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Backup and restore your Azure Analysis Services models

This month we announced the general availability of Azure Analysis Services, which evolved from the proven analytics engine in Microsoft SQL Server Analysis Services. The success of any modern data-driven organization requires that information is available at the fingertips of every business user, not just IT professionals and data scientists, to guide their day-to-day decisions. Self-service BI tools have made huge strides in making data accessible to business users. However, most business users don’t have the expertise or desire to do the heavy lifting that is typically required, including finding the right sources of data, importing the raw data, transforming it into the right shape, and adding business logic and metrics, before they can explore the data to derive insights. With Azure Analysis Services, a BI professional can create a semantic model over the raw data and share it with business users so that all they need to do is connect to the model from any BI tool and immediately explore the data and gain insights. Azure Analysis Services uses a highly optimized in-memory engine to provide responses to user queries at the speed of thought.

One of the features that was added to Azure Analysis Services is the ability to backup your semantic models and all the data within them to a blob storage account. The backups can later be restored to same Azure Analysis Services server or to a different one. This method can also be used to backup models from SQL Server Analysis services and then restore them to Azure Analysis services. Please note that you can only restore models with a 1200 or higher compatibility level and that any active directory users or groups bust be removed from any role membership before restoring. After restoring, you can re-add those users and groups from Azure Active Directory.

Configure storage settings

Before backing up or restoring, you need to configure storage settings for your server. Azure Analysis Services will backup your models to blob storage account of your choosing. You can configure multiple servers to use the same storage account making it easy to move models between servers.

To configure storage settings:

In Azure portal > Settings, click Backup.

Click Enabled, then click Storage Settings.

Select your storage account or create a new one.
Select a container or create a new one.

Save your backup settings. You must save your changes whenever you change storage settings, or enable or disable backup.

Backup

Backups can be performed using the latest version of SQL Server Management Studio. It can also be automated through PowerShell or with the Analysis Services Tabular Object Model (TOM).

To backup using SQL Server Management Studio:

In SSMS, right-click a database > Back Up.
In Backup Database > Backup file, click Browse.
In the Save file as dialog, verify the folder path, and then type a name for the backup file. By default, the file name is given a .abf extension.
In the Backup Database dialog, select options.

Allow file overwrite – Select this option to overwrite backup files of the same name. If this option is not selected, the file you are saving cannot have the same name as a file that already exists in the same location.

Apply compression – Select this option to compress the backup file. Compressed backup files save disk space, but require slightly higher CPU utilization.

Encrypt backup file – Select this option to encrypt the backup file. This option requires a user-supplied password to secure the backup file. The password prevents reading of the backup data any other means than a restore operation. If you choose to encrypt backups, store the password in a safe location.

Click OK to create and save the backup file.

Restore

When restoring, your backup file must be in the storage account you've configured for your server. If you need to move a backup file from an on-premises location to your storage account, use Microsoft Azure Storage Explorer or the AzCopy command-line utility.

If you&039;re restoring a tabular 1200 model database from an on-premises SQL Server Analysis Services server, you must first remove all of the domain users from the model&039;s roles, and add them back to the roles as Azure Active Directory users. The roles will be the same.

To restore by using SSMS:

In SSMS, right-click a database > Restore.
In the Backup Database dialog, in Backup file, click Browse.
In the Locate Database Files dialog, select the file you want to restore.
In Restore database, select the database.
Specify options. Security options must match the backup options you used when backing up.

New to Azure Analysis Services? Find out how you can try Azure Analysis Services or learn how to create your first data model.
Quelle: Azure

da Agency

Making cities safer: data collection for Vision Zero

A critical part of enabling cities to implement their Vision Zero policies – the goal of the current National Transportation Data Challenge &8211; is to be able to generate open, multi-modal travel experience data. While existing datasets use police and hospital reports to provide a comprehensive picture of fatalities and life altering injuries, by their nature, they are sparse and resist use for prediction and prioritization. Further, changes to infrastructure to support Vision Zero policies frequently require balancing competing needs from different constituencies &8211; protected bike lanes, dedicated signals and expanded sidewalks all raise concerns that automobile traffic will be severely impacted.
A timeline of the El Monte/Marich intersection in Mountain View, from 2014 to 2017 provides an opportunity to put some of these challenges into context.

since there is no standard way to report near misses, the City didn’t know that the intersection was so dangerous until somebody actually died, and it was not included in the ped and bike plans,
because the number of fatalities is so low, and the number of areas that need to be fixed is so high, past fatalities may not be a good predictors of future ones. But that makes prioritization challenging &8211; should the City play “whack-a-mole” with locations where fatalities occurred, or should it stick with the ped and bike plans?
even if the City does pick an area to fix, it is not clear what the fix should be. Note that the City wanted to improve the visibility of the intersection, but the residents were skeptical that any solution that did not address the speeding would be sufficient.
it is not clear how to balance competing needs &8211; addressing the speeding issue will potentially increase the travel times of (the currently speeding) automobile travellers.  Increased travel time is quantifiable, how can we make the increased safety also quantifiable so that we can, as a society, make the appropriate tradeoffs?

The e-mission project in the RISE and BETS labs focuses on building an extensible platform that can instrument the end-to-end multi-modal travel experience at the personal scale, collate it for analysis at the societal scale, and help solve some of the challenges above.
In particular, it combines background data collection of trips, classified by modes, with user-reported incident data, and makes the resulting anonymized heatmaps available via public APIs for further visualization and analysis. The platform also has an integration with the habitica open source platform to enable gamification of data collection.
Click to view slideshow.
This could allow cities to collect crowdsourced stress maps, use them to prioritize the areas that need improvement, and after pilot or final fixes are done, quantify the reduction in stress and mode shifts related to the fix.
Since this is an open source, extensible platform and generates open data, it can easily be extended to come up with some cool projects. Here are five example extensions to give a flavor of what improvements can be done:

enhance the incident reporting to provide more details (why? how serious?)
have the incident prompting be based on phone shake instead of a prompt at the end of every trip
encourage reporting through gamification using the habitica integration
convert the existing heatmaps to aggregate, actionable metrics
automatically identify “top 5” or “top 10” hotspots for cities to prioritize

But these are just examples &8211; the whole point of the challenge is to tap into all the great ideas that are out there. Sign up for the challenge, walk/bike around your cities, hear what planners want, and use your ideas to make the world a better place!
Quelle: Amplab Berkeley