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Join me for a webinar where I discuss how the recent changes and trends in big data management effect the enterprise.  This event is sponsored by Red Rock and RockSolid.

Overview:

It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward. 

These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.

Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.

https://redrockevents.webex.com/redrockevents/onstage/g.php?t=a&d=869100422

 

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Often I think about challenges that organizations face with “Big Data”.  While Big Data is a generic and over used term, what I am really referring to is an organizations ability to disseminate, understand and ultimately benefit from increasing volumes of data.  It is almost without question that in the future customers will be won/lost, competitive advantage will be gained/forfeited and businesses will succeed/fail based on their ability to leverage their data assets.

It may be surprising what I think are the near term challenges.  Largely I don’t think these are purely technical.  There are enough wheels in motion now to almost guarantee that data accessibility will continue to improve at pace in-line with the increase in data volume.  Sure, there will continue to be lots of interesting innovation with technology, but when organizations like Google are doing 10PB sorts on 8000 machines in just over 6 hours – we know the technical scope for Big Data exists and eventually will flow down to the masses, and such scale will likely be achievable by most organizations in the next decade.

Instead I think the core problem that needs to be addressed relates to people and skills.  There are lots of technical engineers who can build distributed systems, orders of magnitude more who can operate them and fill them to the brim with captured data.  But where I think we are lacking skills is with people who know what to do with the data.  People who know how to make it actually useful.  Sure, a BI industry exists today but I think this is currently more focused on the engineering challenges of providing an organization with faster/easier access to their existing knowledge rather than reaching out into the distance and discovering new knowledge.  The people with pure data analysis and knowledge discovery skills are much harder to find, and these are the people who are going to be front and center driving the big data revolution.  People who you can give a few PB of data too and they can provide you back information, discoveries, trends, factoids, patterns, beautiful visualizations and needles you didn’t even know were in the haystack.

These are people who can make a real and significant impact on an organizations bottom line, or help solve some of the world’s problems when applied to R&D.  Data Geeks are the people to be revered in the future and hopefully we see a steady increase in people wanting to grow up to be Data Scientists. 

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Reading yesterday that the NSA has submitted a proposal to Apache to incubate their Accumulo platform.  This, according to the description, is a key/value store built over Hadoop which appears to provide similar function to HBase except it provides “cell level access labels” to allow fine grained access control.  This is something you would expect as a requirement for many applications built at government agencies like the NSA.  But this also is very important for organizations in health care and law enforcement etc where strict control is required to large volumes of privacy sensitive data.

An interesting part of this is how it highlights the acceptance of Hadoop.  Hadoop is no longer just a new technology scratching at the edges of the traditional database market.  Hadoop is no longer just used by startups and web companies.  This is highlighted by outputs like this from organizations such as the NSA.  This is also further highlighted by the amount of research and focus on Hadoop by the data community at large (such as last week at VLDB).  No, Hadoop has become a proven and trusted platform and is now being used by traditional and conservative segments of the market.  

 

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Conor O'Mahony over at IBM wrote a good post on a favorite topic of mine “The Future of the NoSQL, SQL, and RDBMS Markets”.  If this is of interest to you then I suggest you read his original post.  I replied in the comments but thought I would also repost my reply here.

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Hi Connor, I wish it was as simple as SQL & RDBMS is good for this and NoSQL is good for that.  For me at least, the waters are much muddier than that.

The benefit of SQL & RDBMS is that its general purpose nature has meant it can be applied to a lot of problems, and because of its applicability it is become mainstream to the point every developer on the planet can probably write basic SQL.  And it is justified, there aren’t many data problems you can’t through a RDBMS at and solve.

The problem with SQL & RDBMS, well essentially I see two.  Firstly, distributed scale is a problem in a small number of cases.  This can be solved by losing some of the generic nature of RDBMS and keeping SQL such as with MPP or attempts like Stonebraker’s NewSQL.  The other way is to lose RDBMS and SQL altogether to achieve scale with alternative key/value methods such as Cassandra, HBase etc.  But these NoSQL databases don’t seem to be the ones gaining the most traction.  From my perspective, the most “popular” and fastest growing NoSQL databases tend to be those which aren’t entirely focused on pure scale but instead focus first on the development model, such as Couch and MongoDB.  Which brings me to my second issue with SQL & RDBMS.

Without a doubt the way in which we build applications has changed dramatically over the last 20 years.  We now see much greater application volumes, much smaller developer teams, shorter development timeframes and faster changing requirements.  Much of what the RDBMS has offered developers – such as strong normalization, enforced integrity, strong data definition, documented schemas – have become less relevant to applications and developers.  Today I would suspect most applications use a SQL database purely as a application specific dumb datastore.  Usually there aren’t multiple applications accessing that database, there aren’t lots of direct data import/exports into other aplications, no third party application reporting, no ad-hoc user queries and the data store is just a repository for a single application to retain data purely for the purpose of making that application function.  Even several major ERP applications have fairly generic databases with soft schemas without any form of constraints of referential integrity.  This is just handled better, from a development perspective, in the code that populates it.

Now of course the RDBMS can meet this requirement – but the issue is the cost of doing this is higher than what it needs to be.  People write code with classes, RDBMS uses SQL.  The translation between these two structures, the plumbing code, can be in cases 50% of more of an applications code base (be that it hand-written code or automatic code generated by a modeling tool).  Why write queries if you are just retrieving and entire row based on key.  Why have a strict data model if you are the only application using it and you maintain integrity in the code?  Why should a change in requirements require you to now to go through the process of building a schema change script/process that has to have deployed sync’d with application version.  Why have cost based optimization when all the data access paths are 100% known at the time of code compilation?

Now I am still largely undecided on all of this.  I get why NoSQL can be appealing.  I get how it fits with today’s requirements, what I am unsure about if it is all very short sighted.  Applications being built today with NoSQL will themselves grow over time.  What may start off today as simple gets/puts within a soft schema’d datastore may overtime gain certain reporting or analytics requirements unexpected when initial development began.  What might have taken a simple SQL query to meet such a requirement in RDBMS now might require data being extracted into something else, maybe Hadoop or MPP or maybe just a simple SQL RDBMS – where it can be processed and re-extracted back into the NoSQL store in a processed form.  It might make sense if you have huge volumes of data but for the small scale web app, this could be a lot of cost and overhead to summarize data for simple reporting needs.

Of course this is all still evolving.  And RDBMS vendors and NoSQL are both on some form of convergence path.  We have already started hearing noises about RBDMS looking to offer more NoSQL like interfaces to the underlying data stores as well as the NoSQL looking to offer more SQL like interfaces to their repositories.  They will meet up eventually, but by then we will all be talking about something new like stream processing :)

Thanks Connor for the thought provoking post.

 

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My friends over at IA Ventures are looking both for an Analyst and for an Associate to their team.  If Big Data, New York and start-ups is in your blood then I can’t think of a better VC to be involved in. 

From the IA blog:

"IA Ventures funds early-stage Big Data companies creating competitive advantage through data and we’re looking for two start-up junkies to join our team – one full-time associate / community manager and one full time analyst. Because there are only four of us (we’re a start-up ourselves, in fact), we’ll need you to help us investigate companies, learn about industries, develop investment theses, perform internal operations, organize community events, and work with portfolio companies—basically, you can take on as much responsibility as you can handle."

Roger, Brad and the team continue to impress with their focus on Big Data, their strategic investments in monetizing data and knowledge of the industry in general.

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In life there are really two major types of data analytics.  Firstly, we don’t know what we want to know – so we need analytics to tell us what is interesting.  This is broadly called discovery.  Secondly, we already know what we want to know – we just need analytics to tell us this information, often repeatedly and as quickly as possible.  This is called anything from reporting or dashboarding through more general data transformation and so on.

Typically we are using the same techniques to achieve this.  We shove lots of data into a repository of some from (SQL, MPP SQL, NoSQL, HDFS etc) then run queries/ jobs/ processes across that data to retrieve the information we care about.  

Now this makes sense for data discovery.  If we don’t know what we want to know, having lots of data in a big pile that we can slice and dice in interesting ways is good.   But when we already know what we want to know, continued batch based processing across mounds of data to produce “updated” results of data, that is often changing in constantly, can be highly inefficient.

Enter Realtime Data Pipelines.  Data is fed in one end, results are computed in real time as data flows down the pipeline and come out the other end whenever relevant changes we care about occur.  Data Pipelines / workflow / streams are becoming much more relevant for processing massive amounts of data with real time results.  Moving relevant forms of analytics out of large repositories into the actual data flow from producer to consumer, I believe, will be a fundamental step forward in big data management.

There are some emerging technologies looking to address this, more details to follow.

 

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It is a constant, yet interesting debate in the world of big data.  What scales best?  OldSQL, NoSQL, NewSQL?

I have a longer post coming on this soon.  But for now, let me make the following comments.  Generally, most data technologies can be made to scale - somehow.  Scaling up tends not to be too much of an issue, scaling out is where the difficulties begin.  Yet, most data technologies can be scaled in one form or another to meet a data challenge even if the result isn’t pretty. 

What is best?  Well that comes down to the resulting complexity, cost, performance and other trade-offs.  Trade-offs are key as there are almost always significant concessions to be made as you scale up.

A recent example of mine, I was looking at scalability aspects of MySQL.  In particular, MySQL Cluster.  It is actually pretty easy to make it scale.  A 5 node cluster on AWS was able to scale to process a sustained transaction rate of 371,000 insert transactions – per second.   Good scalability yes, but there were many trade-offs made around availability, recoverability and non-insert query performance to achieve it.  But for the particular requirement I was looking at, it fitted very well.

So what is this all about?  Well, if a Social Network is  running MySQL in a sharded cluster to achieve the scale necessary to support their multi-millions users the fact that database technology x or database technology y can also scale with different “costs” or trade-offs doesn’t necessarily make it any better – for them.  If you, for example, have some of the smartest and talented MySQL developers on your team and can alter the code at a moment’s notice to meet a new requirement – that alone might make your choice of MySQL “better’ than using NoSQL database xyz from a proprietary vender where there may be a loss of flexibility and control from soup to nuts.

So what is my point?  Well I guess what I am saying is physical scalability is of course an important consideration in determining what is best.  But it is only one side of the coin.  What it “costs” you in terms of complexity, actual dollars, performance, flexibility, availability, consistency etc, etc are all important too.  And these are often relative, what is complex for you may not be complex for someone else.

 

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Yesterday I noticed this tweet by Andrei Savu: . This prompted me to read the related GigaOM article and then check out the HPCC Systems website.

If you're too lazy to read the article or visit that website:

HPCC (High Performance Computing Cluster) is a massive parallel-processing computing platform that solves Big Data problems. The platform is now Open Source!

HPCC Systems compares itself to Hadoop, which I think is completely justified in terms of functionality. Its product originated as a homegrown solution of LexisNexis Risk Solutions allowing its customers (banks, insurance companies, law enforcment and federal government) to quickly analyze billions of records, and as such it has been in use for a decade or so. It is now open sourced, and I already heard an announcement that Pentaho is its major Business Intelligence Partner.

Based on the limited information a made a quick analysis, which I emailed to the HPCC Systems CTO, Armando Escalante. My friend Jos van Dongen said it was a good analysis and told me I should post it. Now, I don't really have time to make a nice blog post out of it, but I figured it can't hurt to just repeat what I said in my emails. So here goes:

Just going by the documentation, I see a two real unique selling points in HPCC Systems as compared to Hadoop:

• Real-time query performance (as opposed to only analytic jobs). HPCC offers two difference setups, labelled Thor and Roxie. Functionalitywise, Thor should be compared to a Map/Reduce cluster like Hadoop: it's good for doing fairly long running analyses on large volumes of data. Roxie is a different beast, and designed to offer fast data access, supporting ad-hoc real-time queries
• Integrated toolset (as opposed to hodgepodge of third party tools). We're talking about an IDE, job monitoring, code repository, scheduler, configuration manager, and whatnot. This really looks like like big productivity boosters, which may make Big Data processing a lot more accessible to companies that don't have the kind of development teams required to work with Hadoop.

(there may be many more benefits, but these are just the ones I could clearly distill from the press release and the website)

Especially for Business Intelligence, Roxie maybe a big thing. If real-time Big Data queries could be integrated with Business Intelligence OLAP and reporting tools, then this is certainly a big thing. I can't disclose the details but I have trustworthy information that integration with Pentaho's Analysis Engine, the Mondrian ROLAP engine is underway and will be available as an Enterprise feature.

A few things that look different but which may not matter too much when looking at HPCC and Hadoop from a distance:

• ECL, the "Enterprise Control Language", which is a declarative query language (as opposed to just Map/Reduce). This initially seems like a big difference but Hadoop has tools like pig and sqoop and hive. Now, it could be that ECL is vastly superior to these hadoop tools, but my hunch is you'd have to be careful in how you position that. If you choose a head-on strategy in promoting ECL as opposed to pig, then the chances are that people will just spend their energy in discovering the things that pig can do and ECL cannot (not sure if those features actually exist, but that is what hadoop fanboys will look for), and in addition, the pig developers might simply clone the unique ECL features and the leveling of that playing field will just be a matter of time. This does not mean you shouldn't promote ECL - on the contrary, if you feel it is a more productive language than pig or any other hadoop tool, then by all means let your customers and prospects know. Just be careful and avoid downplaying the hadoop equivalents because that strategy could backfire.


• Windows support. It's really nice that HPCC Systems is available for Microsoft Windows, it makes that a lot easier for Microsoft shops (and there are a lot of them). That said, customers that really have a big-data problem will solve it no matter what their internal software policies are. So they'd happily start running hadoop on linux if that solves their problems.
• Maturity. On paper HPCC looks more mature than hadoop. It's hard to tell how much that matters though because hadoop has all the momentum. People might choose for hadoop because they anticipate that the maturity will come thanks to the sheer number of developers committing to that platform.

The only thing I can think of where HPCC looks like it has a disadvantage as compared to Hadoop is adoption rate and licensing. I hope these will prove not to be significant hurdles for HPCC, but I think that these might be bigger problems then they seem. Especially the AGPL licensing seems problematic to me.

The AGPL is not well regarded by anyone I know - not in the open source world. The general idea seems to be that even more than plain GPL3 it restricts how the software may be used. If the goal of open sourcing HPCC is to gain mindshare and a developer community (something that hadoop has done and is doing extremely well) then a more permissive license is really the way to go.

If you look at products like MySQL but also Pentaho - they are both very strongly corporately led products. The have a good number of users, but few contributions from outside the company, and this is probably due to a combination of GPL licensing and the additional requirement for handing over the copyright of any contributions to the company. Hence these products don't really benefit from an open source development model (or at least not as much as they could). For these companies, Open source may help initially to gain a lot of users, but those are in majority the users that just want a free ride: conversion rates to enterprise edition customers are quite low. It might be enough to make a decent buck, but eventually you'll hit a cap on how far you can grow. I'm not saying this is bad - you only need to grow as much as you have to, but it is something to be aware of.

Contrast this to Hadoop. The have a Apache 2.0 permissive license, and this results in many individuals but also companies contributing to the project. And there are still companies like Cloudera that manage to make a good living off of the services around their distribution of Hadoop. You don't lose the ability to develop add-ons either with this model - apache 2.0 allows all that. The difference with GPL (and AGPL) of course is that it allows this also to other users and companies. So the trick to stay on top in this model is to simply offer the best product (as opposed to being the sole holder of the copyright to he code).

Anyway - that is it for now - I hope this is helpful.
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Well as predicted, with Aster Data recently being picked up by Teradata most of the key new generation MPP distributed analytics vendors have been acquired (Aster Data, Vertica, Netezza & Greenplum).  This had to happen and was expected to happen.  The MPP Analytics startup “revolution” is over and these technologies will now be integrated into the mainstream.

So what’s next?  As we now, if you are a massive multi-national software company it is a lot less risky to incrementally innovate and leave the development of “game changing” technologies to startups that can be acquired after they prove both the tech and the market.  So what follows MPP?

NoSQL technologies seem the only likely candidate at the moment, although I think it is a few years too early for any major acquisitions to occur.  A key issue that would need to be worked through is what exactly is being acquired as most NoSQL platforms are open source / free (most MPP platforms were proprietary).  But nonetheless, as the market grows and starts to eat away at some noticeable level from the existing RDBMS market the major vendors will want a piece of that action and the frenzy will start again.  But this is still quite a while away yet.

 

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“NoSQL”, for lack of better name, is a generic term that describes any data management system that does not use SQL as a query interface.  Generally this means any data management system that is non-relational, but the term also has also been stretched as far to include the boundaries of what constitutes a data management system at all (such as Hadoop).

Early on (a couple of years back in NoSQL time) when the term was coined I think the positioning was much more aggressive, but more recently this has been softened so now NoSQL is commonly quoted as meaning of “Not only SQL” or “next generation databases” (whatever that means).  The common message you get now is something along the lines of NoSQL systems are more “specialized”, each being designed to solve a smaller number of problems than the generic RDBMS sets out to.  NoSQL is another tool in your toolbox.  A better option in certain cases where the RDBMS doesn’t fit well.  A different hammer for a different type of nail.  All makes sense in theory, but in reality this brings its own set of troubles.

There are now dozens of NoSQL systems available for a developer to choose.  From MongoDB, Cassandra, Voldemort, Hbase, CouchDB, Riak, Neo4J, HamsterDB and so on.  And there are several different orientations of NoSQL system including document, key/value and graph.  It seems the same energy we saw open-source hackers 10 years ago putting into MySQL has now been transferred into a myriad of NoSQL systems.  Again the argument, more choice, better for everyone.

The problem, and I am putting it out there as a problem so we can think of ways to fix it, is that while that is fine in reality, in practice many choices also creates difficulties.  Real world development projects have certain skills bases they draw on, with experience and ability to “make things work” based on years of hard slog cobbling things together.  And there are very few surprises left when deploying an application on a mainstream RDBMS (of course they will, like any software, will still have issue from time to time).

One of the key reasons the RDBMS has been so dominate is the fact that you could use it pretty much for any requirement.  And using it for any requirement meant that your developers had lots of experience building applications and your DBAs had lots of experience running it.  But also you knew that you could almost always make any requirement work “good enough” by buying extra hardware and/or indexing the heck out of it etc.  Regardless of whether it was technically the best fit or not, when all things were considered the RDBMS was a stable constant given short project timeframes and limited development budgets.  It was exactly its generic nature, its ability to do most things good enough, that has led to the RDBMS to become the default option for any new development project (with the various flavors of MySQL, Oracle, DB2 ,SQL Server being less relevant).

As humans, we all have limited brain capacities and most of us can only be experts in a small number of things.  And our expertise typically come from our history, making mistakes learning what works and what doesn’t through the hard yards of experience.   So given a buffet choice of specialized NoSQL systems how on earth do we choose the most appropriate tool for the job, while at the same time dealing with the lack of expertise we will invariably have?  Also what will be the impact to development projects in choosing the wrong tool for the job?  The RDBMS is very very forgiving to poor design, poor implementation and the subsequent addition of unforeseen application requirements (you want to run OLAP now we have built you a busy OLTP database – sure but do it overnight).  Will a specialist NoSQL system have the same tolerance for our incompetence?

So now I return back to the point that is really the keystone of the NoSQL motivation, “there are requirements which a RDBMS doesn’t work at all well for”.  I agree with this, but I have yet to see any quantification of what this actually means.  Is it 5% or 10% of current development projects?  And should the question really be “what percentage of development projects is the RDBMS unusable for”?  Technical purity, and even reducing license costs, needs to be balanced against one of the largest costs, re-skilling development and production teams to understand this new data platform. 

There are some clear cases, the Googles, Twitters, Facebooks etc where scale alone is clearly outside the boundaries of what is possible on today’s RDBMS platforms.  But in terms of today’s development projects, what percentage would these scalability requirements quantify?  1%?  Less?  Sure, we are going through somewhat of a data explosion and by all counts the volume of data we collect and manage in our databases is growing at an alarming rate.  So the demand for scale will continue, but let’s also not forget that the big RDBMS vendors are very market driven, and as the market changes their products will also continue to change with it.  It is very unlikely they will be asleep at the wheel and lose their dominate share of the ~$30b market without a fight.

Contrary to how it may appear, I am actually supportive of a number of NoSQL initiatives and I am even hands on with a few.  But I do have concerns about how we quantify the market, how we ensure that people are making the right decisions in choosing a NoSQL platform.  And also how do bridge the gap with skill sets and experience for developers who will have years upon years of RDBMS experience but, by nature, only have exposure to NoSQL systems periodically based on certain application requirements. 

 

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