A Sneak Peek at Our New HTML 5 UI and Geek Love for Some of the Libraries Used in Building AnalyticsPBI4Azure

By Terence Craig

Drumroll please! After nearly a year of development work, we are about to offer early access to the first real-time/streaming analytics solution software appliance for the cloud – AnalyticsPBI for Azure.  There will be more forthcoming on the product launch but the new UI is so cool I had to show it off a bit.

We will be following up with a formal launch and Early Access Program (EAP) signups in the next couple of weeks so watch this space and patternbuilders.com for details – the big data analytics market is about to change in a big way! Here’s a sneak peek on what we’ve been working on.

For the geek part of my blog I am going to give a shout out to three libraries that we are using – all have made a huge difference in the product’s performance, scalability, and usability. The first two libraries come from Microsoft – Reactive Extensions and TPL Dataflow.  The third library is the open source math and statistics library, Math.Net.

Events to Measures – Scalable Analytics Calculations using PatternBuilders in the Cloud

By Terence Craig

One part of the secret sauce that enables PatternBuilders to provide more accessible and performant user experiences for both creators and consumers of streaming analytics models is its infrastructure. Our infrastructure makes it easy to combine rich search capabilities for a diverse set of standard analytics that can be used to create more complex streaming analytics models. This post will describe how we create those standard analytics that we call Measures.

In my last post about our architecture, we delved into how we used custom SignalReaders as the point of entry for data into Analytics PBI.  We’ve tightened up our nomenclature a bit since our last post, so it’s worth reviewing some of our definitions:

Nomenclature	Description
Feed	An external source of data to be analyzed.  These can include truly real-time feeds such as stock-tickers, the Twitter firehose, or batch feeds, such as CSV files converted to data streams.
Event	An external event within a Feed that analysis will be performed on. For example, a stock tick, RFID read, PBI performance event, tweet, etc.  AnalyticsPBI can support analysis on any type of event as long as it has one or more named numeric fields and a date.  An Event can have multiple Signals.
Signal	A single numeric data element within an Event, tagged with the metadata that accompanied the Event, plus any additional metadata (to use NSA parlance) applied by the FeedReader. For example, a stock tick would have Signals of Price and Volume among others.
Tag	A string representing a piece of metadata about an Event.  Tags are combined to form Indexes for both Events and Measures.
FeedReader (formerly SignalReader)	A service written by PatternBuilders, customers, or third parties to read particular Feed(s), convert the metadata to Tags, and potentially add metadata from other sources to create Events.  Simple examples include a CSV reader and a stock tick reader. An example of a more complex reader is the reader we have created for the University of Sydney project that filters the Twitter firehose for mentions of specific stock symbols and hyperlinks to major media articles and then creates an Event that includes a Signal derived from the sentiment scores of those linked articles.  That reader was discussed here.A FeedReader’s primary responsibility is to create and index an object that converts “raw data” received from one or more Feeds to an Event. To accomplish this it does the following: Captures an Event from a feed – stock ticker, RFID channel, the Twitter firehose, etc. Uses the Event itself and any appropriate external data to attach or enrich metadata and numeric data to the Event. Creates a MasterIndex from all of the metadata attached to the Event. This MasterIndex and the Date associated with this Event is used to create Measures and Models later on in the process.  It can also attach geo data if appropriate. Extracts the numeric Signals for that Event. Pushes the Event object onto a named queue – the “EventToBeCalculatedQueue”–for processing. This queue, like all PatternBuilders queues, has a pluggable implementation. It can be in memory (cheaper, and faster) or persistent (more costly and slightly slower). One of the great advantages of the various cloud services, including our reference platform Azure, is the availability of scalable, fast, reliable, persistent queues.
Measure	A basic calculation that is generated automatically by the PatternBuilders calculation service and persisted. Measures are useful in and of themselves but they are also used to dynamically  generate results for more complex streaming Analytic Models.

As the topic of this post is Events to Measures, let’s create a simple Measure and follow it thru the process. For this purpose, we’ll be working with a simplified StockFeedReader that will create a tick Event from a tick feed that includes two Signals – Volume and Price – for stock symbols on a minute-by-minute basis. The reader will enrich the Feed’s raw tick data with metadata about the company’s industries and locations. After enrichment, the JSON version of the event would look like this:

{
     "Feed": "SampleStockTicker",
     "FeedGranularity": "Minute",
     "EventDate": "Fri, 23 Aug 2013 09:13:32 GMT",
     "MasterIndex": "AcmeSoftware:FTSE:Services:Technology",
     "Locations":  [
          {
              "Americas Sales Office": {
                  "Lat": "40.65",
                  "Long": "73.94"
               }
          }
          {
               "Europe Sales Office": {
                  "Lat": "51.51",
                  "Long": "0.12"
               }
          }
      ],
      "Tags":  [
          {
              "Tag1": "AcmeSoftware",
              "Tag2": "Technology",
              "Tag3": "FTSE"
          }
       ],
       "Signals":  [
          {
               "Price": "20.00",
               "Volume": "10000"
          }
       ]
}

Note that there is a MasterIndex field that is a concatenation of all the Tags about the tick. When the MasterIndex is persisted, it is actually stored in a more space efficient format but we will use the canonical form of the index as shown above throughout this post for clarity.

A MasterIndex has two purposes in life:

1. To allow the user to easily find a Signal by searching for particular Tags.
2. To act as the seed for creating indexes for Measures and Models. These indexes, along with a date range, are all that is required to find any analytic calculations in the system.

Once an Event has been created by a FeedReader, the FeedReader uses an API call to place the Event on the EventToBeCalculatedQueue. Based on beta feedback, we’ve adopted a pluggable queuing strategy. So before we go any further, let’s take a quick detour and talk briefly about what that means.  Currently, PatternBuilders supports three types of queues for Events:

• A pure in-memory queue. This is ideal for customers that want the highest performance and the lowest cost and who are willing to redo calculations in the unlikely event of machine failure. To keep failure risk as low as possible, we actually replicate the queues on different machines and optionally, place those machines in different datacenters.
• Cloud-based queues. Currently, we use Azure ServiceBus Queues but there is no reason that we couldn’t also support other PaSS vendor’s queues as well. The nice thing about ServiceBus queues is that the latest update from Microsoft for Windows 2012 allows them to be used on-premise against Windows Server with the same code as for the cloud—giving our customers maximum deployment flexibility.
• AMPQ protocol. This allows our customers to host FeedReaders and Event queues completely on-premise while using our calculation engine.  When combined with encrypted Tags, this allows our customers to keep their secrets “secret” and still enjoy the benefits of a real-time cloud analytics infrastructure.

Once the Event is placed on the IndexRequestQueue, it will be picked up by the first available Indexing server which monitors that queue for new Events (all queues and Indexing servers can be scaled up or down dynamically). The indexing service is responsible for creating measure indexes from the Tags associated with the Event.  This is the most performance critical part of loading data so forgive our skimpiness on implementation details but we are going to let our competition design this one for themselves :-).  Let’s just say that conceptually the index service creates a text search searchable index for all non-alias tags and any associated geo data. Some tags are simply aliases for other Tags and do not need measures created for them. For example, the symbol AAPL is simply and alternative for Apple Computer, so creating an average volume metric for both APPL and Apple is pointless since they will always be the same. Being able to find that value by searching on APPL or Apple on the other hand is amazingly useful and is fully supported by the system.

More formally:

<Geek warning on>

The indexes produced by an Event will be:

where n equals the number of non-alias tags and the upper limit for k is equal to n.

</Geek warning off>

From our simple example above, we have the following Tags: AcmeSoftware, FTSE, Services, and Technology.  This trivial example will produce the following Indexes:

AcmeSoftware
FTSE
Services
Technology
AcmeSoftware:FTSE
AcmeSoftware:Services
AcmeSoftware:Technology
FTSE:Services
FTSE:Technology
Services:Technology
AcmeSoftware:FTSE:Services
AcmeSoftware:FTSE:Technology
AcmeSoftware:Services:Technology
FTSE:Services:Technology
AcmeSoftware:FTSE:Services:Technology

The indexing service can perform parallel index creation across multiples cores and/or machines if needed. As Indexes are created, they, and each Signal in the Event, are combined into a calculation request object and placed in the MeasureCalculationRequestQueue queue that is monitored by the Measure Calculation Service.

The analytics service will take each index and use it to create/update all of the standard measures (Sum, Count, Avg, Standard Deviation, Last, etc.) for each unique combination of index and the Measure’s native granularity for each Signal (Granularity management is complex and will be discussed in my next post).

Specifically, the Calculation Service will remove a calculation request object from the queue and perform the following steps for all Measures appropriate to the Signal:

1. Attempt to retrieve the Measure from either cache or persistent storage.
2. If not found, create the Measure for the appropriate Date and Signal.
3. Perform the associated calculation and update the Measure.

Graphically the whole process looks something like this:

The advantages of this approach are manifold.  First, it allows for very sophisticated search capabilities across Measures and Models.  Second, it allows deep parallelization for Measure calculation. This parallelization allows us to scale the system by creating more Indexing Services and Calculation Services with no risk of contention and it is this scalability which allows us to provide near real-time, streaming updates for all Measures and most Models.  Each Index, time, and measure combination is unique and can be calculated by separate threads or even separate machines. A measure can be aggregated up from its native granularity using a pyramid scheme if the user requests it (say by querying for an annual number from a measure whose Signal has a native granularity of a minute). A proprietary algorithm prevents double counting for the edge cases where Measures with different Indexes are calculated from the same Events.

So now you’ve seen how we get from a raw stream to a Measure.  And how, along the way, we’re able to enrich meta and numeric data to enable both richer search capabilities and easier computation of more complex analytics models.  Later on, we explore how searches are performed and models are developed—you will see how this enrichment process makes exploring and creating complex analytics models much easier than the first generation of  big data, business intelligence, or desktop analytics systems.

However, before we get there we need to talk about how PatternBuilders handles dates and Granularity in more detail.  At our core, we are optimized for time-series analytics and how we deal with time is a critical part of our infrastructure. This is why in my next post we will be doing a deep (ok medium deep) dive into how we handle pyramidal aggregation and the always slippery concepts of time and streaming data. Thanks for reading and as always comments are free and welcomed!

“Hadoopla”

© Marqin Cook

By Terence Craig

I had to miss Strata due to a family emergency. While Mary picked up the slack for me at our privacy session, and by all reports did her usual outstanding job, I also had to cancel a Tuesday night Strata session sponsored by 10Gen on how PatternBuilders has used Mongo and Azure to create a next generation big data analytics system.   The good news is that I should have some time to catch up on my writing this week so look for a version of what would have been my 10Gen talk shortly. In the meantime, to get me back in the groove, here is a very short post inspired by a Forbes post written by Dan Everett of SAP on “Hadoopla”

As a CEO of a real-time big data analytics company that occasionally competes with parts of the Hadoop ecosystem, I may have some biases (you think?).  But I certainly agree that there is too much Hadoopla (a great term).  If our goal as an industry is to move Big Data out of the lab and into mainstream use by anyone other than the companies that thrive on and have the staff to support high maintenance and very high skill technologies, Hadoop is not the answer – it has too many moving parts and is simply too complex.

To quote from a blog post I wrote a year ago:

“Hadoop is a nifty technology that offers one of the best distributed batch processing frameworks available, although there are other very good ones that don’t get nearly as much press, including Condor and Globus.  All of these systems fit broadly into the High Performance, Parallel, or Grid computing categories and all have been or are currently used to perform analytics on large data sets (as well as other types of problems that can benefit from bringing the power of multiple computers to bear on a problem). The SETI project is probably the most well know (and IMHO, the coolest) application of these technologies outside of that little company in Mountain View indexing the Internet. But just because a system can be used for analytics doesn’t make it an analytics system…..“

Why is the industry so focused on Hadoop? Given the huge amount of venture capital that has been poured into various members of the Hadoop eco-system and that eco-system’s failure to find a breakout business model that isn’t hampered by Hadoop’s intrinsic complexity, there is ample incentive for a lot of very savvy folks to attempt to market around these limitations.  But no amount of marketing can change the fact that Hadoop is a tool for companies with elite programmers and top of the line computing infrastructures. And in that niche, it excels.  But it was not designed, and in my opinion will never see, broad adoption outside of that niche despite the seeming endless growth of Hadoopla.

Data Science: What the World Needs is Answers, Not Just Insights Part 2 (of 3)

By Marilyn Craig, Managing Director, Insight Voices

As you may or may not know, we are in the midst of a 3-part series on data science, covering roles, skills, etc.—generally what you should think about as well as what’s not as important (no matter what the latest articles say!). For Part 2, we have a guest poster—Marilyn Craig of Insight Voices. Marilyn is what I like to call a “classic quant.” She has been at the forefront of big data and data science before most people knew these terms (and spaces) existed and has been my go-to person whenever I had an analytics question (see title) that I needed an answer to. In this post, Marilyn looks at insights and makes the case for why we should all care far more about answers. Take it away Marilyn!

Here’s an interesting question for this new world order of Big Data Analytics: what’s an Insight and what’s an Answer? Sometimes they are the same, sometimes not. An insight is a piece of information or understanding. It may or may not be useful. It may or may not help your business improve, solve world hunger, or even make sense. An answer is always useful. It is the result of asking a question. And the best kinds of answers are those that solve the questions that you really care about.

Privacy and Big Data: Speaking at Strata East (NYC), Book Update, and Upcoming O’Reilly Webcast

By Mary Ludloff

There are times when Terence and I look at each other and say, “What on earth were we thinking?” And this is one of those times! PatternBuilders is crazy busy right now putting out release 3.0 of our Analytics Platform (the secret sauce for our analytics applications that we like to call data-science-in-a-box), ramping up on a funding round, working with partners on a University of Sydney research project on the impact of social media on a company’s stock price (a really fun project and a post about it is in the works), and, of course, supporting customers and prospects on their big data initiatives. So… since we did not have enough to do (sarcasm on), we decided it was time to update our book, participate in a pre-Strata East webcast, speak at the Strata Conference and the MongoDB User Group (that is collocated with Strata) in New York City! In the words of the immortal Bette Davis in All About Eve (and ever so slightly revised):

“Fasten your seat belts, it’s going to be a bumpy night ride!”

Really, what were we thinking?????

Speaking on Inman Connect Panel on Real Estate and Big Data

By Terence Craig

I apologize for falling behind on blogging, but between several new hires,  major partnerships, and the industry finally starting to understand the need for product-driven (instead of project-driven) big data, things have been very hectic. Good, but hectic.

I did want to pull my head off my keyboard for a minute to tell you about participating in the big data & real estate panel this Thursday at Connect San Francisco.  Our panel will be moderated by industry luminary Brad Inman @bradInman.

Real estate has always been a data-driven business and is relying more and more on the insights and operational nimbleness provided by big data.  For those of you who are scratching your heads and going, “Huh, Real Estate and big data?” – think about it for a minute.  The real estate industry is “using” big data to do all kinds of things and drive all kinds of business models, such as:

• Commercial landlords using smart thermostats and smart windows adjusted in real-time to save energy.
• Capturing real-time parking meter data to make real-time decisions about how long to leave a retail location open.
• Using real-time video analysis to stop vandalism before it happens.
• Offering sophisticated analytics – see consumer facing sites like Truila and Zillow.
• Risk Modeling – check out RMS. Like most of the PatternBuilders team, they were “doing” Big Data before the term was invented.

If you are attending the show, stop by and say hi. If you are interested in Big Data & Real Estate, look for our post-Connect blog next week. In it, we will talk about some great insights about the New York real estate market derived from a ton of data we grabbed from the NYC public data market which was then spun up in the PatternBuilders framework on our brand spanking new Microsoft Azure cloud beta release.

Big Data Tools Need to Get Out of the Stone Age: Business Users and Data Scientists Need Applications, Not Technology Stacks

By Terence Craig

Things have been crazy at PatternBuilders recently. The excitement and positive reactions to FinancePBI, our Financial Services big data analytics solution, from media, analysts, venture folks, cloud infrastructure partners, and users has been amazing.  Our new cross industry graphical big data correlation mashups are generating a lot of excitement as well—we like to call this feature Google Correlate on steroids. Check out how our newest partner analytics consultancy, InsightVoices, has used it to find relationships between stock prices and traffic sensor data.

Mary’s recent post on Strata West 2012 provides a great overview of how hot the hype cycle around big data has become (while managing to work in a plug for her favorite gory TV series as well). In case you’re still not convinced, here are some additional nuggets:

• The market for big data technology worldwide is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015, a compound annual growth rate (CAGR) of 40% (hat tip to IDC).
• The amount of big data being generated continues to grow exponentially, now being expected to double in two years. This is largely driven by social networks, smartphones, and really cool IP-enabled devices like the Fitbit and this IPhone-based brain scanning device by our new Strata buddy Tan Le at Emotiv Lifesciences. Yes, she is much smarter than us but we like her anyway!
• The White House is even doing its share, investing $200 million a year in access and funding to help propel big data sets, techniques, and technologies while giving a shout out to our friends at Data Without Borders.

Big Data and Cloud not a fit? Comments on Infoworld Article

By Terence Craig

Since Disqus seems to have completely eaten (bleh) my comment on @davidlinthicum’s very interesting InfoWorld post – Big data and the cloud: A far from perfect fit, I decided to just expand my comments and make a short blog post out of it. IMHO the problems that David is describing are more a reflection of problems with batch oriented technologies like Hadoop (more on my take on Hadoop here) in the cloud than a general problem for cloud based big data solutions.

Computing always has, and probably always will have, a bias towards creating batch focused technologies at the beginning of any large paradigm shift.   But as new technologies are absorbed, understood, and move from early adopter to more mainstream use, the batch paradigm will inevitably start to shift to streaming and real-time. We have seen this again and again (from punch cards to touch sensitive tablets, downloaded media to streaming media, DOM to SAX parsers, HTML to Ajax, paper maps to real-time GPS). The reason this evolution almost always occurs is simple: humans live and think in real-time and when our tools do as well we are more productive and happier.  So why do we have this bias for batch processing in our first generation computational technologies? Simply put, because batch processing is a lot easier.

No, Hadoop Doesn’t Own Big Data Analytics!

By Terence Craig

A number of folks have asked me if I was concerned about Microsoft’s  recent announcement that they would be partnering with HortonWorks and abandoning their own distributed processing technology for Hadoop.  While I thought this was an unfortunate choice on Microsoft’s part (the Dryad project’s implementation of multi-server Linq was pretty compelling), since HPC is a small part of Microsoft’s business, it probably made sense from a business standpoint.   In any case, we (as in all of us at PatternBuilders) are not concerned and just to be clear: we don’t believe that this announcement (or any other) means that the many Hadoop ecosystem players own the still forming big data analytics market.

That is not to say that the announcement isn’t proof of the strength of the Hadoop ecosystem. Hadoop is a nifty technology that offers one of the best distributed batch processing frameworks available, although there are other very good ones that don’t get nearly as much press, including Condor and Globus.  All of these systems fit broadly into the High Performance, Parallel, or Grid computing categories and all have been or are currently used to perform analytics on large data sets (as well as other types of problems that can benefit from bringing the power of multiple computers to bear on a problem). The SETI project is probably the most well know (and IMHO, the coolest) application of these technologies outside of that little company in Mountain View indexing the Internet.

Video: Big Data Made Easy. Sticky – see below for latest posts.