Analytic Database Market Opportunities

In my first post in this series I gave an overview of ParStream and their product. In the second post I gave an overview of the Analytic Database Market from my perspective. In the third post I introduced a simple Analytic Database Market Segmentation.

In this post I will look at the gaps in this market and the new opportunities for ParStream and RainStor to introduce differentiated offerings. First, though I'll address the positioning of Hadapt.

Hadapt Positioning
Hadapt have recently come out of stealth and will be offering a very fast 'adaptive' version of Hadoop. Hadapt is a reworked and commercialized version of the Daniel Abadi's HadoopDB project. You can read Curt Monash's overview for more on that.  Basically Hadapt provides a Hadoop compatible interface (buzz phrase alert) and uses standard SQL databases (currently Postgres or VectorWise) underneath instead of HDFS. The unique part of their offering is keeping track of node performance and adapting queries to make the best use of each node. The devil is in the details of course, but a number of questions remain unanswered: How much  of the Hadoop API will be mapped to the database? Will there be a big inflection in performance between logic that maps to the DB and logic that runs in Hadoop? Etc. At a high level Hadapt seems like a very smart play for cloud based Hadoop users. Amazon EC2 instances have notoriously inconsistent I/O performance and a product that works around that should find fertile ground.

RainStor's Current Positioning
RainStor, if you don't know, is a special archival database that features massive compression. They current sell it as an OLDR solution (Online Data Retrieval) primarily aimed at company's that have large data volumes and stringent data retention requirements, e.g., anyone in Financial Services. They promise between 95% (20:1)  and 98% (40:1) compression rates for data whilst remaining fully query-able. Again Curt Monash has the best summary of their offering. I briefly met some RainStor guys a while back and I feel pretty confident that the product delivers what it promises. That said, I have never come across a RainStor client and I talk to lots of Teradata and Netezza types who would be their natural customers. So, though I have no direct knowledge of how they are doing, I suspect that it's been slow going to date and focusing on a different part of the market might be more productive.

Hyper Compressed Hadoop - RainStor Opportunity
I tweeted a while back that "RainStor needs a MapReduce story like yesterday". I still think that's right although now I think they need a Hadoop compatible story. To me, RainStor and Hadoop/MapReduce seem like a great fit. Hadoop users value the ability to process large data volumes over simple speed. Sure, they're happy with speed when they can get it but Hadoop is about processing as much data as possible. RainStor massively compresses databases while keeping them online and fully query-able. If RainStor could bring that compression to Hadoop it would be incredibly valuable. Imagine a Hadoop cluster that's maxed out at 200TB of raw data, compressed using splittable LZO to 50TB and replicated on 200TB of disk. If RainStor (replacing HDFS) could compress that same data at 20:1, half their headline rate, that cluster can now scale out to roughly 2,000TB. And many operations in Hadoop are constrained by disk I/O so if RainStor can operate to some extent on compressed data the cluster might just run faster. Even if it runs slightly slower the potential cost savings are huge (insert your own Amazon EC2 calculation here where you take EC2+S3 spend and divide by 20).

ParStream Opportunities
I see 2 key opportunities for ParStream in the current market. They can co-exist; but may require significant re-engineering of the product. First a bit of background; I'm looking for 'Blue Ocean Strategies' where ParStream can create a temporary monopoly. Selling into the 'MPP Upstart' segment is not considered due to the large number of current competitors. It's interesting to note though that that is where ParStream's current marketing is targeted.

Real-Time Analytic Hadoop - ParStream Opportunity 1
ParStream's first opportunity is to repurpose their technology into a Hadoop compatible offering. Specifically a 'real-time analytic Hadoop' product that uses GPU acceleration to vastly speed up Hadoop processing and opens up the MapReduce concept for many different and untapped use cases.  ParStream claim to have a unique index format and to mix workloads across CPUs and GPUs to minimise response times. It should be possible to use this technology to replace HDFS with their own data layer and indexing. They should also aim to greatly simplify data loading and cluster administration work. Finally transparent SQL access would be a very handy feature for business that want to provide BI directly from their 'analytic Hadoop' infrastructure. In summary: Hadoop's coding flexibility, processing speeds that approach CEP, and Data Warehouse style SQL access for downstream apps.

Target customers: Algorithmic trading companies (as always…), Large-scale online ad networks, M2M communications, IP-based Telcos, etc . Generally businesses with large volumes of data and high inbound data rates who need to make semi-complex decisions quickly and who have a relatively small staff.

Single User Data Warehouses - ParStream Opportunity 2
ParStream's second opportunity is to market ParStream as a single user, desk side data warehouse for analytic professionals, specifically targeting GPU powered workstations (like this one: ~$12k => 4 GPUs [960 cores], 2 Quad core CPUs, 48GB RAM, 3.6TB of fast disk). This version of ParStream must run on Windows (preferably Win7 x64, but Win Server at a minimum). Many  IT departments will balk at having a non-Windows workstation out in the office running on the standard LAN. However they are very used to analysts requesting 'special' powerful hardware. That's why the desk side element is so critical, this strategy is designed to penetrate restrictive centralised IT regimes.

In my experience a handful of users place 90% of the complex query demand on any given data warehouse. They're typically statisticians and operational researchers doing hard boiled analysis and what-if modelling. Many very large businesses have separate SAS environments that this group alone uses but that's a huge investment that many can't afford. Sophisticated analysts are a scarce and expensive resource and many companies can't fill the vacancies they have. A system that improves analyst productivity and ensures their time is well used will justify a significant premium. It also gives the business an excellent retention tool  to retain their most valuable 'quants'.

This opportunity avoids the challenges of selling a large scale GPU system into a business that has never purchased one before and avoids the red ocean approach of selling directly into the competitive MPP upstart segment. However it will be difficult to talk directly to these users inside the larger corporation and, when you convince them they need ParStream; you still have to work up the chain of command to get purchase authority (not the normal direction). On the plus side though these users form a fairly tight community and they will market it themselves if it makes their jobs easier.

Target customers: Biotech/Bioscience start-up companies, University researchers, marketing departments or consultancies. Generally, if a business is running their data warehouse on Oracle or SQL Server, their will be an analytic professional who would give anything to have a very fast database all to themselves.

In my next post I will look at why Hadoop is getting so much press, whether the hype is warranted and, generally, the future shape of the thing we currently call the data warehouse.

Analytic Database Market 'Fly Over'

This is a follow up to my previous post where I laid out my initial thoughts about ParStream. This is a very high level 'fly over' view of the analytic database market. I'll follow this up with some thoughts about how ParStream can position themselves in this market.

Powerhouse Vendors

The power players in the Analytic Database market are: Oracle (particularly Exadata), IBM (mostly Netezza, also DB2), and Teradata. Each of these vendors employs a large, very well funded and sophisticated sales force. A new vendor competing against them in accounts will find it very, very hard to win deals. They can easily put more people to work on a bid than a company like ParStream *employs*. If you are tendering for business in a Global 5000 corporation then you should expect to encounter them and you need a strategy for countering their access to the executive boards of these companies (which you will not get). In terms of technology their offerings have become very similar in recent years with all 3 emphasising MPP appliances of one kind or another, however most of the installed base are still using their traditional SMP offerings (Netezza and Teradata excepted).

New MPP niche players

There are a number of recent entrants to the market who also offer MPP technology, particularly: Greenplum, AsterData and ParAccel. All 3 offer software-only MPP databases, although Greenplum's emphasis has shifted slightly since being acquired by EMC. These vendors seem to focus mostly on (or succeed with) customers who have very large data volumes but are small companies in terms of employees. Many of these customers are in the web space. These vendors also have strong stories about supporting MapReduce/Hadoop inside their databases, which also plays to the leanings of web customers. According to testimonials on the vendor's websites customers seem to choose them because they are very fast and software only.

Microsoft

Microsoft is a unique case. They do not employ a direct sales force (as far as I know) however they have steadily become major force in enterprise software. Almost all companies run Windows desktops, have at least a few Windows servers and at least a few instances of SQL Server in production. Therefore Microsoft will be considered in virtually every selection process you're involved in. Microsoft have been steadily adding BI-DW features to the SQL Server product line and generally those features are all "free" with a SQL Server license. This doesn't necessarily make SQL Server cheaper but it does make it feel like very good value. Recent improvements include the Parallel Data Warehouse appliance (with HP hardware), columnar indexing for the next release and PowerPivot for local analysis of large data volumes.

Proprietary columnar

Columnar databases have been the hot technology in analytic databases for the last few years. The biggest vendors are Sybase with their very mature IQ product, SAND with an equally mature product and Vertica with their newer (and reportedly much faster) product. These databases can be used in single server (SMP / scale-up) and MPP (multi-server / scale-out) configurations. They appear to be most popular with customers who appreciate the high levels of compression that these databases offer and already have relatively mature star-schema / Kimball style data warehouses in place.  In my experience Sybase and SAND are used most in companies where they were introduced by an OEM as part of another product. Vertica is so new that it's not clear who their 'natural' customers are yet.

Open Source columnar

In the open source world there are 2 MySQL storage engines and a standalone product offering columnar databases. The MySQL engine Infobright was the first open source columnar database. It features very high compression and very fast loading however it is not suited for lots of joins and may be better thought of as a OLAP tool managed via SQL. The InfiniDB MySQL engine on the other hand is very good at joins and very good at squeezing all the available performance out of a server, however it does not have any compression currently. Finally there is LucidDB which is a Java based standalone product and has performance characteristics somewhere between the other two. LucidDB features excellent compression, index support and generally good performance but can be slow to load.


Vectorised columnar
There is only one player here: VectorWise. VectorWise is a columnar database (AFAIK) that has been architected from top to bottom to take advantage of the vector pipelines built into all recent CPUs. Vectorisation is a way of running many highly parallel operations through a single CPU. It basically removes all of the waiting and memory shifting that slows a CPU down. Initial testers have been very positive about the performance of VectorWise and had nothing but good things to say. There is also talk of an open source release so they are covering a lot of bases. They also have the advantage of being part of Ingres who may not be the force they once were but have a significant installed base and are well placed to sell VectorWise. They are the biggest direct competitor to ParStream that I can see right now.


Open Source MapReduce/NoSQL
ParStream will also compete with a new breed of open source MapReduce/NoSQL products, most notably Hadoop (and it's variants). These products are not databases per se but they have gained a lot of mindshare among developers who need to work with large data volumes. Part of their attraction is their 'cloud friendliness'. They are perfect for the cloud because they have been designed to run on many small servers and to expect that a single server could fail at any time. There is a trade-off to be made and MapReduce products tend to be much more complex to query, however for a technically savvy audience the trade is well worth it.

Next time I'll talk about where I think ParStream need to place themselves to maximise their opportunity.

UPDATE: Actually, in the next post I talk about how analytic database vendors are positioned and introduce a simple market segmentation. A further post about market opportunities will follow.

2011 Preview: BI-DW Top 5

Here are the trends I expect to see in 2011, but beware my crystal ball is hazy and known to be biased.


Top 5 for 2011


5) Niche BI acquisitions take off   
  Big BI consolidation may well be finished, but I think 2011 will be the start of niche vendor acquisitions as established BI vendors seek new growth in a (hopefully) recovering economy.  I don't expect any given deal size to be huge (probably sub $100m) however we could easily see half a dozen vendors being picked up.

  The driver for such acquisitions should be clear; Big BI vendors have ageing product stacks and many have been through post-merger product integration pains.  Their focus on innovation has been sorely lacking (non-existent?).  Also, there is huge leverage in applying a niche product to an existing portfolio.  The Business Objects / Xcelsius acquisition is a great example of this (although BO seems to think Xcelsius is a lot better and more useful than I do).

  I will not make any predictions about who might be acquired. However, here are some examples of companies with offerings that are not available from Big BI vendors.  Tableau's data visualisation offering is 1^st class IMHO and is a perfect fit for the people who actually use BI products in practice.  Lyza's BI/ETL collaboration offering is unique (and hard to describe) and a great fit for business oriented BI projects.  Jedox' Palo offering brings unique power to Excel power users and appears to be the only rival to Microsoft's PowerPivot offerings; I suspect a stronger US sales force would help them immensely.

4) GPU based computing comes to the fore
  I blogged some time ago about GPU's offering a glimpse of the many-core future.  Since then I've been waiting (and waiting) for signs that GPUs were making the jump into business servers.  Finally, in April 2010, Jedox released Palo OLAP Accelerator for GPUs. And this autumn I discovered ParStream's new GPU accelerated database (I blogged about it last week).  Finally in December we saw the announcement of a new class of Amazon EC2 instance featuring a GPU as part of the package.

  Based on these weak signals, I think 2011 will be the year that GPU processing and GPU acceleration starts to become a widely accepted part of business computing.  The most recent GPU cards from Nvidia and AMD offer many hundreds (512+) of processing cores and multiple cards can be used in a single server.  There is a large class of business computing problems that could be addressed by GPUs: analytic calculations (e.g. SAS / R), anything related to MapReduce / Hadoop, anything related to enterprise search / e-discovery, anything related to stream processing / CEP, etc.  As final note I would strongly suggest that vendors who sell columnar databases or in-memory BI products (or are losing sales to such) should point their R&D team at GPUs and get something together quickly. Niche vendors have an opportunity to push the price/perform baseline up by an order of magnitude and take market share while Big BI vendors try to catch up.

3) Data Warehousing morphs into Data Intensive Computing

  I once asked Netezza CTO Justin Lindsey if he considers Netezza machines to be supercomputers.  He said no he didn't but that the scientific computing 'guys' call it a "Data Intensive Supercomputer" and use it in applications where the ratio of data to calculations is very high, i.e., the opposite of classical supercomputing applications.  That phrase really stuck with me and it seems to describe the direction that data warehousing is headed.

  If you've been around BI-DW for a while you'll be familiar with the Inmon v Kimball ideology war. That fight illustrates the idea that data warehouses had a well defined purpose simply because we could argue about the right way to do 'it'.  I've noticed the purpose of the data warehouse stretching out over the last few years. The rise of analytics and ever increasing data volumes mean that more activities are finding a home on the data warehouse as a platform.  Either the activity cannot be done elsewhere or the data warehouse is the most accessible platform for data driven projects with short term data processing needs.

  In 2011 we need to borrow this term from the supercomputing guys and apply it to ourselves.  We need to change our thinking from delivering and supporting a data warehouse to offering a Data Intensive Computing service (that enables a data warehouse).  Those that fail to make the change should not be surprised when departments implement their own analytic database, make it available to the wider business and start competing with them for funding.

2) SharePoint destabilises incumbent BI platforms

  SharePoint is not typically considered a BI product and is rarely mentioned when I talk to fellow BI people. Those who specialise in Microsoft's products occasionally mention the special challenges (read headaches) associated with supporting it but it's "just a portal".  Right?  Not quite.  Microsoft has managed to drive a nuclear Trojan horse into the safety of incumbent BI installations.  SharePoint contains extensive BI capabilities and enables BI capabilities in other Microsoft products (like, um, Excel!).  Worst of all, if you're the incumbent BI vendor, SharePoint is everywhere!  It has something like 75% market share overall and effectively 100% market share in big companies.

  So what?  Well, when you want to deploy a dashboard solution where is the natural home for such content?  The intranet portal.  When you need to collaborate on analysis with widely dispersed teams, what can you use that's better than email?  Excel docs on the portal.  If report bursting is filling up your inboxes like sand in an hourglass, where can you put reports instead?  Maybe the intranet?  You get the point. We have a history in BI of pushing yet another friggin' portal onto the business when we select our BI platform.  Our chosen platform comes with such a nice portal, heck that's part of why we bought it. A year later we wonder why it doesn't get used.  We wonder why we spend more time unlocking expired logins than answering questions about reports.

   Right now businesses are only using a small fraction of SharePoint's capability. But they pay for all of them and I expect business to push for more return from SharePoint investments in 2011.  I expect a lot of these initiatives to involve communicating business performance (BI) and collaborating on performance analysis (BI again).  The trouble for incumbent vendors is clear: SharePoint has no substitute; your BI suite has direct substitutes, Microsoft offers some substitutes for free, your BI content is going to end up on SharePoint, once it's there its SharePoint content. BI vendors should expect hard conversation about maintenance fees and upgrade cycles in any account where dashboards are being hosted on SharePoint.

  As a final note, I would suggest that vendors who sell to large customers need to have a compelling SharePoint story.  It's basically a case of "if you can't beat them, join them".  If you have a portal as part of your suite you need to integrate with SharePoint (yesterday).  You need to make you products work better with SharePoint than Microsoft's own products do.  This will be a huge, expensive PITA - do it anyway.  You must find a way to embrace SharePoint without letting it own you.  Good luck. 

1) BI starts to dissolve into other systems

  My final trend for 2011 is about BI becoming bifurcated (love that word) between the strategic stuff (dashboards and analysis) and everything else. That "everything else" doesn't naturally live on a portal or in a report that gets emailed out.  It belongs in the system that generates the data in the first place; it belongs right at the point of interaction. James Taylor and Neil Raden talked about this idea in the book "Smart Enough Systems". I won't repeat their arguments here but I will outline some of the reason why I think it's happening now.

   First, 'greenfield' BI sites are a thing of the past. Everyone now has BI, it may not work very well but they have it.  New companies use BI from day 1.  The market is effectively saturated.  Second, most of the Big BI vendors are now part of large companies that sell line of business systems.  There is a natural concern about diluting the value of the BI suite, however "BI for the masses" is a dead-end and I think they probably get that.  Third, deep integration is one of the last remaining levers that Big BI vendors can use against nimble niche vendors and against SharePoint.  They will essentially have to go down this route at some point.  Finally, many system vendors have reached an impasse with their customers regarding upgrades. Customers are simply refusing to upgrade systems that work perfectly well. These vendors must create a real, tangible reason for the customers to move. I suspect that deep BI integration is their best bet.

  I have had too many conversations about 'completing the circle' and feeding the results of analysis back into source systems.  Sadly it never happens in practice, the walls are just too high.  Once the data has left the source system it is considered tainted and pushing tainted data into production systems is never taken lightly.  Thus the ultimate answer seems to be to push the "smarts" that have been generated by analysis down into the source system instead.  Expect to see plenty of marketing talk in 2011 about systems getting 'smarter' and more integrated.

Initial thoughts about ParStream

So here are my thoughts about ParStream based on researching their product on the internet only. I have not used the product, so I am simply assuming it lives up to all claims. As an analytics user and a BI-DW practitioner I sincerely hope that ParStream succeeds.

I'm a GPU believer
I'm a long time believer in the importance of utilising GPU for challenging database problems. I wrote a post in July 2009 about using GPUs for databases and implored database vendors to move in that direction: "Why GPUs matter for DW/BI" (http://joeharris76.blogspot.com/2009/07/why-gpus-matter-for-dwbi.html).  Here's the key quote - "There's a new world coming. It has a lot of cores. It will require new approaches. That world is accessible today through GPUs. Database vendors who move in this direction now will gain market share and momentum. Those who think they can wait on Intel and 'traditional' CPUs to 'catch up' may live to regret it."

On the right track
I think ParStream is *fundamentally* on the right track with a GPU accelerated analytic database. The ParStream presentation from Mike Hummel (http://www.youtube.com/watch?v=knicXkXd9hQ) talks about a query that took 12 minutes on Oracle taking just a few *miliseconds* on ParStream. If that is even half right the potential to shake up the industry and radically raise the bar on database performance is very exciting.

Reminiscent of Netezza
I remember the first time I used Netezza back in 2004. I had just taken a new role and my new company had recently installed a first generation Netezza appliance. In my previous job we had an Oracle data warehouse that was updated *weekly* and contained roughly 100 million rows. Queries commonly took *hours* to return. The Netezza machine held just less than 1 *billion* rows. I ran the following query: "SELECT month,  COUNT(*), SUM(call_value) FROM cdr GROUP BY month;". It came back in 15 seconds! I was literally blown away.

A fast database changes the game
When you have a very fast analytic databases it totally changes the game. You can ask more questions, ask more complex questions and ask them more often. Analytics requires a lot of trial and error and removing time spent waiting on the database enables a new spectrum of possibilities. For example, Netezza enabled me to reprice _every_ call in our database against _every_ one of our competitors tariffs (i.e. an 'explosive' operation: 50 mil records in => 800 mil records out) and then calculate the best *possible* price for each customer on any tariff. I used that information to benchmark my company on "value for money" and to understand the hidden drivers for customer churn.

ParStream appliance strategy:
So, given that background, let's look at the positioning of ParStream, the potential problems they may face, and the opportunities they need to pursue.

ParStream is not Netezza
I've positively compared ParStream to Netezza above so you might expect me to applaud ParStream for offering an appliance. Sadly not; Netezza's appliance success was due to unique factors that ParStream cannot replicate. Netezza had to use custom hardware because they use a custom FPGA chip. Customers were (and are) nervous about investing heavily in such hardware, however Netezza goes to great lengths to reassure them; providing service guarantees, plenty of spare parts and using commodity components wherever possible (power supplies, disks, host server, etc.). Also we must remember that most customers looking at Netezza were using very large servers (or server clusters) and required *very many* disks to get reasonable I/O performance for their databases. Netezza was actually reducing complexity for those customers.

The world has changed going into 2011
ParStream cannot replicate those market conditions. The world has changed considerably going into 2011 and different factors need to be emphasised. ParStream relies on Nvidia GPUs that are widely available and installed on commodity interconnects (e.g. PCIe). Moreover there are high quality server offerings available in 2 form factors that make the appliance strategy more of a liability than an asset. First, Nvidia (and others) sell 1U rack mounted 'server' that contain 4 GPUs and connect to 'host' server via a PCIe card. Second Supermicro (and others) sell 4U 'super' servers that contain 2 Intel Xeons and  4 GPUs in a pre-integrated package. The ParStream appliance may well be superior to these offerings in some key way however such advantages will be quickly wiped by out as the server manufactures continuously refresh their product line.

Focus on the database software business
ParStream should focus on the database software business where they have a huge advantage not the server business where they have huge disadvantages. You should read this article if you have any further doubts: "The Power of Commodity Hardware" (http://www.svadventure.com/svadventure/2009/01/the-power-of-commodity-hardware.html). Key quotes: "Customers love commodity hardware.", "Competing with HP, IBM, and Dell is dumb.", "Commodity hardware is much more capital efficient".  Also consider the fates of Kickfire and Dataupia who floundered on a database appliance strategy, and ParAccel who is going strong after initially offering an appliance and quickly moving to emphasise software-only.

Position GPUs as a new commodity
ParStream must position GPUs and GPU acceleration as a new commodity. Explain that GPUs are an essential part of all serious supercomputers and the technology is being embraced by everyone; Intel with Larabee, AMD with Fusion, etc. Emphasise the option to add 'commodity' 4 GPU pizza boxes servers alongside a customer's existing Xeon/Opteron servers and, using ParStream, make huge performance gains. Talk to Dell customers about using a single Dell PowerEdge C410x GPU chasis (http://www.dell.com/us/en/enterprise/servers/poweredge-c410x/pd.aspx) to accelerate an entire rack of "standard" servers running ParStream. The message must be clear: ParStream runs on commodity hardware; you may not have purchased GPU hardware before but you can get exactly what ParStream needs from your preferred vendor.

One final point here; ParStream needs to make Windows support a priority. This is probably not going to be fun, technically speaking, but Windows support will be important for the markets that ParStream should target (which will have to be another post, sadly).

UPDATE - I followed this post up with:
An overview of the analytic database market, a simple segmentation of the main analytic database vendors, and a summary of the key opportunities I see in the analytic databases market (esp. for ParStream and RainStor)