I want to get the data from published Power BI report which is embedded in a web page as a dashboard. Is there any way to do this? What technology/tool can be used to read data of a dashboard embedded in a web page?
If you don't see the visual header (ellipsis) where you can click Export data, then no. This means that the owner of the report hide this on purpose or the admin disabled it. Also exporting data requires Pro or Premium and edit permissions on the dataset and report, which you may not have.
If you see the visual header, then you can, but keep in mind that there are some limitations:
The maximum number of rows that can be exported using API to .csv is 30,000.
Export using Underlying data will not work if the data source is an Analysis Services live connection and the version is older than 2016 and the tables in the model do not have a unique key.
Export using Underlying data will not work if the Show items with no data option is enabled for the visualization being exported.
If you have applied filters to the visualization, the exported data will export as filtered.
When using DirectQuery, the maximum amount of data that can be exported is 16 MB. This may result in exporting less than the maximum number of rows, especially if there are many columns, data that is difficult to compress, and other factors that increase file size and decrease number of rows exported.
Power BI only supports export in visuals that use basic aggregates. Export is not available for visuals using model or report measures.
Custom visuals, and R visuals, are not currently supported.
Power BI admins have the ability to disable the export of data.
Concurrent export data requests from the same session are not supported. Multiple requests should be run synchronously.
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Context: We store historical data in Azure Data Lake as versioned parquet files from our existing Databricks pipeline where we write to different Delta tables. One particular log source is about 18 GB a day in parquet. I have read through the documentation and executed some queries using Kusto.Explorer on the external table I have defined for that log source. In the query summary window of Kusto.Explorer I see that I download the entire folder when I search it, even when using the project operator. The only exception to that seems to be when I use the take operator.
Question: Is it possible to prune columns to reduce the amount of data being fetched from external storage? Whether during external table creation or using an operator at query time.
Background: The reason I ask is that in Databricks it is possible to use the SELCECT statement to only fetch the columns I'm interested in. This reduces the query time significantly.
As David wrote above, the optimization does happen on Kusto side, but there's a bug with the "Downloaded Size" metric - it presents the total data size, regardless of the selected columns. We'll fix. Thanks for reporting.
I currently have an Access database which pulls data from an Oracle for various countries and is currently around 1.3 GB. However, more countries and dimensions should be added, which will further increase its size. My estimate would be around 2 GB, hence the title.
Per country, there is one table. These tables are then linked in a second Access db, where the user can select a country through a form which pulls the data from the respective linked table in Access db1, aggregates it by month, and writes it into a table. This table is then queried from Excel and some graphs are displayed.
Also, there is another form where the user can select certain keys, such as business area, and split the data by this. This can be pulled into a second sheet in the same excel file.
The users would not only want to be able to filter and group by more keys, but also be able to customize the period of time for which data is displayed more freely, such as from day xx to day yy aggregated by week or month (currently, only month-wise starting from the first of each month is supported).
Since the current Access-Access-Excel solution seems to me to be quite a cumbersome one, I was wondering whether I might make the extensions required by the users of this report using R and either shiny or markdown. I know that shiny does not allow files larger than 30MB to be uploaded but I would plan on using it offline. I was just not able to find a file size limit for this - or do the same restrictions apply?
I know some R and I think that the data aggregations needed could be done very fast using dplyr. The problem is that the users do not, so the report needs to be highly customizable while requiring no technical knowledge. Since I have no preexisting knowledge of shiny or markdown, I was wondering whether it was worth going through the trouble of learning one enough to implement this report in them.
Would what I want to do be feasible in shiny or R markdown? If so, would it still load quickly enough to be usable?
I am trying to export all of my leads from Marketo (we have over 20M+) into a CSV file, but there is a 10k row limit per CSV export.
Is there any other way that I can export a CSV file with more than 10k row? I tried searching for various dataloader tool on Marketo Launchpoint but couldn't find a tool that would work.
Have you considered using the API? It may not be practical unless you have a developer on your team (I'm a programmer).
marketo lead api
If your leads are in salesforce and marketo/salesforce are in parity, instead of exporting all your leads, do a sync from salesforce to the new MA tool (if you are switching) instead. It's a cleaner easier sync.
For important campaigns etc, you can create smart lists and export those.
There is no 10k row limit for exporting Leads from a list. However, there is a practical limit, especially if you choose to export all columns (instead of only the visible columns). I would generally advise on exporting a maximum of 200,000-300,000 leads per list, so you'd need to create multiple Lists.
As Michael mentioned, the API is also a good option. I would still advise to create multiple Lists, so you can run multiple processes in parallel, which will speed things up. You will need to look at your daily API quota: the default is either 10,000 or 50,000. 10,000 API calls allow you to download 3 million Leads (batch size 300).
I am trying out Data Loader for Marketo on Marketo Launchpoint to export my lead and activity data to my local database. Although it cannot transfer marketo data to CSV file directly, you can download Lead to your local database and then export to get a CSV file. For your reference, we have 100K leads and 1 billion activity data.
You might have to run multiple times for 20M leads, but the tool is quite easy and convenient to use so maybe it’s worth a try.
Initially there are 4 steps to get bulk leads from marketo
1. Creating a Job
2. Enqueue Export Lead Job
2. Polling Job Status
3. Retrieving Your Data
http://developers.marketo.com/rest-api/bulk-extract/bulk-lead-extract/
I can't find answers to my questions regarding to how Google Analytics Cost Data Upload works. I have a few questions:
Why two different upload methods? Why not just have one dimension-wide upload option?
Let say I upload costs using daily upload method and after that I upload costs using data import. Will data import override daily data, will that data be merged or even deleted? What will happen in this case? Will the next daily upload override data of data import?
If you delete uploaded data, will costs in GA report be reseted to zero?
Does lifetime data storage limit per property also applies for daily uploads or only data import?
Thank you for your help!
Dimension widening is for the generalized case whereas Cost Data is for a specific upload case (i.e. there are specific reports in GA for Cost Data). However, your question is still valid as to why there are 2 separate uploads, it doesn't look necessary but that's the way it is for now.
Daily upload and Data Import work independently of each other. One is not going to affect the other. You should be using daily upload for cost data since it has a set schema and reports. Data import for other data sets where you define the schema.
For Cost Data, if you delete the data then yes, your reports will not show any related cost data anymore (or you can just unlink the profile from the data set to accomplish the same result). For Dimension Widening, even if you delete the data set or unlink it, any data that was joined before the deletion or unlinking will stay as part of the reports, you can't remove it after the fact. This is because of differences between the two in how the data gets joined.
As stated in #2, these are different mechanisms, so they don't share storage. Daily upload has limits that apply to the upload (e.g. 20 appends/day, max 5MB per append). The doc you linked to clearly states what these are for each. Just treat them separately unless it is stated otherwise.
I need to create a Microsoft BI Produced Report, that will display Actual Data, retrieved from database. But I also want users to be able to fill Forecast Data for next month in the column beside the reported actual data, and the inputted data will be loaded to Forecast Cube.
Is it possible to do it ? What is the right strategy ?
Thanks for your input :) !
MS BI products are based on reporting, so they don't have any direct ways to interact with the data like you're asking. There are several options that start with an SSRS report though (or a PowerPivot book etc.).
SharePoint Workflow - allows a lot of other control aspects to the input process. Pickup the list with an ETL package.
You could also do a similar thing with a simple web app. Link either through a report action.
ETL - Make the report exportable to excel and leave blank columns for user input. Re-absorb it through an ETL process that reads the modified Excel file. This can be a manually triggered job, not necessarily part of a DM/DW nightly ETL.
You could also just have a manual script. I would think and hope that the forecast data isn't changed that often, so a simpler solution would probably be best.