microsoft SQL Server Analytical Services (SSAS) is the Microsoft OLAP engine.
You can, however, use Power BI in an in-memory ROLAP like manner over a star schema using the Matrix visualization but unless data volumes are relatively small data load times can be excessive and you may run out of RAM. Direct queries get around the size limitation but can be slow unless you have a very powerful database server.
I have Essbase as the BI solution (for Predictive Analytics and Data Mining) in my current workplace. It's a really clunky tool, hard to configure and slow to use. We're looking at alternatives. Any pointers as to where I can start at?
Is Microsoft Analysis Services an option I can look at? SAS or any others?
Essbase focus and strenght is in the information management space, not in predictive analytics and data mining.
The top players (and expensive ones) in this space are SAS (with Enterprise Miner & Enteprise Guide combination) and IBM with SPSS.
Microsoft SSAS (Analysis Services) is a lot less expensive (it's included with some SQL Server versions) and has good Data Mining capabilities but is more limited in the OR (operations research) and Econometrics/Statistics space.
Also, you could use R, an open source alternative, that is increasing its popularity and capabilities over time, for example some strong BI players (SAP, Microstrategy, Tableau, etc.) are developing R integration for predictive analytics and data mining.
Check www.kpionline.com , is a product cloud based in Artus.
It has many dashboards, scenarios and functions prebuilt to do analysis.
Other tool than you could check is microstrategy. It has many functions to analysis.