In-Memory Computing Summit 2: Database Innovation

In the late 1990s, my boss at the time at Aberdeen Group asked me a thought-provoking question:  Why was I continuing to cover databases?  After all, he pointed out, it seemed at first glance like a mature market – because the pace of technology innovation had slowed down and nothing important seemed to be on the horizon.  Moreover, consolidation meant that there were fewer and fewer suppliers to cover.  My answer at the time was that users in “mature” markets like the mainframe still needed advice on key technologies, which they would not find because analysts following my boss’s logic would flee the field. 
However, as it turned out, this was not the right reason at all – shortly thereafter, the database field saw a new round of innovations centering on columnar databases, data virtualization, and low-end Linux efforts that tinkered with the ACID properties of relational databases.  These, in turn, led to the master data management, data governance, global repository, and columnar/analytic database technologies of the late 2000s.  In the early 2010s, we saw the Hadoop-driven “NoSQL” loosening of commit constraints to enable rapid analytics on Big Data by sacrificing some data quality.
As a result, the late-1990s “no one ever got fired for buying Oracle” mature market wisdom is now almost gone from memory – and databases delivering analytics are close to the heart of many firms’ strategies.  And so, it seems that the real reason to cover databases today is that their markets, far from being mature, are rapidly spawning new entrants and offering many technology-driven strategic reasons to upgrade and expand.
The recent 2015 In-Memory Computing Summit suggests that a new round of database innovation, driven by the needs listed in my first post, is bringing changes to user environments especially in three key areas:

1.       Redefinition of data-store storage tiering, leading to redesign of data-access software;

2.       Redefinition of “write to storage” update completion, allowing flexible loosening of availability constraints in order to achieve real-time or near-real-time processing of operational and sensor-driven Big Data; and

3.       Balkanization of database architectures, as achieving Fast Data turns out to mean embedding a wide range of databases and database suppliers in both open source and proprietary forms.

Real-Time Flat-Memory Storage Tiering:  Intel Is a Database Company

All right, now that I’ve gotten your attention, I must admit that casting Intel as primarily a database company is a bit of an exaggeration.  But there’s no doubt in my mind that Intel is now thinking about its efforts in data-processing software as strategic to its future.  Here’s the scenario that Intel laid out at the summit:
At present, only 20% of a typical Intel CPU is being used, and the primary reason is that it sits around waiting for I/O to occur (i.e., needed data to be loaded into main memory) – or, to use a long-disused phrase, applications running on Intel-based systems are I/O-bound.  To fix this problem, Intel aims to ensure faster I/O, or, equivalently, the ability to service the I/O requests of each of multiple applications running concurrently, and do it faster.  Since disk does not offer much prospect for I/O-speed improvement, Intel has proposed a software protocol standard, NVRAM(e), for flash memory.  However, to ensure that this protocol does indeed speed things up adequately, Intel must write the necessary data-loading and data-processing software itself.
So will this be enough for Intel, so that it can go back to optimizing chip sets? 
Well, I predict that Intel will find that speeding up I/O from flash storage, which treats flash purely as storage, will not be enough to fully optimize I/O.  Rather, I think that the company will also need to treat flash as an extension of main memory:  Intel will need to virtualize (in the old sense of virtual memory) flash memory and treat main memory and flash as if they were on the same top storage tier, with the balancing act between faster-response main memory and slower-response flash taking place "beneath the software covers."  Or, to coin another phrase, Intel will need to provide virtual processors handling I/O as part of their DNA.  And from there, it is only a short step to handling the basics of data processing in the CPU -- as IBM is already doing via IBM DB2 BLU Acceleration.

Real-Time Flat-Memory Storage Tiering:  Redis Labs Discovers Variable Tiering

"Real time" is another of those computer science phrases that I hate to see debased by marketers and techie newbies.  In the not-so-good old days, it meant processing and responding to every single (sensor) input in a timely fashion (usually less than a second) no matter what.  That, in turn, meant always-up and performance-optimized software aimed specifically at delivering on that "no matter what" guarantee.  Today, the term seems to have morphed into one in which basic old-style real-time stream data processing (e.g., keep the car engine running) sits cheek-by-jowl with do-the-best-you-can stream processing involving more complex processing of huge multi-source sensor-data streams (e.g., check if there's a stuck truck around the corner you might possibly bash into).  The challenge in the second case (complex processing of huge data streams) is to optimize performance and then prioritize speed-to-accept-data based on the data's "importance".
I must admit to having favorites among vendors based on their technological approach and the likelihood that it will deliver new major benefits to customers, in the long run as well as the short. At this conference, Redis Labs was my clear favorite.  Here's my understanding of their approach:
The Redis architecture begins with a database cluster with several variants, allowing users to trade off failover/high availability with performance and maximize main memory and processors in a scale-out environment.  Then, however, the Redis Labs solution focuses on "operational" (online real-time update/modify-heavy sensor-driven transaction processing).  To do this of course, the Redis Labs database puts data in main memory where possible.  Where it is not (according to the presentation), the Redis Labs database  treats flash as if it were main memory, mimicking flat-memory access on flash interfaces.  As the presenter put it, at times of high numbers of updates, flash is main memory; at other times, it's storage.
The Redis Labs cited numerous benchmarks to show that the resulting database was the fastest kid on the block for "operational" data streams.  To me, that's a side effect of a very smart approach to performance optimization that crucially includes the ideas of using flash as if it were main memory and of varying the use of flash as storage, meaning that sometimes all of flash is the traditional tier-2 storage and sometimes all of flash is tier-1 processing territory.  And, of course, in situations where main memory and flash are all that is typically needed, for processing and storage, we might as well junk the tiering idea altogether:  it's all flat-memory data processing.

memSQL and Update Completion

Redis Labs' approach may or may not complete the optimizations needed for flash-memory operational data processing.  At the Summit, memSQL laid out the most compelling approach to the so-called "write-to-storage" issue that I heard. 
I first ran into write-to-storage when I was a developer attending meetings of the committee overseeing the next generation of Prime Computer's operating system.  As they described it, in those days, there was either main memory storage, which vanished whenever you turned off your PC or your system crashed, and there was everything else (disk and tape, mostly) that kept this information for a long time, whether the system crashed or not.  So database data or files or anything that was new or changed didn't really "exist" until it was written to disk or tape.  And that meant that further access to that data (modifications or reads) had to wait until the write-to-disk had finished.  Not a major problem in the typical case; but where performance needs for operational (or, in those days, online transaction processing/OLTP) data processing required it, write-to-storage was and is a major bottleneck.
memSQL, sensibly enough, takes the "master-slave" approach to addressing this shortcoming.  While the master software continues on its merry way with the updated data in main memory, the slave busies itself with writing the data to longer-term storage tiers (including flash used as storage).  Problem solved?  Not quite.
If the system crashes after a modification has come in but before the slave has finished writing (a less likely occurrence, but still possible), then both the first and second changes are lost.  However, in keeping with the have-it-your-way approach of Hadoop, memSQL allows the user to choose a tradeoff between performance speed and what it calls a "high availability" version.  And so, flash plus master-slave processing plus a choice of "availability" means that performance is increased in both operational and analytical processing of sensor-type data, the incidence of the write-to-storage problem is decreased, and the user can flexibly choose to accept some data loss to achieve the highest performance, or vice versa.

The Balkanization of Databases:  The Users Begin To Speak

Balkanization may be an odd phrase for some readers, so let me add a little pseudo-history here.  Starting a bit before 100 BC, the Roman Empire had the entire region from what is now Hungary to northern Greece (the "Balkans") under its thumb.  In the late 400s, a series of in-migrations led to a series of new occupiers of the region, and some splintering, but around 1450 the Ottoman Empire again conquered the Balkans.  Then, in the 1800s, nationalism arrived, Romantics dug up or created rationales for much smaller nations, and the whole Balkan area irretrievably broke up into small states.  Ever since, such a fragmentation has been referred to as the "Balkanization" of a region.
In the case of the new database scenarios, users appear to be carrying out similar carving out of smaller territories via a multitude of databases.  One presenter's real-world use case involved, on the analytics side, HP Vertica among others, and several Hadoop-based databases including MongoDB on the operational side.  I conclude that, within enterprises and in public clouds, there is a strong trend towards Balkanization of databases.
That is new.  Before, the practice was always for organizations to at least try to minimize the number of databases, and for major vendors to try to beat out or acquire each other.  Now, I see more of the opposite, because (as memSQL's speaker noted) it makes more sense if one is trying to handle sensor-driven data to go to the Hadoop-based folks for the operational side of these tasks, and to the traditional database vendors for the analytical side.  Given that the Hadoop-based side is rapidly evolving technologically and spawning new open-source vendors as a result, it is reasonable to expect users to add more database vendors than they consolidate, at least in the near term.  And in the database field, it's often very hard to get rid of a database once installed. 

SAP and Oracle Say Columnar Analytics Is "De Rigueur"

Both SAP and Oracle presented at the Summit.  Both had a remarkably similar vision of "in-memory computing" that involved primarily columnar relational databases and in-main-memory analytics.
In the case of SAP, that perhaps is not so surprising.  SAP HANA marketing has featured its in-main-memory and columnar-relational technologies for some time.  Oracle’s positioning is a bit more startling:  in the past, its acquisition of TimesTen and its development of columnar technologies had been treated in its marketing as a bit more of a check-list item -- yeah, we have them too, now about our incredibly feature-full, ultra-scalable traditional database ...
Perhaps the most likely answer why both Oracle and SAP were there and talking about columnar was that for flat-memory analytics, columnar's ability to compress data and hence fit it in the main-memory and/or flash tier more frequently trumps traditional row-oriented relational strengths where joins involving less than 3 compressible rows are concerned.  Certainly, the use case cited above where HP Vertica's columnar technology was called into service makes the same point.
And yet, the rise in columnar's importance in the new flat-memory systems also reinforces the Balkanization of databases, if subtly.  In Oracle's case, it changes the analytical product mix.  In SAP's case, it reinforces the value of a relatively new entrant into the database field.  In HP's case, it brings in a relatively new database from a relatively new vendor of databases that is likely to be new to the user or relatively disused before.  Even within the traditionally non-Balkanized turf of analytical-database vendors some effective Balkanization is beginning to happen, and one of its key driving forces is the usefulness of columnar databases in sensor-driven-data analytics.

A Final Note:  IBM and Cisco Are Missing At the Feast But Still Important

Both IBM's DB2 BLU Acceleration and Cisco Data Virtualization, imho, are important technologies in this Brave New World of flat-memory database innovation; but neither was a presenter at the Summit.  That may be because the Summit was a bit Silicon-Valley heavy but I don't know for sure.  I hope to give a full discussion at some point of the assets these products bring in the new database architectures, but not today.  Hopefully, the following brief set of thoughts will give an idea of why I think them important.
In the case of IBM, what is now DB2 BLU Acceleration anticipated and leapfrogged the Summit in several ways, I think.  Not only did BLU Acceleration optimize main-memory and to some extent flash memory analytics using the columnar approach; it also optimized the CPU itself.  Among several other valuable BLU Acceleration technologies is one that promises to further speed update processing and, hence, operational-plus-analytic columnar processing.  The only barrier -- and so far, it has proved surprisingly high -- is to get other vendors interested in these technologies, so that database "frameworks" which offer one set of databases for operational and another for analytical processing can incorporate "intermediate" choices between operational and analytic, or optimize operational processing yet further.
In the case of Cisco, its data virtualization capabilities offer a powerful approach to creating a framework for the new database architecture along the lines of a TP monitor -- and so much more.  The Cisco Data Virtualization product is pre-built to optimize analytics and update transactions across scale-out clusters, so is well acquainted with all but the very latest Hadoop-based databases, and has excellent user interfaces.  It can also serve as a front end to databases within a slot/"framework", or as a gateway to the entire database architecture.  As I once wrote, this is amazing "Swiss army knife" technology -- there's a tool for everything.  And for those in Europe, Denodo’s solutions are effective in this case as well.
I am sure that I am leaving out important innovations and potential technologies here.  That's how rich the Summit was to a database analyst, and how exciting it should be to users.
So why am I a database analyst, again?  I guess I would say, for moments like these.

In-Memory Computing Summit 1: The Database Game, The Computing Game, Is Changing Significantly

So why was I, a database guy, attending last Monday what turned out to be the first ever In-Memory Computing Summit – a subject that, if anything, would seem to relate more to storage tiering?  And why were heavy-hitter companies like Intel, SAP, and Oracle, not to mention TIBCO and Hitachi Data Systems, front and center at this conference?  Answer:  the surge in flash-memory capacity and price/performance compared to disk, plus the advent of the Internet of Things and the sensor-driven web, is driving a major change in the software we need, both in the area of analytics and operational processing.  As one presentation put it, software needs to move from supporting Big Data to enabling Fast (but still Big) Data.

In this series of blog posts, I aim to examine these major changes as laid out in the summit, as well as their implications for databases and computing IT.  In the first post, I’d like to sketch out an overall “vision”, and then in later posts explore the details of how the software to support this is beginning to arrive.

The Unit:  The “Flat-Memory” System

In an architecture that can record and process massive streams of "sensor" data (including data from mobile phones and from hardware generating information for the Internet of Things) there is a premium on "stream" processing of incoming data in real time, as well as on transactional writes in addition to the reads and queries of analytical processing.  The norm for systems handling this load, in the new architecture, is two main tiers:  main memory RAM and "flash" or non-volatile memory/NVRAM (approximately 3 orders of magnitude).  This may seem like hyperbole when we are talking about Big Data, but in point of fact one summit participant noted a real-world system using 1 TB of main memory and 1 PB of flash.

Fundamentally, flash memory is like main-memory RAM:  more or less all addresses in the same tier take an equal amount of time to read or change.  In that sense, both tiers of our unitary system are "flat memory", unlike disk, which has spent many years fine-tuning performance that can vary widely depending on the data's position on disk.  To ease the first introduction of flash, it provided interfaces to CPUs that mimic disk accesses and therefore make flash's data access both variable and slow (compared to a flat-memory interface).  Therefore, for the most part, NVRAM in our unitary system will remove this performance-clogging software and access flash in much the same way that main-memory RAM is accessed today.  In fact, as Intel testified at the summit, this process is already underway at the protocol level.

The one remaining variable in performance is the slower speed of flash memory.  Therefore, existing in-memory databases and the like will not optimize the new flat-memory systems out of the box.  The real challenge will be to identify the amount of flash that needs to be used by the CPU to maximize performance for any given task, and then use the rest for longer-term storage, in much the same way that disk is used now.  For the very largest databases, of course, disk will be a second storage tier.

The Architecture:  Bridging Operational Fast and Analytical Big

Perhaps memSQL was the presenter at the conference who put the problem most pithily:  in their experience, users have been moving from SQL to NoSQL, and now are moving from NoSQL towards SQL.  The reason is that for deeper analytical processing of data such as social-media whose value is primarily that it's Big (e.g, much social-media data) use of SQL and relational/columnar databases is better, while for Big Data whose value is primarily that it's fresh (and therefore needs to be processed Fast) SQL software causes unacceptable performance overhead.  Users will need both, and therefore will need an architecture that includes Hadoop and SQL/analytical data processing. 

One approach would treat each database on either side as a "framework", which would be applied to transactional, analytical, or in-between tasks depending on its fitness for these tasks.  That, to me, is a "bridge too far", introducing additional performance overhead, especially at the task assignment stage.  Rather, I envision something more akin to a TP monitor, streaming sensor data to a choice among transactional databases (at present, mostly associated with Hadoop), and analytical data to a choice among other analytical databases.  I view the focus of presenters such as Redis Labs on the transactional side and SAP and Oracle on the analytical side as an indication that my type of architecture is at least a strong possibility.

The Infrastructure:  If This Goes On …

One science fiction author once defined most science fiction as discussing one of three “questions”:  What if? If only …, and If this goes on …  The infrastructure today for the new units and architecture is clearly “the cloud” – public clouds, private clouds, hybrid clouds.  With the steady penetration of Hadoop into enterprises, all of these are now reasonably experienced in supporting both Hadoop and SQL data processing.  And yet, if this goes on …
The Internet of Things is not limited to stationary “things”.  On the contrary, many of the initial applications involve mobile smartphones and mobile cars and trucks.  A recent NPR examination of car technology noted that cars are beginning to communicate not only with the dealer/manufacturer and the driver but also with each other, so that, for example, they can warn of a fender-bender around the next curve.  These applications require Fast Data, real-time responses that use the cars’ own databases and flat memory for real-time sensor processing and analytics.  As time goes on, these applications should become more and more frequent, and more and more disconnected from today’s clouds.  If so, that would mean the advent of the mobile cloud as an alternative and perhaps dominant infrastructure for the new systems and architecture.

Perhaps this will never happen.  Perhaps someone has already thought of this.  If not, folks:  You heard it here first.

Lessons of Moore's-Law History 3: The Consumer is the Chasm

In the previous two blog posts, we have seen (hopefully!) the importance of betting on technologies and suppliers that most closely resemble the silicon-transistor-chip evolution process, and the need to bet on suppliers adhering to that process that emphasize infrastructure software combining openness to technology evolution with support for existing systems – but we have not dealt with the “chasms” that occur when Moore’s-Law evolution results in new, radically different higher-level technologies. 

Here I am combining two books popular back in the 1990s:  Crossing the Chasm, by Geoffrey Moore (no relation), which talked about how innovative startups bridged the gap between early techie adopters and the bulk of the market; and The Innovator’s Dilemma, by Clayton Christensen, which argued that “disruptive innovation” driven by new technologies would over time change existing markets profoundly, usually to the detriment of larger, well-entrenched companies presently dominant in an industry.  In an industry like the computer industry (including software and services), where Moore’s Law drives a more rapid pace of underlying improvement to platforms, entrenched companies must often react to disruptive innovation by “crossing the chasm” rapidly with their customers to the new technology, else they risk losing their markets and their ability to survive.

When A Company Is Ultimate-Customer-Blind

The 1990s and early 2000s gave us a good set of data on who succeeded and who failed in “crossing the chasm.”  The results were surprising to many.  It seemed reasonable that a Sun or an Oracle would surpass a Microsoft, that a Digital Equipment would beat a Novell, that an IBM would dominate a Compaq or a Dell in the PC market.  And yet, here we are 25 years later, and by no stretch of the imagination can we say that these things happened.

It seems to me that a common theme of this litany of successes and failures at adapting to new Moore’s-Law technologies is that companies that sold to the business market failed far more often than companies that sold to the consumer market.  Sun, which did a superb job jumping from the workstation to the server market, failed among other things to note consumer-driven Linux/Windows open-source software that was undercutting its prices and playing better with the server farms that later led to public clouds.  IBM’s initial great Charlie-Chaplin PC marketing fell before the next-day delivery and Intel allegiance of Dell and Compaq.  On a smaller scale, we saw Informix’s Oracle-protected VAR channel fail fatally when Informix failed to reach through that channel to detect that the servers it was shipping to VARs were no longer being bought at a comparable rate by the ultimate customers. 

On a longer time scale, the proprietary chip sets of IBM, HP, and Sun/Oracle have steadily lost ground compared to Intel markets.  It may seem logical that IBM over the last few years is walking away from the PC, starting with its consumer PCs/laptops; but that approach has steadily given it less and less sense of trends in the consumer market, and that, Moore’s-Law history would suggest, is dangerous.  It was once assumed that one’s employees would buy consumer versions of what a business-focused computer company offered; but, starting in the 1980s, the opposite has appeared true:  Word/Lotus, Excel, presentation software, laptops, smartphones, and social media have all been imported into businesses before and despite corporate standards.  If a computer company wishes to handle a disruptive innovation, Moore’s-Law history suggests, it must immediately sense the consumer movement, and then follow the consumer across the chasm, and quickly.

The Top Line is Now the Bottom Line

From 2008 on until fairly recently, a focus on cutting costs allowed companies like IBM, Oracle, and HP to look good to Wall Street although revenues were slightly down to slightly up.  Now, however, the contrast with consumer-focused companies like Google, Amazon, and Apple that continue to grow revenues by leaps and bounds is becoming all too obvious, and the chasm to cross to support the new mobile and cloud technologies is becoming wider by the day.  Generalizing to other industries, it is not enough to keep in sync with a Moore’s-Law-related process and focus on flexible, forward-compatible software.  One should also focus on growing top-line revenues by crossing the chasm with the customer. 

That means aligning platforms with consumer-successful companies like Intel.  That means aligning strategies with Google's, Amazon's, and Apple’s smartphone and cloud technology changes.  And that means far better coordination with and analytics about ultimate end-user customers – e.g., agile marketing.

Moore’s Law won’t last forever – it probably won’t last more than 5-10 years longer.  But the lessons of its history will last much longer than that.  Caveat seller.

We Have Blamed the Vendors and IT; Is It Now Time to Blame the Business Types?

Reflecting on the recent financial reports of the giants of computing infrastructure – companies like IBM, HP, Oracle, and Microsoft – I am struck by the continuation of a troubling trend:  The technology and its usefulness is moving ahead as fast as, if not faster than, ever before; IT has made some striking adjustments; but the revenues to vendors from this, and therefore IT spend, are flat if not falling. Why?

Let’s see.  In databases, Oracle and IBM are almost flat, despite (at least in IBM’s case) two technologies in two years that potentially deliver performance and price-performance gains well ahead of Moore’s Law.  I find plenty to criticize in Windows 8, but its implementation with regard to business needs, in Windows and Office usage, should deliver benefits comparable to that of the introduction of the mouse-based user interface – and Microsoft enterprise revenues are flat.  Cloud technology is not replacing, but rather complementing, in-house architectures, and therefore should require, initially, more rather than less total spending – and yet, increases in cloud spending are instead paired with overall flat or lower user computing spend.  No, I don’t buy the “public cloud saves money” rationale – the benefits come later rather than sooner, and involve mainly flexibility and to a much lesser extent multi-tenancy and grid savings, especially when many users are employing multiple cloud providers that must be coordinated.  And the IBM and Oracle/SPARC data suggest the biggest hardware decline is in Unix/Linux hardware, the darling of the cloud set, not in mainframes or PCs. 

In the past, a typical explanation for this has been the need for the business to cut back IT spend in response to decreasing profits.  Of course, that did not prevent IT and computing from becoming a larger and larger part of overall business spending during the 1980s and 1990s.  In the early 2000s, IT spend actually decreased proportionally to the cost-cutting of the rest of the business – and yet, despite an initially tepid overall business recovery, computing spending from about 2003 to 2008 rebounded and grew quite briskly. So this pattern of user computing spending is unprecedented in three ways:  its length, its unremitting focus on cost-cutting apparently comparable in size to the rest of the business, and its seeming inability to reflect major technology advances with a good claim to deliver major cost or other benefits to the rest of the business.

The Blame Game

In the past, I have found, when things go wrong in computing spending the first reaction is to blame the vendors.  In this case, the obvious critique is that they have failed to communicate the benefits of the technology, cost and otherwise.  Except that, as I can attest from my experience as an analyst, both the technology advances and the communications from the vendors are as good as, if not in many cases better than, those of most or all of the past.  Big Data, for example, is not pure hyperbole, and vendors have done a reasonable job by traditional standards of highlighting the ability of targeted Big-Data analytics to explain and bind the customer as never before, in a cost-effective way. 

OK, then the next line of analysis is to blame IT, typically for failing to align themselves with the business’ strategy, or (when times are tough) identify ways of lifting the dead hand of older systems and other seemingly outdated costs.  To this, I have one response:  agile IT.  As never before, IT is aligning itself with agile development, by supporting a software lifecycle that includes operational feedback integrated with development, and by encouraging automation of functions that provides a basis for rapid response and identification of architectural problems and opportunities at the administrator level.  As my studies have shown, these plus the increasingly agile nature of the software that IT makes available to the business translate into major cost-cutting and major benefits beyond what traditional IT approaches can deliver.  And as for aligning itself with business strategy, IT is well aware of Big Data as a hot topic, hence the sudden and somewhat odd demand for Hadoop experts.  No, whatever IT may have done in the past, it seems clear that it has upped its game.

OK, then, who does that leave?  And yet, how can we blame the business types?  Aren’t these the same folk who drove those increases in IT percentage of spending over the 1980s and 1990s?  Aren’t these the most receptive listeners when we talk about the power of embedded analytics to improve business processes and the importance of analyzing the “customer of one”?

There’s Something Not Going On Here …

To understand why there might be cause for concern and, yes, for blame in business strategy – driven by the CEO, the CFO, and their staff – let’s look in very broad-brush terms at what has been going on, not just for the last five years, but for the last three decades.  Here are a few of the highlights, as suggested by various studies:

1.       Advances in productivity have been accruing 90% to the CEO – ½ in his income as CEO, and ½ in his increases in wealth as an investor.  While this still represents a fairly small percentage of overall expense, it does unnecessarily increase the focus on cutting costs for all except the CEO.

2.       Hedge funds that sometimes act as turnaround artists or “shadow banks” have taken a good 90% or so of the funds’ investment profits for themselves, and thus receive compensation not just in the tens of millions of dollars but, at the top, in billions of dollars, despite the fact that they deliver less to the investor than an index fund in most if not all cases.  While it is difficult to see direct effects of this “fleecing of the rich” on corporate strategies, one significant effect is to increase the fleeced CEO’s focus on growing profits uber alles, to at least get some return from their investments – with the Googles of the world the few happy exceptions.

3.       The present long-lasting recession/stagnation has effectively removed perhaps 10% of the workforce from work over a long period of time.  This has the effect, since businesses often discriminate against the long-term underemployed, of creating a permanent gap between “potential” GNP and real GNP.  To put it another way, even if the economy grows at a reasonable pace, it will still be much smaller it should be.  That means smaller markets and, again, more relative emphasis on cost-cutting to achieve profit growth rather than growing revenues to grow profits.

There are three concerns about this excessive focus on cost-cutting.  The first, which probably is not serious right now but is far more troubling than five years ago, is that cost-cutting eventually runs out as a strategy if revenues continue to be flat (and, don’t forget, revenues are pretty flat for the bulk of businesses, hence the 1% growth in GNP over the first part of this year).  The second concern is that this cost-cutting, applied economy-wide (as it seems to be doing), reinforces and cements smaller markets by eliminating the part of the market funded by the now underemployed.  Ordinarily, recessions are too short for this to happen; but it seems to be happening now, as new jobs continue to make no dent in the workforce/working-age-population ratio.

The third concern, which I for one find the most troubling, is the possibility that reflexive cost-cutting becomes the answer to every situation, the reflex of the business.  If that is true, it would suggest that businesses are blowing opportunities for savvy increases in computing technology spend because it’s all about cost-cutting, not strategic investment. 

Blame or Not, What Might Business Strategists Do Better?

First, I would suggest that businesses set up a long-term plan and process for using analytics to better understand and improve the relationship with the customer.  This means acquiring data virtualization software and the like, to allow aggressive searching out and combination of all the new customer information constantly being created.  It also means buying the new infrastructure tools (database and information architecture) that can scale to handle aggregation of social-media data on the Web and in-house customer-experience data. 

Second, I would argue (not that I expect many businesses to listen) that in order to grow revenues as well as profits (and probably grow profits faster), business types need to acquire and foster use of agile tools and processes – such as agile marketing, with its emphasis on data-driven understanding of the customer and prospect rather than the anecdotal, top-down opinion that has been all too common in the past.  As I noted in a recent blog post, an agile business is a possibility, if the CEO and his lieutenants really commit to it – and it typically means not only the CEO using a Scrum-type planning process, but also constant modification and constant feedback up and down the organization about plans.  I should also note that an agile process specifically builds in slack for learning and review, and yet (according to my studies) it produces much better cost-saving and profit results than a “cram as much work as possible in, be as cost-efficient as possible” approach, for the CEO as well as the developer.

And then there is the really controversial stuff. Sustainability efforts are, by and large, really underperforming, as a recent article in www.thinkprogress.com confirms my impression that gains in the US in carbon emissions are effectively coming from offshoring the problem.  Businesses need to acquire effective global carbon-tracking software, and use it.  Businesses need to join to push shared quality regulations that will move all towards new computer and software technology that is more productive, rather than clinging to the infrastructure of the past that in the long run costs more, as in the electrical/energy grid vs. software-coordinated regional combined solar/wind – and rather than enabling the block-all-regulation political excesses of the U.S. Chamber of Commerce and its ilk.

I’m not saying that all this is doable, or that the case for questioning the approach of the overall business to computing technology is clear.  I am saying that it’s time we recognize the seriousness of the overall problem of which computing spend is a part, and we stop blaming the usual suspects.  Five years is enough.  Thirty years is enough.  In the long run, a cost-cutting strategy is not enough – and the long run is beginning to arrive.   

 

Hey, Was I Actually Right About HP and Oracle?

HP's outlook won't be all that bad only because analysts are expecting Armageddon. – Larry Dignan, CNET, Nov. 21, 2011

Oracle Corp. /quotes/zigman/76584/quotes/nls/orcl ORCL +3.54% will likely beat analysts' estimates when it reports its fourth-quarter on Thursday. But investors will probably take their cue from what's expected to be a strong outlook … Big companies are investing heavily in Oracle's new generation of database software, as well as hardware it introduced since acquiring Sun Microsystems last year. – Steven Jones, MarketWatch, June 21, 2011.

I will stick out my neck and predict that HP will not implode over the next 3 years, and it will not fall behind IBM in revenues either, barring a truly epochal acquisition by IBM. – Wayne Kernochan, Infostructure Associates, blog post, May 17, 2011

.Sun’s UltraSPARC architecture has already shown signs of falling behind, and it appears that Oracle is not committing the necessary resources or expertise to play catch-up with IBM and Intel. … Exadata  … unless it is acquired by a leading-edge top-three-in-technology processor technology vendor such as Intel or IBM, … will start falling behind in scalability … It seems likely that within the next 3 years, Oracle will either load additional pricing on its locked-in customers to support another acquired platform, or deliver less and less relative price/performance … It may make sense for Oracle’s customers to move to a vendor such as IBM even if it will be painful in the short term. If my outlook for Oracle’s pricing and technology over the next 3 years is correct, the cost inefficiencies compared to others won’t get better, they’ll get worse; and the degree of database lock-in will stay the same or worsen, too, as additional Oracle implementations occur. – Wayne Kernochan, Pund-IT Review, Sept. 2011

Oracle’s hardware business will continue to look troubled while the Sun portion continues to shrink – Erik Hessedahl, All Things D, June 18, 2012

Oracle hardware revenues decrease by 10% in constant currency over 9 months, total revenues up about 3% for fiscal 2012 and 1% for 4Q12 - Oracle earnings press releases Mar/Jun 2012

Desktop units were up 5%, revenue down 3% yty for 2Q12, revenue run rate for fiscal 2012 about $121B – HP earnings press release 5/23/12

Revenue flat for 1Q12 yty, revenue run rate for fiscal 2012 about $99B – IBM earnings press release, 4/17/12

Oracle, EMC, IBM, and Big Data: Avoiding The One-Legged Marathon

Note: this is a post of an article published in Nov. 2011 in another venue, and the vendors profiled here have all upgraded their Big Data stories significantly since then. Imho, it remains useful as a starting point for assessing their Big Data strategies, and deciding how to implement a long-term IT Big Data strategy oneself.

In recent weeks, Oracle, EMC, and IBM have issued announcements that begin to flesh out in solutions their vision of Big Data, its opportunities, and its best practices. Each of these vendor solutions has significant advantages for particular users, and all three are works in progress.

However, as presented so far, Oracle’s and EMC’s approaches appear to have significant limitations compared to IBM’s. If the pair continues to follow the same Big Data strategies, I believe that many of their customers will find themselves significantly hampered in dealing with certain types of Big Data analysis over the long run – an experience somewhat like voluntarily restricting yourself to one leg while running a marathon.

Let’s start by reviewing the promise and architectural details of Big Data, then take a look at each vendor’s strategy in turn.

Big Data, Big Challenges
As I noted in an earlier piece, some of Big Data is a relabeling of the incessant scaling of existing corporate queries and their extension to internal semi-structured (e.g., corporate documents) and unstructured (e.g., video, audio, graphics) data. The part that matters the most to today’s enterprise, however, is the typically unstructured data that is an integral part of customers’ social-media channel – including Facebook, instant messaging, Twitter, and blogging. This is global, enormous, very fast-growing, and increasingly integral (according to IBM’s recent CMO survey) to the vital corporate task of engaging with a "customer of one" throughout a long-term relationship.

However, technically, handling this kind of Big Data is very different from handling a traditional data warehouse. Access mechanisms such as Hadoop/MapReduce combine open-source software, large amounts of small or PC-type servers, and a loosening of consistency constraints on the distributed transactions (an approach called eventual consistency). The basic idea is to apply Big Data analytics to queries where it doesn’t matter if some users get "old" rather than the latest data, or if some users get an answer while others don’t. As a practical matter, this type of analytics is also prone to unexpected unavailability of data sources.

The enterprise cannot treat this data as just another BI data source. It differs fundamentally in that the enterprise can be far less sure that the data is current – or even available at all times. So, scheduled reporting or business-critical computing based on Big Data is much more difficult to pull off. On the other hand, this is data that would otherwise be unavailable for BI or analytics processes – and because of the approach to building solutions, should be exceptionally low-cost to access.

However, pointing the raw data at existing BI tools would be like pointing a fire hose at your mouth, with similarly painful results. Instead, the savvy IT organization will have plans in place to filter Big Data before it begins to access it.

Filtering is not the only difficulty. For many or most organizations, Big Data is of such size that simply moving it from its place in the cloud into an internal data store can take far longer than the mass downloads that traditionally lock up a data warehouse for hours. In many cases, it makes more sense to query on site and then pass the much smaller result set back to the end user. And as the world of cloud computing keeps evolving, the boundary between "query on site" and "download and query" keeps changing.

So how are Oracle, EMC, and IBM dealing with these challenges?

Oracle: We Control Your Vertical
Reading the press releases from Oracle OpenWorld about Oracle’s approach to Big Data reminds me a bit of the old TV show Outer Limits, which typically began with a paranoia-inducing voice intoning "We control your horizontal, we control your vertical …" as the screen began flickering.

Oracle’s announcements included specific mechanisms for mass downloads to Oracle Database data stores in Oracle appliances (Oracle Loader for Hadoop), so that Oracle Database could query the data side-by-side with existing enterprise data, complete with data-warehouse data-cleansing mechanisms.

The focus is on Oracle Exalytics BI Machine, which combines Oracle Database 11g and Oracle’s TimesTen in-memory database for additional BI scalability. In addition, there is a "NoSQL" database that claims to provide "bounded latency" (i.e., it limits the "eventual" in "eventual consistency"), although how it should combine with Oracle’s appliances was not clearly stated.

The obvious advantage of this approach is integration, which should deliver additional scalability on top of Oracle Database’s already-strong scalability. Whether that will be enough to make the huge leap to handling hundred-petabyte data stores that change frequently remains to be seen.

At the same time, these announcements implicitly suggest that Big Data should be downloaded to Oracle databases in the enterprise, or users should access Big Data via Oracle databases running in the cloud, but provide no apparent way to link cloud and enterprise data stores or BI. To put it another way, Oracle is presenting a vision of Big Data used by Oracle apps, accessed by Oracle databases using Oracle infrastructure software and running on Oracle hardware with no third party needed. We control your vertical, indeed.

What also concerns me about the company’s approach is that there is no obvious mecha-nism either for dealing with the lateness/unavailability/excessive lack of quality of Big Data, or for choosing the optimal mix of cloud and in-enterprise data location. There are only hints: the bounded latency of Oracle NoSQL Database, or the claim that Oracle Data Integrator with Application Adapter for Hadoop can combine Big Data with Oracle Database data – in Oracle Database format and in Oracle Database data stores. We control your horizontal, too. But how well are we controlling it? We’ll get back to you on that.

EMC: Competing with the Big Data Boys
The recent EMC Forum in Boston in many ways delivered what I regard as very good news for the company and its customers. In the case of Big Data, its acquisition of Greenplum with its BI capabilities led the way. And Greenplum finally appears to have provided EMC with the data management smarts it has always needed to be a credible global information management solutions vendor. In particular, Greenplum is apparently placing analytical intelligence in EMC hardware and software, giving EMC a great boost in areas such as being able to handle querying within the storage device and monitoring distributed systems (such as VCE’s VBlocks) for administrative purposes. These are clearly leading-edge, valuable features.

EMC’s Greenplum showed itself to be a savvy supporter of Big Data. It supports the usual third-party suspects for BI: SQL, MapReduce, and SAS among others. Querying is "software shared-nothing", running in virtual machines on commodity VBlocks and other scale-out/grid x86 hardware. Greenplum has focused on the fast-deploy necessities of the cloud, claiming a 25-minute data-model change – something that has certainly proved difficult in large-scale data warehouses in the past.

Like Oracle, Greenplum offers "mixed" columnar and row-based relational technology; un-like Oracle, it is tuned automatically, rather than leaving it up to the customer how to mix and match the two. However, its answer for combining Big Data and enterprise data is also tactically similar to Oracle’s: download into the Greenplum data store.

Of our three vendors, EMC via Greenplum has been the most concrete about what one can do with Big Data, offering specific support for combining social graphing, customer-of-one tracking of Twitter/Facebook posts, and mining of enterprise customer data. The actual demo had an unfortunate "1984" flavor, however, with a customer’s casual chat about fast cars being used to help justify doubling his car insurance rate.

The bottom line with Greenplum appears to be that its ability to scale is impressive, even with Big Data included, and it is highly likely to provide benefits out of the box to the savvy social-media analytics implementer. Still, it avoids, rather than solves, the problems of massive querying across Big Data and relational technology – it assumes massive downloads are possible and data is "low latency" and "clean", where in many cases it appears that this will not be so. EMC Greenplum is not as "one-vendor" a solution as Oracle’s, but it does not have the scalability and robustness track record of Oracle, either.

IBM: Avoiding Architectural Lock-In
At first glance, IBM appears to have a range of solutions for Big Data similar to Oracle and EMC – but more of them. Thus, it has the Netezza appliance; it has InfoSphere BigInsights for querying against Hadoop; it has the ability to download data into both its Informix/in-memory technology and DB2 databases for in-enterprise data warehousing; and it offers various Smart Analytics System solutions as central BI facilities.

Along with these, it provides Master Data Management (InfoSphere MDM), data-quality features (InfoSphere DataStage), InfoSphere Streams for querying against streaming Web sensor data (like mobile GPS), and the usual quick-deployment models and packaged hardware/software solutions on its scale-out and scale-up platforms. And, of course, everyone has heard of Watson – although, intriguingly, its use cases are not yet clearly pervasive in Big Data implementations.

To my mind, however, the most significant difference in IBM’s approach to Big Data is that it offers explicit support for a wide range of ways to combine Big-Data-in-place and enterprise data in queries. For example, IBM’s MDM solution allows multiple locations for customer data and supports synchronization and replication of linked data. Effectively used, this allows users to run alerts against Big Data in remote clouds or dynamically shift customer querying between private and public clouds to maximize performance. And, of course, the remote facility need not involve an IBM database, because of the MDM solution’s cross-vendor "data virtualization" capabilities.

Even IBM’s traditional data warehousing solutions are joining the fun. The IBM IOD conference introduced the idea of a "logical warehouse", which departs from the idea of a single system or cluster that contains the enterprise’s "one version of the truth", and towards the idea of a "truth veneer" that looks like a data warehouse from the point of view of the analytics engine but is actually multiple operational, data-warehouse, and cloud data stores. And, of course, IBM’s Smart Analytics Systems run on System x (x86), Power (RISC) and System z (mainframe) hardware.

On the other hand, there are no clear IBM guidelines for optimizing an architecture that combines traditional enterprise BI with Big Data. It gives one the strong impression that IBM is providing customers with a wide range of solutions, but little guidance as to how to use them. That IBM does not move the customer towards an architecture that may prevent effective handling of certain types of Big-Data queries is good news; that IBM does not yet convey clearly how these queries should be handled, not so much.

Composite Software and the Missing Big-Data Link
One complementary technology that users might consider is data virtualization (DV), as provided by vendors such as Composite Software and Denodo. In these solutions, subqueries on data of disparate types (such as Big Data and traditional) are optimized flexibly and dynamically, with due attention to "dirty" or unavailable data. DV solution vendors’ accrued wisdom can be simply summed up: usually, querying on-site instead of doing a mass download is better.

How to deal with the "temporal gap" between "eventually consistent" Big Data and "hot off the press" enterprise sales data is a matter for customer experimentation and fine-tuning, but that customer can always decide what to do with full assurance that subqueries have been optimized and data cleansed in the right way.

The Big Data Bottom Line
To me, the bottom line of all of this Big Data hype is that there is indeed immediate business value in there, and specifically in being able to go beyond the immediate customer interaction to understand the customer as a whole and over time – and thereby to establish truly win-win long-term customer relationships. Simply by looking at the social habits of key consumer "ultimate customers," as the Oracle, EMC, and IBM Big Data tools already allow you to do, enterprises of all sizes can fine-tune their interactions with the immediate customer (B2B or B2C) to be far more cost-effective.

However, with such powerful analytical insights, it is exceptionally easy to shoot oneself in the foot. Even skipping quickly over recent reports, I can see anecdotes of "data overload" that paralyze employees and projects, trigger-happy real-time inventory management that actually increases costs, unintentional breaches of privacy regulations, punitive use of newly public consumer behavior that damages the enterprise’s brand or perceived "character", and "information overload" on the part of the corporate strategist.

The common theme running through these user stories is a lack of vendor-supplied context that would allow the enterprise to understand how to use the masses of new Big Data properly, and especially an understanding of the limitations of the new data.

Thus, in the long run, the best performers will seek a Big-Data analytics architecture that is flexible, handles the limitations of Big Data as the enterprise needs them handled, and allows a highly scalable combination of Big Data and traditional data-warehouse data. So far, Oracle and EMC seem to be urging customers somewhat in the wrong direction, while IBM is providing a "have it your way" solution; but all of their solutions could benefit strongly from giving better optimization and analysis guidance to IT.

In the short run, users will do fine with a Big-Data architecture that does not provide an infrastructure support "leg" for a use case that they do not need to consider. In the long run, the lack of that "leg" may be a crippling handicap in the analytics marathon. IT buyers should carefully consider the long-run architectural plans of each vendor as they develop.

The Other BI: Oracle TimesTen and In-Memory-Database Streaming BI

This blog post highlights a software company and technology that I view as potentially useful to organizations investing in business intelligence (BI) and analytics in the next few years. Note that, in my opinion, this company and solution are not typically “top of the mind” when we talk about BI today.

The Importance of TimesTen-Type In-Memory Database Technology to BI

All right, now I’m really stretching the definition of “other”. Let’s face it, Oracle is “top of the mind” when we talk about BI, and they recently announced a TimesTen appliance, so TimesTen is not an invisible product, either. And finally, the hoopla about SAP HANA means that in-memory database technology itself is probably presently pretty close to the center of IT’s radar screen.

So why do I think Oracle’s TimesTen is in some sense not “top of the mind”? Answer: because there are potential applications of in-memory databases in BI for which the technology itself, much less any vendor’s in-memory database solution, is not a visible presence. In particular, I am talking about in-memory streaming databases.

To understand the relevance of in-memory databases to complex event processing and BI, let’s review the present use cases of in-memory databases. Originally, in-memory technology was just the thing for analyzing medium-scale amounts of financial-market information in real time, information such as constantly changing stock prices. Lately, in-memory databases have added two more BI duties: (a) serving as a “cache” database for enterprise databases, to speed up massive BI where smaller chunks of data could be localized, and (b) serving as a really-high-performance platform for mission-critical small-to-medium-scale BI applications that require less scaling year-to-year, such as some SMB reporting. These new tasks have arrived because rapid growth in main-memory storage has inevitably allowed in-memory databases to tackle a greater share of existing IT data-processing needs. To put it another way, when you have an application that is always going to require 100 GB of storage, sooner or later it makes sense to use an in-memory database and drop the old disk-based one, because in-memory database performance will typically be up to 10-100 times faster.

Now let’s consider event-processing or “streaming” databases. Their main constraint today in many cases is how much historical context they can access in real-time in order to deepen their analysis of incoming data before they have to make a routing or alerting decision. If that data can be accessed in main memory instead of disk, effectively up to 10-100 times the amount of “context” information can be brought to bear in the analysis in the same amount of time.

In other words, for streaming BI, IT potentially has two choices – a traditional event-processing database that is often entirely separate from a back-end disk-based database, or (2) a traditional main-memory database already pre-optimized for in-depth main-memory analysis and usually pre-integrated with a disk-based database (as TimesTen is with Oracle Database) as a “cache database” in cases where disk must be accessed. How to choose between the two? Well, if you don’t need much historical context for analysis, the event-processing database probably has the edge – but if you’re looking to upgrade your streaming BI, that’s not likely to be the case. In other cases, such as those where the processing is “routing-light” and “analysis-heavy”, an in-memory database not yet optimized for routing but far more optimized for in-depth analytics performance would seem to make more sense.

Thus, one way of looking at the use case of in-memory database event processing is to distinguish between in-enterprise and extra-enterprise data streams (more or less). Big Data is an example of an extra-enterprise stream, and can involve a fire hose of “sensor-driven Web” (GPS) and social media data that needs routing and alerting as much as it needs analytics. Business-critical-application-destined and embedded-analytics data streams are an example of in-enterprise data, even if admixed with a little extra-enterprise data; they require heavier-duty cross-analysis of smaller data streams. For these, the in-memory database’s deeper analysis before a split-second decision is made is probably worth its weight in gold, as it is in the traditional financial in-memory-database use case.

Won’t having two databases carrying out the general task of handling streaming data complicate the enterprise architecture? Not really. Past experience shows us that using multiple databases for finer-grained performance optimization actually decreases administrative costs, since the second database, at least, is typically much more “near-lights-out,” while switching between databases doesn’t affect users at all, because a database is infrastructure software that presents the same standard SQL-derivative interfaces no matter what the variant. And, of course, the boundary between event-processing database use cases and in-memory ones is flexible, allowing new ways of evolving performance optimization as user needs change.

The Relevance of Oracle TimesTen to Streaming BI

In many ways, TimesTen is the granddaddy of in-memory databases, a solution that I have been following for fifteen years. It therefore has leadership status in in-memory database use-case experience, and especially in the financial-industry stock-market-data applications that resemble my streaming-BI use case as described above. What Oracle has added since the acquisition is database-cache implementation and experience, especially integrated with Oracle Database. At the same time, TimesTen remains separable at need from other Oracle database products, as in the new TimesTen Appliance.

These characteristics make TimesTen a prime contender for the potential in-memory streaming BI market. Where SAP HANA is a work in progress, and approaches like Volt are perhaps less well integrated with enterprise databases, TimeTen and IBM’s solidDB stand out as combining both in-memory original design and database-cache experience – and of these two, TimesTen has the longer in-memory-database pedigree.

It may seem odd of me to say nice things about Oracle TimesTen, after recent events have raised questions in my mind about Oracle BI pricing, long-term hardware growth path, and possible over-reliance on appliances. However, inherently an in-memory database is much less expensive than an enterprise database. Thus, users appear to have full flexibility to use TimesTen separately from other Oracle solutions, free from worries about possible long-term effects of vendor lock-in.

Potential Uses of TimesTen-Type In-Memory Streaming BI for IT

As noted above, the obvious IT use cases for TimesTen-type streaming BI lie in driving deeper analysis in in-enterprise streaming applications. In particular, in the embedded-analytics area, in-memory performance speedups can allow consideration of a wider array of systems-management data in fine-tuning downtime-threat and performance-slowdown detection. In the real-time analytics area, an in-memory database might be of particular use in avoiding “over-steering”, as when predictable variations in inventories cause overstocking because of lack of historical context. In the Big Data area, an in-memory database might apply where the data has been pre-winnowed to certain customers, and a deeper analysis of those customers fine-tunes an ad campaign. For example, within a half-hour of the end of the game, Dick’s Sporting Goods had sent me an offer of a Patriots’ AFC Championship T-shirt, complete with visualization of the actual T-shirt – a reasonably well-targeted email. That’s something that’s far easier to do with an in-memory database.

IT should also consider the likely evolution of both event-processing and in-memory databases over the next few years, as their capabilities will likely become more similar. Here, the point is that event-processing databases often started out not with data-management tools, but with file-management ones – making them significantly less optimized “from the ground up” for analysis of data in main memory. Still, event-processing databases such as Progress Apama may retain their event-handling, routing, and alerting advantages, and thus the situation in which in-memory is better for in-enterprise and event-processing is better for extra-enterprise is likely to continue. In the meanwhile, increasing use of in-memory databases for the older use cases cited above means that in-memory streaming-BI databases offer an excellent way of gaining experience in their use, before they become ubiquitous. That, in turn, means that narrow initial “targets of opportunity” in one of the situations cited in the previous paragraph are a good idea, whatever the scope of one’s overall in-memory database commitment right now.

The Bottom Line for IT Buyers

In some ways, this is the least urgent and most speculative of the “other BI” solutions I have discussed so far. We are, after all, discussing additional performance and deeper analytics in a particular subset of IT’s needs, and in an area where the technology of in-memory databases and their event-processor alternatives is moving ahead rapidly. In a sense, this is really an opportunity for those IT shops that specialize in applying a little extra effort and “designing smarter” across multiple new technologies to provide a nice ongoing competitive advantage. For the rest, if the shoe can easily be made to fit, why not wear it?

My suggestion for most IT buyers, therefore, is therefore to have a “back-pocket” in-memory-database-for-streaming-BI short list that can be whipped out at the appropriate time. Imho, Oracle TimesTen right now should be on that list.

I hate to close without noting the overall long-term BI potential of in-memory databases. The future of in-memory databases is not, in my firm opinion, to supersede the IBM DB2s, Oracle Databases, and Microsoft SQL Servers of the world, at any time in the next four years. The hardware technologies to enable such a thing are not yet clear, much less competitive. Rather, the value of in-memory databases is to allow us to optimize our querying for both main-memory and disk storage – which are two very different things, and which will both apply appropriately to many key customer needs over the next few years. Overall, the effect will be another major ongoing jump in data-processing performance. As we enter this new database-technology era, those who initially kick the tires in a wider variety of BI projects will find themselves with a significant “experience” advantage over the rest, especially because the key to outstanding success will be determining the appropriate boundary between disk-based and in-memory database usage. Don’t force in-memory streaming BI into the organization. Do keep checking to see if it will fit your immediate needs. Sooner or later, it probably will.