During the session, Faisal introduced the value proposition for Data Explorer as well as described some of the most common challenges in data acquisition and transformation for self-service BI. He also spent a big portion of the session showing demos with “real world data” so we definitely recommend you take a look!

You can watch it online or download it from Channel 9 to watch it later.

Enjoy!

This got demonstrated yesterday in a blog post on the Microsoft BI blog: Surfing Restaurant Inspections with Microsoft Data Explorer and Geoflow

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Father’s Day is approaching and you might be thinking about a good place to have a nice lunch with your Dad… We would like to show you how Data Explorer and Geoflow can help you gather some insights to make a good decision.

In order to achieve this, we will look at publicly available data about Food Establishment Inspections for the past 7 years and we will also leverage the Yelp API to bring ratings and reviews for restaurants. For the purpose of this post, we will focus on the King County area (WA) but you can try to find local data about Food Establishment inspections for your area too.

What you will need:

• Data Explorer.
• Geoflow.
  • Access to the following data:
  • Food Establishment Inspections data from datakc.org (more details):
  • We will link to this URL (which points to the CSV file version of the data) using Data Explorer: https://www.datakc.org/api/views/pph9-v8tz/rows.csv?accessType=DOWNLOAD
  • Yelp API (more details):
  • You will need to sign up for an API key before being able to connect. Please also make sure to read the Yelp API Terms of Use before using this API.

What you will learn in this post:

• Import data from the Yelp Web API (JSON) using Data Explorer.
• Import public data about Food Establishment Inspections from a CSV file.
• Reshape the data in your queries.
• Parameterize the Yelp query by turning it into a function, using the Data Explorer formula language, so you can reuse it to retrieve information about different types of restaurants as well as different geographical locations.
• Invoke a function given a set of user-defined inputs in an Excel table.
• Combine (Merge) two queries.
• Load the final query into the Data Model.
• Visualize the results in Geoflow.

That sounds like too much for a single blog post, but let’s get started and you’ll see how it is easier than you might think.

Import data from Yelp API

The first thing that we will have to do is click the “From Web” button in the Data Explorer ribbon tab. Data Explorer supports importing data from different kinds of Web sources, including scrapping tables from HTML pages, as well as importing data from Web APIs. Data Explorer supports XML, JSON and OData formats.

Taking a look at the Yelp API documentation, you will find that they offer a Search API which accepts multiple GET parameters to customize your query, including a search term (i.e. restaurants, sushi, seafood, etc.), a location (i.e. “Seattle”, “98052”, lat/long information) and a few others. Finally we also need to provide the API Key that we will receive after registering for this API (wsid parameter).

With that in mind, let’s paste in Data Explorer the following URL which will return Seafood restaurants in Redmond (note that you will have to replace the wsid value with your own key).

https://api.yelp.com/business_review_search?term=seafood&location=Redmond &ywsid=XXXXXX

We will get a result that looks like a single column table with one row, containing text data which, in fact, is structured as JSON. We can interpret this text as JSON by right-clicking in the column header and selecting “Transform -> JSON”.

Now as a result we get a record which we can expand and select which fields to include in the output. Having used the Yelp API before, we know that the “businesses” field is the one that contains the list of restaurants that we are looking for, so we can just expand that one by unselecting “message” in the Expand menu and clicking “OK”.

This will return a single cell containing a list, which we can expand using the same mechanism again and turn it into a bunch of records, one per restaurant. Now we can decide which columns to expand, containing different information about each restaurant such as Name, Lat/Long information, Rating, etc. We will expand the following columns: city, review_count, latitude, address1, name, avg_rating and longitude.

Now we have the list of Seafood restaurants into a tabular shape, and after applying some column renames and reordering the columns, we can get to the following shape.

That was actually easy… At the end, all we’ve done is define a set of data acquisition and transformation steps, which get recorded in the Steps pane on the right side of the Query Editor dialog. This Query Editor dialog is an easy-to-use interface for defining these steps, which get translated into the corresponding set of Data Explorer formulas (you can access them by clicking the script icon to the right end of the formula bar).

Parameterizing our Yelp API query

As we saw in the previous section, we can customize our Yelp Search by modifying the GET parameters in the URL. We can also make our Data Explorer query parameterized by turning it into a function, which can take input parameters and use them within the function body. In order to do this, we just need to add the following first line to our query script and use the parameters in the first step (Source). Finally, we can also rename our query to GetRestaurants.

This was very straightforward, but if you want to learn more about Data Explorer functions you can take a look at the Language and Library Specifications. There’s more cool stuff waiting for you there!

Invoking the GetRestaurants function

Now that we have a function, it would be a good thing to learn how to invoke it… There’re basically three ways in which you can invoke a function:

• Invoking the function “inline”: Simply open the query editor for your function query and click Invoke. You will get asked for parameters as needed. This is a very quick way of invoking the function, however it defeats the purpose of reusability since at this point the function will no longer be available from outside this query (given that the result is a table).

• Referencing the function: You can use the Reference option in the Query ribbon tab to create a new query in the workbook that references the current query. In this case, we could do this and be able to invoke our function from a new query.
• Invoking the function over a table: We can also use the “Insert Custom Column” option from a different query in order to invoke a function across all rows in the table. We can also make the function parameters be based on other columns from the table.

The third method is the one that we will use in this example, so for instance we could create a table with different cities in the King County area and invoke the GetRestaurants function for each row, so that we can get restaurants for each of the cities in the list.

First, we will create a table in the Excel sheet containing a list of cities, such as Redmond, Bellevue, Seattle, Kirkland, Issaquah and Sammamish.

Now we can go to the Data Explorer tab and click the “From Table” option. This will create a new query that references the sheet table. We can insert a custom column into this table by clicking on the table icon on the top-left corner of the table and selecting “Insert -> Custom…”

In the “Insert Custom Column” dialog, we can type in any custom formula using the Data Explorer formula language. In this case, we will simply invoke the GetRestaurants function using the following formula:

At this point, we will be asked for information about the privacy level of my workbook data. This is done so that users don’t accidentally leak data from a private or organizational source and inadvertently send it to a public source (like the Yelp API in this case). We will select “Public” in this case. You can learn more about Privacy Levels in Data Explorer in our Online Help pages.

After providing this information, the new column will be added to the table. Note that we have a new column with nested tables on each row (containing the restaurants for each of the cities) and we can expand these columns similarly to how we did earlier with the JSON records. We can also remove the “Cities” column since we don’t need it anymore.

Once we have expanded all columns, you may find that there are some duplicated rows. This is due to the fact that the Yelp API may return restaurants within a radius up to 25 miles (which can also be customized in the Yelp API parameters) so a given restaurant in Seattle might be returned when searching within Seattle and Bellevue, for instance. We can get rid of duplicated rows by selecting “Remove Duplicates” in the table menu (top-left corner of the table). This will leave us with approximately 60 candidate seafood restaurants, with their ratings and total number of reviews, as well as location information (Address, City, Lat and Long). We can select the Ratings, Review_Count, Latitude and Longitude columns and make sure they are formatted as Number by right-clicking the column headers and selecting “Change Type -> Number”. Finally, let’s also rename the query to “Seafood restaurants in King County”.

With this, we have the list of restaurants in the desired shape… We could just pick the highest rated restaurant in the list and make a reservation for Father’s Day, but of course we want to know first what the King County Food Establishment Inspections records have to say about each of these restaurants…

Import public data about Food Establishment Inspections from a CSV file

The next step in our scenario is to bring in public data from datakc.org (King County’s public data web site). We found this dataset which seems suitable for our needs, since it contains information about food inspections for the past 7 years, as well as the Name, Address and City for these restaurants (which we also have in our Seafood Restaurants query).

We can get to the CSV version of this dataset by clicking the Export option in the top-right corner of the page and we will get this URL for CSV: https://www.datakc.org/api/views/pph9-v8tz/rows.csv?accessType=DOWNLOAD

This link can be used directly in Data Explorer, either using “From Web” or “From File -> CSV”…

Once we have the inspections data loaded as a table, we can reshape it a little bit as we did earlier. The experience in the Query Editor is consistent regardless of what the underlying data sources look like on each case. We’ll do the following:

• Remove all columns except the following ones: Name, Address, City, Inspection Type, Inspection Result and Violation Description.
• We would like to focus only on the Unsatisfactory reviews, so we can filter the Inspection Result to only include those values.

• You will also notice that the Name and City columns contain values on uppercase and lowercase… This is something that we need to “normalize” so that when we combine this query with the Yelp Restaurants list we get matching rows. Let’s select the Name and City columns and use the “Transform -> Uppercase” option in the column menu (right-click in column header).
• Finally, let’s rename this query to Inspections and we will end up with a table similar to this one:

The next thing we need to do is go back to the Seafood Restaurants query and also turn the Name and City columns into Uppercase, as a preparation for being able to merge the two queries based on these columns.

Combining Yelp Restaurants list with Food Inspections data

Once we’ve normalized Name and City columns in both queries, we can go ahead and merge the two queries by using the Merge option in the Data Explorer ribbon tab. Before doing that, let’s enable Fast Combine for this workbook. This will turn off the Privacy Levels check for queries being combined in this workbook and we know that, since we already marked both as Public, this is OK. After enabling Fast Combine, we can click Merge.

This will launch the Merge dialog where we can select the two queries to merge as well as the columns to match. We will pick the Seafood Restaurants query as the left query, so that we augment this table with inspections data for those restaurants where the information is available in the Inspections table. We will multi-select (CTRL+Click) the Name and City columns in both queries to specify that both columns have to match.

The text at the bottom of this dialog will tell us that we got some matches, so some of the recommended restaurants in Yelp actually are not as recommendable as they would seem, according to the inspections results… We can click OK at any time and look at the newly created column as result of the Merge.

In this case, instead of expanding the nested columns we will perform an aggregate operation, which can be found also on the same dropdown menu in this column.

We are interested in the Count of Inspection Result values, which basically gives us the number of Unsatisfactory Inspections for each of these restaurants.

With that, we will get a new column like the one in the screenshot below.

Now we can use this information to decide which restaurant we should go to. Interestingly, restaurants with an average rating of 3 or lower are the ones which also got a higher number of unsatisfactory inspections… There are also some surprises with restaurants rated as 3.5-4 and getting a significant number of negative inspection results, so be aware of that the next time you check Yelp if you don’t want to take any risks!

Visualize restaurant locations, reviews and inspections results in Geoflow

To finish this post, we will put the resulting query into a nice Geoflow map. First, we need to load the final query into the Excel Data Model, which you can do by clicking the “Load to data model” option in the Query Settings pane. Once done, it will switch to a confirmation message as displayed below.

Now you can go to the Insert tab in the Excel ribbon and select “Map”. This will launch Geoflow where we can create a new tour with multiple layers to visualize our data.

Let’s create two different visualization layers, one for the food inspections, which we can represent as a heatmap and use the Inspections column for the value field, and another layer for the Yelp ratings, which we can represent as a Column chart and use the Review_Count as column height and the Restaurant Name as categories.

In order to add each of the layers, we will need to pick the Final Report table and select the Latitude and Longitude columns first and tell Geoflow to “Map It” based on these fields.

Then we can pick the Review_Count field and select Column type chart, using the Review_Count as the Height and the Name as Category.

Then, we can add another layer to incorporate the Inspections Count as a heatmap and add the Inspections Count as the Value field. This will give us the final map as displayed below.

Finally, we will get a map similar to this one. The taller columns represent popular restaurants and we should avoid the areas with red in the heatmap (which mean higher number of unsatisfactory reviews).

Hope you liked this blog post and the only thing left for us is to wish you and your family a happy Father’s Day!!

This update contains several quality improvements and bug fixes, including the fix for the interaction issues between Data Explorer and the VBA Editor. If you have hit this issue in the past, install our latest bits and enjoy. If you haven’t, you don’t really want to know about it… but please install our bits anyways!

As always, we hope that you enjoy the latest and greatest Data Explorer bits and please send us a smile or frown if you have any feedback. We’ll be more than happy to hear from you!

Data Explorer Team

Using Microsoft’s Self-Service BI tools, Marc Reguera (Microsoft Finance Director) was able to get insights about Crime Rates on cities throughout United States, finding out which cities have the highest & lowest robbery rates per capita with just a few minutes of work…

Have you been able to leverage our tools to gain new insights? We would love to hear about your scenarios!

This update includes lots of bug fixes and quality improvements, as well as a couple of new features that we believe are going to be very valuable to you, based on your feedback over the past few months. These new features include:

Support for locale during imports and transformations 

We’ve added support for importing and visualizing data according to your regional/locale settings. You will notice a new dropdown menu in the Data Explorer ribbon to control the locale setting for your workbook (by default, it maps to your Operating System locale, at the time when the workbook was created). This will be the default locale used to interpret numbers, dates and times when importing data from text sources.

In addition, you can specify a different locale whenever you want to perform any “Change Type” operation.

Finally, you can also specify the File Origin for any text file that you import in Data Explorer by going into the “Edit Settings” dialog for the Source step in your query (clicking on the gear icon next to Source).

You can find more details in our Internationalization Help page.

Connection encryption for database sources

This new feature provides the ability to turn on/off the encryption of data and credentials sent when connecting to database data sources (such as SQL Server, Windows Azure SQL database, IBM DB2, etc.). You will find this option in the Credentials dialog when connecting to a new data source.

New ribbon layout and icons

If you have already used Data Explorer, you’ll notice that we have moved some things around in the ribbon in order to make them more discoverable and better organized. We’ve also been busy replacing most of our icons to give Data Explorer a nicer look.

Automatic column type detection

We’ve improved our column type detection logic, in order to give you better column types (other than Text) when importing data from text data sources such as Web, Text or CSV.

As always, we look forward to your feedback about these new features as well as any others in Data Explorer. If you have any questions, issues or suggestions don’t hesitate to reach out to us via our forum or send us a smile/frown from the Data Explorer ribbon tab.

Enjoy!

We’d like to let you know that there is an updated build for Data Explorer available. You should see “Auto Update” on your ribbon light up soon. Alternatively, you can go here to download.

In this build, we are introducing a built-in formula for unpivoting columns to rows. Over the past few weeks, many of you have expressed feedback indicating that the ability to unpivot data would be very useful for cleaning up and consuming data for everyday needs. We are happy to provide this as a simple, consistent and intuitive experience.

As an example, consider the following historical population data obtained from the London Datastore.

While this format works well as a report showing population details by year and area, it is not particularly well suited for consumption in analytic/BI scenarios  – especially if you were thinking about aggregating or summarizing the data along different dimensions.

Data Explorer now provides the ability to select the columns you would like to unpivot, and accomplish the task in a single click. Here’s the Unpivot menu option you can apply over a selection of columns:

The result of unpivoting yields the following table:

And a few more Data Explorer transformations gives you a nice cleaned up table that can be used for summarizing, or for consumption in PowerPivot or Power View.

This update of Data Explorer also includes a better experience for browsing and using search results. You no longer have to click on the tiny “Use” link in the search results pane – instead you can click on the search result itself. This was a point of confusion for many users, and we hope this improves the general experience.

As always, there are dozens of bug fixes in the update as well.

We hope you enjoy this latest build. Let us know what you think!

Data Explorer Team

Because Public Data is essential in a variety of Self-Service BI scenarios, we added an “Online Search” feature in Data Explorer. This button is your first step to find data and import it right into your spreadsheet, without having to switch context.

This data can be used in a couple of different ways:

1. To acquire and understand public data (e.g. find the list of most populated cities, the list of companies in S&P 500, etc.).
2. To augment or correlate your (corporate) data with some publicly available data (e.g. find the name for an Airline code, or country name given an country code; compare your sales by country to the GDP by country, etc.).

Data Explorer makes it easy to search and bring the data directly in Excel without any need of copy/paste and reformatting. And once you create your queries, you can save them and refresh them later when the data is updated without effort.

In the current Data Explorer Preview, this feature allows you to search and seamlessly import tables from Wikipedia (approximately 1.5 million tables); and going forward we will extend the scope of the search and allow query over a variety of other sources.

With millions of tables we are sure you will find a good use of public data. We are listening to your feedback, so let us know what you think of this feature and what kind of data sources you are using in your own scenarios!

Step-by-step example

Let’s go thru a simple example step by step. In this example, Michael, a business analyst working in a company owning several luxury retail stores in the USA, is investigating for a location for their next store. Starting with a current list of stores, he wants to visualize potential counties where he could open new stores based on their median income. The whole scenario takes him less than 5 minutes.

Step 1. Michael starts by a spreadsheet containing a list of current stores.

Figure 1. Initial spreadsheet, with the list of current stores (Step 1)

Step 2. Michael adds the list of current stores as a first Data Explorer query.

For that, he clicks on the “From Table” button in Data explorer and validates the next form. The table is added as a new query in a new sheet.

Step 3. He initiates the search, by clicking on the “Online Search” button in Data Explorer tab.

Step 4. Search for the highest income counties in United States:

Michael types “highest income counties USA” in the Online Search pane. He can see the search results, and have a preview of the data while moving the mouse over each result.

Figure 2. Search pane and preview (Step 4)

Step 5. Use the data:

When he finds the result he was looking for (here the second one), he clicks on “Use”. The data is then automatically imported into Excel as a new query. At this point the data is in Excel and he can use all Excel features (sort, filter, charts, etc.). He can also choose to merge this query with another query, or to reshape it using Data Explorer.

Figure 3. Data imported into Excel (Step 5)

Step 6. Merge initial data and public data.

Clicking on the “Merge” button, Michael can merge the two queries created so far, based on the County column.

For that, he selects the Wikipedia table as primary table, and the list of current stores as second table, then clicks on the County column in both table, and then clicks on apply.

Figure 4. Merging the two queries (Step 6) based on County

Step 7. Michael adds some additional columns.

In order to know the number of stores in each country, he right clicks on the column header, and then clicks on “NewColumn”, choose aggregate and then Count/Excluding blanks (see following picture). This will simply add a column with the number of existing stores in each county.

Figure 5. Add an aggregate column (Step 7)

Michael also adds a column to aggregate the County and State together (this is to disambiguate the map visualization in next step). For that, Michael right-clicks on a column header again and choose “Insert Column” then “Custom” and add the following formula:

= [County] & ", " & [State]

After validating the form, Michael sees the following table in Excel.

Figure 6. End of step 7 with new columns

Step 8. Visualize the data with PowerView.

Michael goes to the “Insert” tab in Excel, and then clicks on “PowerView”.

He then clicks on the Map button and configures the view as follows:

- Sum Size: Rank

- Location: County, State (our second new column)

- Color: Store count (our new aggregate column)

- (no other field is used)

And finally, Michael gets the following map showing the highest income counties, with the color indicating the number of existing stores for Michael’s company.

We believe this scenario demonstrates the value of Data Explorer in helping users discover data, filter and shape the data easily and combine with other data. Finally, users can analyze the data or build visualizations on top.

Please let us know what you think about Online Search, and Data Explorer in general!

As you might be aware, HDInsight is Microsoft’s own offering of Hadoop as a service. In this example, we are going to look at how data can be consumed from HDInsight. We will be using a dataset in HDInsight that contains historical stock prices for all stocks traded on the NYSE between 1970 and 2010. While this dataset is not too big compared to “Big Data” standards, it does represent many of the challenges posed by big data as far as end user consumption goes. In case you are interested in trying this yourself, the source of this data is Infochimps. You will need to get the data into an Azure Blob Storage account that is associated with your HDInsight cluster.

The goal of this post is to show you how to use this data to build a report of those stocks that are traded on the NYSE and are part of the S&P 500 index. This dataset by alone isn’t enough, because all it provides is price and volume information by stock symbol and date. So we will need to ultimately find company name/sector information from another dataset. More on that later.

Here’s a view of the report we are attempting to build:

The steps below show you how to build this interactive report.

Step 1: Connect to HDInsight and shape the data

The first step is to connect to HDInsight and get the data in the right shape. HDInsight is a supported data source in the Data Explorer ribbon.

If you are following along and don’t see HDInsight in the Other Sources dropdown, you need to get the latest update for Data Explorer.

Once the account details are provided, we will eventually be connected up to the HDFS filesystem view:

At this point, you will likely notice the first challenge. The data that we want to consume is scattered across dozens of files – and we absolutely need the data from all of these files. However, this is an easy problem for Data Explorer.

As a first step, we need to use Data Explorer to subset the files based on a condition, so that all unnecessary files are filtered out. We can use a condition that filters down to the list of files that contain “daily_price” in the filename.

After this step, we have just the files needed. Now for the magic.

Data Explorer has a really cool feature that lets you create a logical table out of multiple text files. You can “combine” multiple files in a filesystem view by simply clicking on the Combine icon in the Content column header.

Clicking on that icon produces this:

At this point, one of the top rows can be promoted as header using the feature Data Explorer provides for creating a header row. The rest of the header rows can be filtered out also using the filter capabilities.

A few more operations to hide unwanted columns will produce our final view:

Clicking on Done will start to run the query and stream the data down into Excel.

Step 2: Find the S&P 500 list of companies along with company information

At this point, the data should be streaming down into Excel. There is quite a bit of data that will find its way down into Excel. However, since we are not done with data shaping, we need to toggle a setting that will disable evaluation/download of the results for this particular query.

Clicking on Enable Download stops the download:

In order to fully build out the report, the next thing we need is the list of companies that are part of the S&P 500 index. We can try to find this in Data Explorer’s Online Search.

Searching for S&P 500 yields a few results:

The first one looks pretty close to the data we need. So we can import that into Excel by clicking Use:

Step 3: Merging the two tables, and subsetting to the S&P 500 price data

The last step in our scenario is to combine the two tables using Data Explorer. We can do this by clicking on the Merge button in the Data Explorer ribbon:

The Merge dialog lets us pick the tables we’d like to merge, along with the common columns between the two tables so that Data Explorer can do a join. Note that a left outer join is used when merging is done this way.

Clicking on Apply completes the merge, and we are presented with a resulting table:

Columns from the second table can be added by expanding and looking up columns from the NewColumn column:

The result of selecting the columns we’d like to add to the table produces this:

Note that there are many columns that have null values for the new columns. This is expected as we have more companies in the left table (the one we pulled from HDInsight).

A simple filtering out of nulls fixes the problem, and leaves us with what we need:

We are now left with the historical end of day figures for all companies in the S&P 500. Clicking on Done will now bring the data into Excel.

Step 4: Fix up a few types in PowerPivot and visualize in Power View

Once the data is downloaded, adding it to Excel’s data model (xVelocity) is easy. Clicking on the Load to data model link puts the data into the model in one click:

Once the data is in the data model, it can easily be modeled using Excel’s PowerPivot functionality.

In order for the visualization to work correctly, we need to adjust the types of the following columns in PowerPivot:

• date – this column needs to be converted to Date type
• stock_price_close – this column needs to be converted to Decimal type

Once this is done, adding the visualization via Power View is easy.

• We can insert a Power View from the Insert ribbon/tab in Excel.
• Delete any default visualizations. From the table named Query in the Power View fields list, select the following columns:
• date
• stock_price_close
• NewColumn.Company
• Change the visualization to a Line chart.

This will produce a visualization that shows all the companies on a single chart. We can then customize the chart and see just the companies we are interested in.

That’s all it takes to get all that data from HDInsight, and to combine that data with some publicly available information. Data Explorer’s Online Search is a good source for public data.

We hope this gives you an idea for how Data Explorer enables richer connectivity, discovery and data shaping scenarios while enhancing your Self Service BI experience in Excel. An interesting thought exercise will be to consider how you might accomplish this scenario without using Data Explorer.

Let us know what you think!

Data Explorer Team

We hope you have been enjoying Microsoft “Data Explorer” Preview for Excel.  We certainly have been having a fun time looking at the overall response and going through all the feedback you have been sending along. We would like to thank you for taking the time to send us feedback.

We would like to let you know that we just published an update that includes quite a few bug fixes and a handful of additional features that we think you will enjoy.

Now that it has been a few weeks since we initially released the preview, it is a good time to talk about how we plan on delivering updates to you while we are in this preview phase.

In the Data Explorer ribbon, you may have noticed an “Update” button.

Usually, this button is grayed out. When we do publish updates, you will notice that this button turns on and you can click on it to get details about the update. This is just a small convenience feature to help you get to the right update without much trouble.

To update your Data Explorer preview bits, you can just wait for this to light up. Of course, you can always go to the Data Explorer page on Microsoft Download Center to find the latest bits if you are still waiting for the update button to light up.

This update includes additional data sources – namely:

• Windows Azure HDInsight
• Teradata
• PostgreSQL

We also have improved the query editing functionality. If you go into the Settings dialog, you will find an option to “Enable Advanced Query Editing”

Once you do this, you will be able to edit an entire query by clicking on the new icon that appears in the formula bar.

Here’s what the query editing experience looks like:

Enabling “Advanced Query Editing” also allows you to write a query from scratch instead of using one of the pre-defined data sources.

There are a lot of bug fixes as well as I already mentioned. Give this build of Data Explorer a spin and let us know how you like it. Reach us via the Data Explorer forum.

Faisal Mohamood

Program Manager | Data Explorer