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Big Data - A Microsoft Tools Approach

BuckWoody — Mon, 20 Feb 2012 21:16:00 GMT

(As with all of these types of posts, check the date of the latest update I’ve made here. Anything older than 6 months is probably out of date, given the speed with which we release new features into Windows and SQL Azure)

I don’t normally like to discuss things in terms of tools. I find that whenever you start with a given tool (or even a tool stack) it’s too easy to fit the problem to the tool(s), rather than the other way around as it should be.

That being said, it’s often useful to have an example to work through to better understand a concept. But like many ideas in Computer Science, “Big Data” is too broad a term in use to show a single example that brings out the multiple processes, use-cases and patterns you can use it for.

So we turn to a description of the tools you can use to analyze large data sets. “Big Data” is a term used lately to describe data sets that have the “Four V’s” as a characteristic, but I have a simpler definition I like to use:

Big Data involves a data set too large to process in a reasonable period of time

I realize that’s a bit broad, but in my mind it answers the question and is fairly future-proof. The general idea is that you want to analyze some data, and using whatever current methods, storage, compute and so on that you have at hand it doesn’t allow you to finish processing it in a time period that you are comfortable with. I’ll explain some new tools you can use for this processing.

Yes, this post is Microsoft-centric. There are probably posts from other vendors and open-source that cover this process in the way they best see fit. And of course you can always “mix and match”, meaning using Microsoft for one or more parts of the process and other vendors or open-source for another. I never advise that you use any one vendor blindly - educate yourself, examine the facts, perform some tests and choose whatever mix of technologies best solves your problem.

At the risk of being vendor-specific, and probably incomplete, I use the following short list of tools Microsoft has for working with “Big Data”. There is no single package that performs all phases of analysis. These tools are what I use; they should not be taken as a Microsoft authoritative testament to the toolset we’ll finalize for a given problem-space. In fact, that’s the key: find the problem and then fit the tools to that.

Process Types

I break up the analysis of the data into two process types. The first is examining and processing the data in-line, meaning as the data passes through some process. The second is a store-analyze-present process.

Processing Data In-Line

Processing data in-line means that the data doesn’t have a destination - it remains in the source system. But as it moves from an input or is routed to storage within the source system, various methods are available to examine the data as it passes, and either trigger some action or create some analysis.

You might not think of this as “Big Data”, but in fact it can be. Organizations have huge amounts of data stored in multiple systems. Many times the data from these systems do not end up in a database for evaluation. There are options, however, to evaluate that data real-time and either act on the data or perhaps copy or stream it to another process for evaluation.

The advantage of an in-stream data analysis is that you don’t necessarily have to store the data again to work with it. That’s also a disadvantage - depending on how you architect the solution, you might not retain a historical record. One method of dealing with this requirement is to trigger a rollup collection or a more detailed collection based on the event.

StreamInsight - StreamInsight is Microsoft’s “Complex Event Processing” or CEP engine. This product, hooked into SQL Server 2008R2, has multiple ways of interacting with a data flow. You can create adapters to talk with systems, and then examine the data mid-stream and create triggers to do something with it. You can read more about StreamInsight here: http://msdn.microsoft.com/en-us/library/ee391416(v=sql.110).aspx

BizTalk - When there is more latency available between the initiation of the data and its processing, you can use Microsoft BizTalk. This is a message-passing and Service Bus oriented tool, and it can also be used to join system’s data together than normally does not have a direct link, for instance a Mainframe system to SQL Server. You can learn more about BizTalk here: http://www.microsoft.com/biztalk/en/us/overview.aspx

.NET and the Windows Azure Service Bus - Along the same lines as BizTalk but with a more programming-oriented design are the Windows and Windows Azure Service Bus tools. The Service Bus allows you to pass messages as well, and opens up web interactions and even inter-company routing. BizTalk can do this as well, but the Service Bus tools use an API approach for designing the flow and interfaces you want. The Service Bus offerings are also intended as near real-time, not as a streaming interface. You can learn more about the Windows Azure Service Bus here: http://www.windowsazure.com/en-us/home/tour/service-bus/ and more about the Event Processing side here: http://msdn.microsoft.com/en-us/magazine/dd569756.aspx

Store-Analyze-Present

A more traditional approach with an organization’s data is to store the data and analyze it out-of-band. This began with simply running code over a data store, but as locking and blocking became an issue on a file system, Relational Database Management Systems (RDBMs) were created. Over time a distinction was made between data used in an online processing system, meant to be highly available for writing data (OLTP) and systems designed for analytical and reporting purposes (OLAP).

Later the data grew larger than these systems were designed for, primarily due to consistency requirements. In analysis, however, consistency isn’t always a requirement, and so file-based systems for that analysis were re-introduced from the Mainframe concepts, with new technology layered in for speed and size.

I normally break up the process of analyzing large data sets into four phases:

Source and Transfer - Obtaining the data at its source and transferring or loading it into the storage; optionally transforming it along the way
Store and Process - Data is stored on some sort of persistence, and in some cases an engine handles the acquisition and placement on persistent storage, as well as retrieval through an interface.
Analysis - A new layer introduced with “Big Data” is a separate analysis step. This is dependent on the engine or storage methodology, is often programming language or script based, and sometimes re-introduces the analysis back into the data. Some engines and processes combine this function into the previous phase.
Presentation - In most cases, the data wants a graphical representation to comprehend, especially in a series or trend analysis. In other cases a simple symbolic representation, similar to the “dashboard” elements in a Business Intelligence suite. Presentation tools may also have an analysis or refinement capability to allow end-users to work with the data sets. As in the Analysis phase, some methodologies bundle in the Analysis and Presentation phases into one toolset.

Source and Transfer

You’ll notice in this area, along with those that follow, Microsoft is adopting not only its own technologies but those within open-source. This is a positive sign, and means that you will have a best-of-breed, supported set of tools to move the data from one location to another. Traditional file-copy, File Transfer Protocol and more are certainly options, but do not normally deal with moving datasets.

I’ve already mentioned the ability of a streaming tool to push data into a store-analyze-present model, so I’ll follow up that discussion with the tools that can extract data from one source and place it in another.

SQL Server Integration Services (SSIS)/SQL Server Bulk Copy Program (BCP) - SSIS is a SQL Server tool used to move data from one location to another, and optionally perform transform or other processes as it does so. You are not limited to working with SQL Server data - in fact, almost any modern source of data from text to various database platforms is available to move to various systems. It is also extremely fast and has a rich development environment. You can learn more about SSIS here: http://msdn.microsoft.com/en-us/library/ms141026.aspx BCP is a tool that has been used with SQL Server data since the first releases; it has multiple sources and destinations as well. It is a command-line utility,and has some limited transform capabilities. You can learn more about BCP here: http://msdn.microsoft.com/en-us/library/ms162802.aspx

Sqoop - Tied to Microsoft’s latest announcements with Hadoop on Windows and Windows Azure, Sqoop is a tool that is used to move data between SQL Server 2008R2 (and higher) and Hadoop, quickly and efficiently. You can read more about that in the Readme file here: http://www.microsoft.com/download/en/details.aspx?id=27584

Application Programming Interfaces - API’s exist in most every major language that can connect to one data source, access data, optionally transforming it and storing it in another system. Most every dialect of the .NET-based languages contain methods to perform this task.

Store and Process

Data at rest is normally used for historical analysis. In some cases this analysis is performed near real-time, and in others historical data is analyzed periodically. Systems that handle data at rest range from simple storage to active management engines.

SQL Server - Microsoft’s flagship RDBMS can indeed store massive amounts of complex data. I am familiar with a two systems in excess of 300 Terabytes of federated data, and the Pan-Starrs project is designed to handle 1+ Petabyte of data. The theoretical limit of SQL Server DataCenter edition is 540 Petabytes. SQL Server is an engine, so the data access and storage is handled in an abstract layer that also handles concurrency for ACID properties. You can learn more about SQL Server here: http://www.microsoft.com/sqlserver/en/us/product-info/compare.aspx

SQL Azure Federations - SQL Azure is a database service from Microsoft associated with the Windows Azure platform. Database Servers are multi-tenant, but are shared across a “fabric” that moves active databases for redundancy and performance. Copies of all databases are kept triple-redundant with a consistent commitment model. Databases are (at this writing - check http://WindowsAzure.com for the latest) capped at a 150 GB size limit per database. However, Microsoft released a “Federation” technology, allowing you to query a head node and have the data federated out to multiple databases. This improves both size and performance. You can read more about SQL Azure Federations here: http://social.technet.microsoft.com/wiki/contents/articles/2281.federations-building-scalable-elastic-and-multi-tenant-database-solutions-with-sql-azure.aspx

Analysis Services - The Business Intelligence engine within SQL Server, called Analysis Services, can also handle extremely large data systems. In addition to traditional BI data store layouts (ROLAP, MOLAP and HOLAP), the latest version of SQL Server introduces the Vertipaq column-storage technology allowing more direct access to data and a different level of compression. You can read more about Analysis Services here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/analysis-services.aspx and more about Vertipaq here: http://msdn.microsoft.com/en-us/library/hh212945(v=SQL.110).aspx

Parallel Data Warehouse - The Parallel Data Warehouse (PDW) offering from Microsoft is largely described by the title. Accessed in multiple ways including using Transact-SQL (the Microsoft dialect of the Structured Query Language), This is an MPP appliance scaling in parallel to extremely large datasets. It is a hardware and software offering - you can learn more about it here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/data-warehousing/pdw.aspx

HPC Server - Microsoft’s High-Performance Computing version of Windows Server deals not only with large data sets, but with extremely complicated computing requirements. A scale-out architecture and inter-operation with Linux systems, as well as dozens of applications pre-written to work with this server make this a capable “Big Data” system. It is a mature offering, with a long track record of success in scientific, financial and other areas of data processing. It is available both on premises and in Windows Azure, and also in a hybrid of both models, allowing you to “rent” a super-computer when needed. You can read more about it here: http://www.microsoft.com/hpc/en/us/product/cluster-computing.aspx

Hadoop - Pairing up with Hortonworks, Microsoft has released the Hadoop Open-Source system - including HDFS and a Map/Reduce standardized software, Hive and Pig - on Windows and the Windows Azure platform. This is not a customized version; off-the-shelf concepts and queries work well here. You can read more about Hadoop here: http://hadoop.apache.org/common/docs/current/ and you can read more about Microsoft’s offerings here: http://hortonworks.com/partners/microsoft/ and here: http://social.technet.microsoft.com/wiki/contents/articles/6204.hadoop-based-services-for-windows.aspx

Windows and Azure Storage - Although not an engine - other than a triple-redundant, immediately consistent commit - Windows Azure can hold terabytes of information and make it available to everything from the R programming language to the Hadoop offering. Binary storage (Blobs) and Table storage (Key-Value Pair) data can be queried across a distributed environment. You can learn more about Windows Azure storage here: http://msdn.microsoft.com/en-us/library/windowsazure/gg433040.aspx

Analysis

In a “Big Data” environment, it’s not unusual to have a specialized set of tasks for analyzing and even interpreting the data. This is a new field called “data Science”, with a requirement not only for computing, but also a heavy emphasis on math.

Transact-SQL - T-SQL is the dialect of the Structured Query Language used by Microsoft. It includes not only robust selection, updating and manipulating of data, but also analytical and domain-level interrogation as well. It can be used on SQL Server, PDW and ODBC data sources. You can read more about T-SQL here: http://msdn.microsoft.com/en-us/library/bb510741.aspx

Multidimensional Expressions and Data Analysis Expressions - The MDX and DAX languages allow you to query multidimensional data models that do not fit well with typical two-plane query languages. Pivots, aggregations and more are available within these constructs to query and work with data in Analysis Services. You can read more about MDX here: http://msdn.microsoft.com/en-us/library/ms145506(v=sql.110).aspx and more about DAX here: http://www.microsoft.com/download/en/details.aspx?id=28572

HPC Jobs and Tasks - Work submitted to the Windows HPC Server has a particular job - essentially a reservation request for resources. Within a job you can submit tasks, such as parametric sweeps and more. You can learn more about Jobs and Tasks here: http://technet.microsoft.com/en-us/library/cc719020(v=ws.10).aspx

HiveQL - HiveQL is the language used to query a Hive object running on Hadoop. You can see a tutorial on that process here: http://social.technet.microsoft.com/wiki/contents/articles/6628.aspx

Piglatin - Piglatin is the submission language for the Pig implementation on Hadoop. An example of that process is here: http://blogs.msdn.com/b/avkashchauhan/archive/2012/01/10/running-apache-pig-pig-latin-at-apache-hadoop-on-windows-azure.aspx

Application Programming Interfaces - Almost all of the analysis offerings have associated API’s - of special note is Microsoft Research’s Infer.NET, a new language construct for framework for running Bayesian inference in graphical models, as well as probabilistic programming. You can read more about Infer.NET here: http://research.microsoft.com/en-us/um/cambridge/projects/infernet/

Presentation

Lots of tools work in presenting the data once you have done the primary analysis. In fact, there’s a great video of a comparison of various tools here: http://msbiacademy.com/Lesson.aspx?id=73 Primarily focused on Business Intelligence. That term itself is now not as completely defined, but the tools I’ll show below can be used in multiple ways - not just traditional Business Intelligence scenarios. Application Programming Interfaces (API’s) can also be used for presentation; but I’ll focus here on “out of the box” tools.

Excel - Microsoft’s Excel can be used not only for single-desk analysis of data sets, but with larger datasets as well. It has interfaces into SQL Server, Analysis Services, can be connected to the PDW, and is a first-class job submission system for the Windows HPC Server. You can watch a video about Excel and big data here: http://www.microsoft.com/en-us/showcase/details.aspx?uuid=e20b7482-11c9-4965-b8f0-7fb6ac7a769f and you can also connect Excel to Hadoop: http://social.technet.microsoft.com/wiki/contents/articles/how-to-connect-excel-to-hadoop-on-azure-via-hiveodbc.aspx

Reporting Services - Reporting Services is a SQL Server tool that can query and show data from multiple sources, all at once. It can also be used with Analysis Services. You can read more about Reporting Services here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/reporting-services.aspx

Power View - Power View is a “Self-Service” Business Intelligence reporting tool, which can work with on-premises data in addition to SQL Azure and other data. You can read more about it and see videos of Power View in action here: http://www.microsoft.com/sqlserver/en/us/future-editions/business-intelligence/SQL-Server-2012-reporting-services.aspx

SharePoint Services - Microsoft has rolled several capable tools in SharePoint as “Services”. This has the advantage of being able to integrate into the working environment of many companies. You can read more about lots of these reporting and analytic presentation tools here: http://technet.microsoft.com/en-us/sharepoint/ee692578

This is by no means an exhaustive list - more capabilities are added all the time to Microsoft’s products, and things will surely shift and merge as time goes on. Expect today’s “Big Data” to be tomorrow’s “Laptop Environment”.

The Data Scientist

BuckWoody — Tue, 15 Nov 2011 15:00:18 GMT

A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it.

In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets.

The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly.

I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas.

Persistence

The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all?

This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization.

Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to.

Processing

Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on.

A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one.

Presentation

This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays.

This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does.

And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist.

Why I’m excited

I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail.

Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data.

So watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.