How to Make Use of Azure Cognitive Services

Azure Cognitive Services is a service that is available from your Azure portal which allows you to set-up accounts that enable the use of Microsoft’s pre-built algorithms and models for common advanced analytical functions such as Text Analytics, Recommendation Engines, speech and vision recognition as well as many more service-oriented APIs. These models have been trained and are ready for you to use and consume in your solutions, such as Power BI business intelligence solutions.

There is a very common use case emerging that I’m finding in the Cloud Analytics space that relates specifically to a portion of the Text Analytics function where there are a number of solutions that can take advantage of the Sentiment Analysis capabilities that Azure Cognitive Services provides. By sending in a text string to the API service, you can leverage the existing Microsoft sentiment engine to determine if the message that you sent in is positive, negative or neutral based upon a score.

The best way to demonstrate this is with Power BI. I am going to briefly touch on 2 of the most common social media sources to pull real-time data from and core the messages for sentiment analysis: Twitter & Facebook.

First, the easiest with Power BI (PBI) is Facebook. There is an out-of-the-box connector for Facebook included with PBI, so you can use Get Data to bring data directly into a Power BI model from Facebook very easily:

pbi4

Now, to hook into the Microsoft Sentiment Analysis, follow the method described very well on this PBI community page and from the associated PASS Virtual Chapter presentation on this very topic by Gil from DataChant: http://community.powerbi.com/t5/Community-Blog/Sentiment-Analysis-in-Power-BI/ba-p/55898. His full working sample is awesome and you can play around with it here: https://app.powerbi.com/view?r=eyJrIjoiMjU5NWM3MTMtMTg0NS00YzE2LTk0YTYtMmZkZWM4NjYwMmFjIiwidCI6IjIyNzNjNDFiLWI4ZDAtNDVhZi1iZWU2LWUwODQ5NmFlNjcxOCIsImMiOjN9.

To download and use a similar sample in the PBI Desktop, just download the PBIX from my Github. I recommend going into the detailed PBI Community link from Gil above, it rocks and gives you the background you need to customize this for yourself with step-by-step instructions on how this was made.

For the demo above that I uploaded to Github, I included Gil’s Trump & Clinton Facebook election sentiment analysis data on Tab 2 of the report view on PBI. Tab 1 has Microsoft BI Facebook sentiment data where I used the Microsoft BI Facebook page as the data source. Each of those datasets is run through the Cognitive Services API for Sentiment Analysis, part of the Text Analytics suite.

To make it work for you, follow Gil’s instructions on getting an API key from Cognitive Services and plug in your API Key in the GetSentimentResults function in the Power Query / Edit query tool. You can use the free limited service for Sentiment Analysis or go to the Azure Portal and create a full-use Cognitive Services account. The code for calling out to the API and saving the sentiment score in the PBI model is in a Power Query function call in the PBIX and looks like this:

(Source as table) as any =>
let
JsonRecords = Text.FromBinary(Json.FromValue(Source)),
JsonRequest = “{“”documents””: ” & JsonRecords & “}”,

JsonContent = Text.ToBinary(JsonRequest, TextEncoding.Ascii),
Response =
Web.Contents(“https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment?”,
[
Headers = [#”Ocp-Apim-Subscription-Key”= “your api key here”,
#”Content-Type”=”application/json”, Accept=”application/json”],
Content=JsonContent
]),
JsonResponse = Json.Document(Response,1252)
in
JsonResponse

As you can tell from my sample Sentiment Facebook dashboard below, I did not put as much time into the report as Gil did. So take the PBIX and add your own visuals to make it better!

pbi

Now onto the 2nd example, using Twitter data. There is no out-of-the-box connector directly into PBI for live Twitter feeds, so I used the Solution Template for Twitter (Marketing use case) from the Power BI Solution Template site: https://bpsolutiontemplates.com/.

That site provides you with a “wizard” that walks through the set-up of an Azure SQL Database, your Cognitive Services API as I mentioned above (free or the Azure account) and the Twitter handles/keywords that you want to use as a filter. This creates a much more robust solution to grab the Twitter data in that the solution template with spin-up Logic Apps, App Service and API connectors to Twitter and Sentiment Analysis on the backend instead of directly from Power BI as the first example with Facebook above.

So, essentially Power BI will just connect to your Azure SQL DB that you’ve chosen from the template and that SQL DB will get populated from the Azure App that is built automatically for you. This separates the data from the business logic and from the presentation layer in PBI. The code to call Twitter and Cognitive Services are all in the “middle tier” on the App Service in Azure, which you can open directly from your Azure Portal. Modify the API calls, strings and sentiment bins in that code:

//Sentiment analysis – Cognitive APIs
string sentiment = await MakeSentimentRequest(tweet);
sentiment = (double.Parse(sentiment) * 2 – 1).ToString(CultureInfo.InvariantCulture);
string sentimentBin = (Math.Floor(double.Parse(sentiment) * 10) / 10).ToString(CultureInfo.InvariantCulture);
string sentimentPosNeg = String.Empty;
if (double.Parse(sentimentBin) > 0)
{
sentimentPosNeg = “Positive”;
}
else if (double.Parse(sentimentBin) < 0)
{
sentimentPosNeg = “Negative”;
}
else
{
sentimentPosNeg = “Neutral”;
}

The out-of-the-box reports from these Power BI templates are quite good with a social graph visualization and a number of report tabs for different pivots of the Twitter data and sentiment scores.

pbi2

pbi3

Azure Marketplace Solutions

Yes, it’s been a while and I’m just now getting back into the swing of things, keeping MS-SQL Dude running, after my 2 year hiatus in the Open Source world.

To get back into the swing of things, I’ll share with you all a series of videos that I put together with the Microsoft Azure Partner team around the database, analytics and big data offerings from other software vendors that are available as solution template images on the Azure Marketplace:

We first started at the fundamental data level with my old friends at DataStax and their Cassandra database product

We then moved up the stack into batch analytics, streaming and distributed execution engines on Hadoop with my friends at Hortonworks

Lastly, we finished up with the end-user data visualization and BI layer from Looker

All of these data solutions are available from the Azure Marketplace link above.

You can also find several of the presentations from this series at my Slideshare site here.

 

Pentaho and HP Vertica – Big Data Analytics

Anyone going to the HP Vertica conference next week in Boston? (August 12, 2014)

If so, stop by and say Hi at the Pentaho booth in the expo center!

BTW, I uploaded a quick & short video that I threw together with no voiceover, but shows you how easy & quick it is to make a Dashboard in Pentaho 5.1 against a Big Data source like HP Vertica.

See it here.

HP Vertica is a world-class MPP Analytical Database and is a perfect match for Pentaho’s Analytics Suite. In the video you’ll see me through together a quick Dashboard by stacking Analyzer report components that are querying a Mondrian cube.

The business model is based on Vertica’s VMart data warehouse sample data and the semantic models is generated automatically through Pentaho’s auto modeler. I’m using our Web-based Thin Modeler to modify the model a bit before publishing it for ad-hoc interactive analysis.

Hope to see you @ the HP Vertica conference in Boston next week!  Best, Mark

Migrate Adventure Works Sales Cubes from SSAS MOLAP to Mondrian ROLAP

In the 2 previous blog entries about recreating the SSAS example cubes for Adventure Works sales BI on Pentaho, I focused on generating new departmental-sized ROLAP cubes through the thin client Auto Modeler tools here & here.

In this entry, I’m going to show you how you can move over to the Pentaho BA Suite quickly by getting started with your existing SSAS cubes, such as the Adventure Works sales cubes, by migrating them pretty much as-is to the Pentaho ROLAP engine called Mondrian. This way, you can use the modeling tools that you are already familiar with (i.e. BIDS) and just convert them to Mondrian. Once you are converted over to the Pentaho platform, then you can start playing around with Pentaho analytics and manage the new version of the cubes in Pentaho.

In case you didn’t already do so, make sure you go here to download the evaluation of the Pentaho Business Suite here. And here is the Codeplex link for the open source download of the Adventure Works SSAS 2012 MOLAP cubes that we’ll use for this demonstration.

Make sure you also download the SQL Server 2012 JDBC driver and the tool that I built to help you migrate your online SSAS cubes from the SQL Server Analysis Server to Mondrian XML schema output files using AMO here, called SSAS2MONDRIAN. The full source code and C# project is available on GitHub in case you want to add more to it, have a look around, etc. The tool needs more features and error catching, so please contribute your ideas back to the source project!

So basically, instead of recreating a new OLAP model in Pentaho’s Analyzer, we are going to just migrate as-is from SSAS. Instead of rewriting the details of some of the primary differences between the 2 engines that you need to be aware of, I’m going to just link you back to my last posting on this topic at KromerBigData. You’ll need to become familiar with the Mondrian schema and MDX differences because we don’t have a complete migration tool that can do it all for you. Instead, the ssas2mondrian utility will take your model outline of dimensions, measures, expressions, attributes, hierarchies and  measure groups and convert them to Mondrian descriptors and names, but leaving out decisions about conflicts and conversion gaps in the hands of the BI developer.

Ok, once you’ve loaded the AdventureWorks 2012 DW database and the MOLAP cubes from Codeplex into SQL Server, you should see them appear in your SSMS:

s0

 

Next, run the ssas2mondrian utility and redirect the output to an XML file so that we’ll be able to open the model in Pentaho Schema Workbench to make it ready for Mondrian:

mon0

You can see some of the output in that PowerShell screenshot above. I first ran the conversion utility without redirecting the output to a file, so some of the stdout is visible. I selected the server, cube, SSAS databsae and chose a new name for the resulting Mondrian schema, which is what will appear to report users when they build reports in Pentaho Analyzer.

Notice the conversion utility just using Console.WriteLine to output a Mondrian schema format. But, now I can open that schema file in Pentaho Schema Workbench (PSW) and work with it to clean things up and make it ready for Pentaho Analytics:

psw2

Make sure that you connect to the AdventureWorksDW2012 database from PSW as well under Database Connections. Remember that Mondrian is ROLAP only, so you will always send queries to the source database in Mondrian which will handle caching as well. Also take notice of the red check marks above. That means that you’ll want to open those nodes in the tree and look at the error in the bottom of the PSW screen. It is possible that some of the table names are actually named queries in the SSAS cube model, which means that those will need to become views in the source Adventure Works database to resolve the red check in PSW for Mondrian.

psw1

You can now use that ROLAP model to build Pentaho Analyzer reports and continue to maintain the models directly in Pentaho Mondrian, taking SSAS away from the solution. Or, if you like designing models in BIDS, build your cube there and then run ssas2mondrian to load the model into the Pentaho suite!

OPASS Discussions & Big Data in the Real World

If you are in the Orlando Sanford area tomorrow night (Thursday October 24 @ 6;30 register here) then come join the Orlando PASS gang where I will lead a discussion on Big Data and Big Data Analytics in the real world. I am basing this discussion on the systems that we designed and took to market for Razorfish and will include demos of HDInsight, Hadoop, NoSQL databases, Big Data Analytics with Pentaho and SQL Server. Here is the Slideshare link to the presentation for the meeting.

SQL Dude Adventures and BI Blogging

Hi Everyone! My apologies for the gap in blogging … It’s been a long couple of months transition over to the Open Source Software world and relocating to Orlando for Pentaho. It’s been an amazing adventure so far and I’ve just started transitioning my work to the hybrid OSS / commercial world for this MSSQLDUDE blog, SQL PASS, local BI Meetups & the SQL Server Pro Magazine BI Blog.

I will post my Adventure Works on Pentaho OLAP blog post this evening to kick it off and if you are looking for the more purist Big Data Analytics and Pentaho coverage, check out my Big Data Analytics blog here.

See ya soon! Best, Mark

What Makes your Data Warehouse a “Big Data Warehouse”?

I’ve been closely observing the evolution of marketing of the classic database and data warehouse products over the past 2 years with great interest. Now that Big Data is top-of-mind of most CIOs in corporations around the globe, traditional data vendors like IBM, Oracle, Teradata and Microsoft are referring to their platforms as “Big Data” or “Big Data Warehouses”.

I guess, in the final analysis, this is really an attempt by data vendors at shifting perceptions and melding CIO thinking about Big Data away from Apache Hadoop, Cloudera and Hortonworks and toward their own platforms. Certainly, there are some changes taking place to those traditional data warehouse platforms (MPP, in-memory, columnstore) that are important for workloads that are classic “Big Data” use cases: clickstream analysis, big data analytics, log analytics, risk modeling … And most of those vendors will even tack-on a version of Hadoop with their databases!

But this is not necessarily breaking new ground or an inflection point in terms of technologies. Teradata pioneered MPP decade ago, Oracle led the way with smart caching and proved (once again) the infamous bottleneck in databases is I/O. Columnar databases like Vertica proved their worth in this space and that led to Microsoft and Oracle adopting those technologies, while Aster Data led with MapReduce-style distributed UDFs and analytics, which Teradata just simply bought up in whole.

In other words, the titans in the data market finally felt enough pressure from their core target audiences that Hadoop was coming out of the shadows and Silicon Valley to threaten their data warehouse market share that you will now hear these sorts of slogans from traditional data warehouses:

Oraclehttp://www.oracle.com/us/technologies/big-data/index.html. Oracle lists different products for dealing with different “Big Data” problems: acquire, organize and analyze. The product page lists the Oracle Big Data Appliance, Exadata and Advanced Analytics as just a few products for those traditional data warehouse problems. Yikes.

Teradata: In the world of traditional DWs, Teradata is the Godfather and pioneered many of the concepts that we are talking about today for Big Data Analytics and Big Data DWs. But Aster Data is still a separate technology and technology group under Teradata and sometimes they step on their own messaging by forcing their EDW database products into the same “Big Data” space as Aster Data: http://www.prnewswire.com/news-releases/latest-teradata-database-release-supports-big-data-and-the-convergence-of-advanced-analytics-105674593.html.

But the fact remains that “Hadoop” is still seen as synonymous with “Big Data” and the traditional DW platforms had been used in many of those same scenarios for decades. Hadoop has been seen as an alternative means to provide Big Data Analaytics at a lower cost per scale. Just adding Hadoop to an Oracle Exadata installation, for example, doesn’t solve that problem for customers outside of the original NoSQL and Hadoop community: Yahoo, Google, Amazon, etc.

So what are your criteria for a database data warehouse to qualify as a “Big Data Warehouse”? Here are a few for me that I use:

  1. MPP scale-out nodes
  2. Column-oriented compression and data stores
  3. Distributed programming framework (i.e. MapReduce)
  4. In-memory options
  5. Built-in analytics
  6. Parallel and fast-load data loading options

To me, the “pure-play” Big Data Analytics “warehouses” are: Vertica (HP), Greenplum (EMC) and Aster (Teradata). But the next-generation of platforms that will include improved distributed access & programming, better than today’s MapReduce and Hive, will be Microsoft with PDW & Polybase, Teradata’s appliance with Aster & SQL-H and Cloudera’s Impala, if you like Open Source Software.