Curt Monash

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Choices in data management and analysis
Updated: 17 hours 11 min ago

Are analytic RDBMS and data warehouse appliances obsolete?

Sun, 2016-08-28 20:28

I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:

Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.

My answer, in a nutshell, is:

Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.

To see why, let’s start by asking: “With what do you want to integrate your analytic SQL processing?”

  • If you want to integrate with relational OLTP (OnLine Transaction Processing), your OLTP RDBMS vendor surely has a story worth listening to. Memory-centric offerings MemSQL and SAP HANA are also pitched that way.
  • If you want to integrate with your SAP apps in particular, HANA is the obvious choice.
  • If you want to integrate with other work you do in the Amazon cloud, Redshift is worth a look.

Beyond those cases, a big issue is integration with … well, with data integration. Analytic RDBMS got a lot of their workloads from ELT or ETLT, which stand for Extract/(Transform)/Load/Transform. I.e., you’d load data into an efficient analytic RDBMS and then do your transformations, vs. the “traditional” (for about 10-15 years of tradition) approach of doing your transformations in your ETL (Extract/Transform/Load) engine. But in bigger installations, Hadoop often snatches away that part of the workload, even if the rest of the processing remains on a dedicated analytic RDBMS platform such as Teradata’s.

And suppose you want to integrate with more advanced analytics — e.g. statistics, other predictive modeling/machine learning, or graph analytics? Well — and this both surprised and disappointed me — analytic platforms in the RDBMS sense didn’t work out very well. Early Hadoop had its own problems too. But Spark is doing just fine, and seems poised to win.

My technical observations around these trends include:

  • Advanced analytics commonly require flexible, iterative processing.
  • Spark is much better at such processing than earlier Hadoop …
  • … which in turn is better than anything that’s been built into an analytic RDBMS.
  • Open source/open standards and the associated skill sets come into play too. Highly vendor-proprietary DBMS-tied analytic stacks don’t have enough advantages over open ones.
  • Notwithstanding the foregoing, RDBMS-based platforms can still win if a big part of the task lies in fancy SQL.

And finally, if a task is “partly relational”, then Hadoop or Spark often fit both parts.

  • They don’t force you into using SQL or everything, nor into putting all your data into relational schemas, and that flexibility can be a huge relief.
  • Even so, almost everybody who uses those uses some SQL, at least for initial data extraction. Those systems are also plenty good enough at SQL for joining data to reference tables, and all that other SQL stuff you’d never want to give up.

But suppose you just want to do business intelligence, which is still almost always done over relational data structures? Analytic RDBMS offer the trade-offs:

  • They generally still provide the best performance or performance/concurrency combination, for the cost, although YMMV (Your Mileage May Vary).
  • One has to load the data in and immediately structure it relationally, which can be an annoying contrast to Hadoop alternatives (data base administration can be just-in-time) or to OLTP integration (less or no re-loading).
  • Other integrations, as noted above, can also be weak.

Suppose all that is a good match for your situation. Then you should surely continue using an analytic RDBMS, if you already have one, and perhaps even acquire one if you don’t. But for many other use cases, analytic RDBMS are no longer the best way to go.

Finally, how does the cloud affect all this? Mainly, it brings one more analytic RDBMS competitor into the mix, namely Amazon Redshift. Redshift is a simple system for doing analytic SQL over data that was in or headed to the Amazon cloud anyway. It seems to be quite successful.

Bottom line: Analytic RDBMS are no longer in their youthful prime, but they are healthy contributors in middle age. Mainly, they’re still best-of-breed for supporting demanding BI.

Notes on vendor lock-in

Tue, 2016-07-19 20:35

Vendor lock-in is an important subject. Everybody knows that. But few of us realize just how complicated the subject is, nor how riddled it is with paradoxes. Truth be told, I wasn’t fully aware either. But when I set out to write this post, I found that it just kept growing longer.

1. The most basic form of lock-in is:

  • You do application development for a target set of platform technologies.
  • Your applications can’t run without those platforms underneath.
  • Hence, you’re locked into those platforms.

2. Enterprise vendor standardization is closely associated with lock-in. The basic idea is that you have a mandate or strong bias toward having different apps run over the same platforms, because:

  • That simplifies your environment, requiring less integration and interoperability.
  • That simplifies your staffing; the same skill sets apply to multiple needs and projects.
  • That simplifies your vendor support relationships; there’s “one throat to choke”.
  • That simplifies your price negotiation.

3. That last point is double-edged; you have more power over suppliers to whom you give more business, but they also have more power over you. The upshot is often an ELA (Enterprise License Agreement), which commonly works:

  • For a fixed period of time, the enterprise may use as much of a given product set as they want, with costs fixed in advance.
  • A few years later, the price is negotiated, based on current levels of usage.

Thus, doing an additional project using ELAed products may appear low-cost.

  • Incremental license and maintenance fees may be zero in the short-term.
  • Incremental personnel costs may be controlled because the needed skills are already in-house.

Often those appearances are substantially correct. That’s a big reason why incumbent software is difficult to supplant unless the upstart substitute is superior in fundamental and important ways.

4. Subscriptions are closely associated with lock-in.

  • Most obviously, the traditional software industry gets its profits from high-margin support/maintenance services.
  • Cloud lock-in has rapidly become a big deal.
  • The open source vendors meeting lock-in resistance, noted above, have subscription business models.

Much of why customers care about lock-in is the subscription costs it’s likely to commit them to.

5. Also related to lock-in are thick single-vendor technology stacks. If you run Oracle applications, you’re going to run the Oracle DBMS too. And if you run that, you’re likely to run other Oracle software, and perhaps use Exadata hardware as well. The cloud ==> lock-in truism is an example of this point as well.

6. There’s a lot of truth to the generality that central IT cares about overall technology architecture, while line-of-business departments just want to get the job done. This causes departments to both:

  • Oppose standardization.
  • Like thick technology stacks.

Thus, departmental influence on IT both encourages and discourages lock-in.

7. IBM is all about lock-in. IBM’s support for Linux, Eclipse and so on don’t really contradict that. IBM’s business models is to squeeze serve its still-large number of strongly loyal customers as well as it can.

8. Microsoft’s business model over the decades has also greatly depended on lock-in.

  • Indeed, it exploited Windows/Office lock-in so vigorously as to incur substantial anti-trust difficulties.
  • Server-side Windows tends to be involved in thick stacks — DBMS, middleware, business intelligence, SharePoint and more. Many customers (smaller enterprises or in some cases departments) are firmly locked into these stacks.
  • Microsoft is making a strong cloud push with Azure, which inherently involves lock-in.

Yet sometimes, Microsoft is more free and open.

  • Office for Macintosh allowed the Mac to be a viable Windows competitor. (And Microsoft was well-paid for that, generating comparable revenue for per Mac to what it got for each Windows PC.)
  • Visual Studio is useful for writing apps to run against multiple DBMS.
  • Just recently, Microsoft SQL Server was ported to Linux.

9. SAP applications run over several different DBMS, including its own cheap MaxDB. That counteracts potential DBMS lock-in. But some of its newer apps are HANA-specific. That, of course, has the opposite effect.

10. And with that as background, we can finally get what led me to finally write this post. Multiple clients have complaints that may be paraphrased as:

  • Customers are locked into expensive traditional DBMS such as Oracle.
  • Yet they’re so afraid of lock-in now that they don’t want to pay for our vendor-supplied versions of open source database technologies; they prefer to roll their own.
  • Further confusing matters, they also are happy to use cloud technologies, including the associated database technologies (e.g. . Redshift or other Amazon offerings), creating whole new stacks of lock-in.

So open source vendors of NoSQL data managers and similar technologies felt like they were the only kind of vendor suffering from fear of lock-in.

I agree with them that enterprises who feel this way are getting it wrong. Indeed:

This is the value proposition that propelled Cloudera. It’s also a strong reason to give money to whichever MongoDB, DataStax, Neo Technology et al. sponsors open source technology that you use.

General disclosure: My fingerprints have been on this industry strategy since before the term “NoSQL” was coined. It’s been an aspect of many different consulting relationships.

Some enterprises push back, logically or emotionally as the case may be, by observing that the best internet companies — e.g., Facebook — are allergic to paying for software, even open source. My refutations of that argument include:

  • Facebook has more and better engineers than you do.
  • Facebook has a lot more servers than you do, and would presumably face much higher prices than you would if you each chose to forgo the in-house alternative.
  • Facebook pays for open source software in a different way than through subscription fees — it invents and enhances it. Multiple important projects have originated at Facebook, and it contributes to many others. Are you in a position to do the same thing?

And finally — most of Facebook’s users get its service for free. (Advertisers are the ones who pay cash; all others just pay in attention to the ads.) So if getting its software for free actually does screw up its SLAs (Service Level Agreements) — well, free generally comes with poorer SLAs than paid. But if you’re in the business of serving paying customers, then you might want to have paying-customer kinds of SLAs, even on the parts of your technology — e.g. websites urging people to do business with you — that you provide for free yourself.

Related links

Oracle as the new IBM — has a long decline started?

Thu, 2015-12-31 03:15

When I find myself making the same observation fairly frequently, that’s a good impetus to write a post based on it. And so this post is based on the thought that there are many analogies between:

  • Oracle and the Oracle DBMS.
  • IBM and the IBM mainframe.

And when you look at things that way, Oracle seems to be swimming against the tide.

Drilling down, there are basically three things that can seriously threaten Oracle’s market position:

  • Growth in apps of the sort for which Oracle’s RDBMS is not well-suited. Much of “Big Data” fits that description.
  • Outright, widespread replacement of Oracle’s application suites. This is the least of Oracle’s concerns at the moment, but could of course be a disaster in the long term.
  • Transition to “the cloud”. This trend amplifies the other two.

Oracle’s decline, if any, will be slow — but I think it has begun.

 

Oracle/IBM analogies

There’s a clear market lead in the core product category. IBM was dominant in mainframe computing. While not as dominant, Oracle is definitely a strong leader in high-end OTLP/mixed-use (OnLine Transaction Processing) RDBMS.

That market lead is even greater than it looks, because some of the strongest competitors deserve asterisks. Many of IBM’s mainframe competitors were “national champions” — Fujitsu and Hitachi in Japan, Bull in France and so on. Those were probably stronger competitors to IBM than the classic BUNCH companies (Burroughs, Univac, NCR, Control Data, Honeywell).

Similarly, Oracle’s strongest direct competitors are IBM DB2 and Microsoft SQL Server, each of which is sold primarily to customers loyal to the respective vendors’ full stacks. SAP is now trying to play a similar game.

The core product is stable, secure, richly featured, and generally very mature. Duh.

The core product is complicated to administer — which provides great job security for administrators. IBM had JCL (Job Control Language). Oracle has a whole lot of manual work overseeing indexes. In each case, there are many further examples of the point. Edit: A Twitter discussion suggests the specific issue with indexes has been long fixed.

Niche products can actually be more reliable than the big, super-complicated leader. Tandem Nonstop computers were super-reliable. Simple, “embeddable” RDBMS — e.g. Progress or SQL Anywhere — in many cases just work. Still, if you want one system to run most of your workload 24×7, it’s natural to choose the category leader.

The category leader has a great “whole product” story. Here I’m using “whole product” in the sense popularized by Geoffrey Moore, to encompass ancillary products, professional services, training, and so on, from the vendor and third parties alike. There was a time when most serious packaged apps ran exclusively on IBM mainframes. Oracle doesn’t have quite the same dominance, but there are plenty of packaged apps for which it is the natural choice of engine.

Notwithstanding all the foregoing, there’s strong vulnerability to alternative product categories. IBM mainframes eventually were surpassed by UNIX boxes, which had grown up from the minicomputer and even workstation categories. Similarly, the Oracle DBMS has trouble against analytic RDBMS specialists, NoSQL, text search engines and more.

 

IBM’s fate, and Oracle’s

Given that background, what does it teach us about possible futures for Oracle? The golden age of the IBM mainframe lasted 25 or 30 years — 1965-1990 is a good way to think about it, although there’s a little wiggle room at both ends of the interval. Since then it’s been a fairly stagnant cash-cow business, in which a large minority or perhaps even small majority of IBM’s customers have remained intensely loyal, while others have aligned with other vendors.

Oracle’s DBMS business seems pretty stagnant now too. There’s no new on-premises challenger to Oracle now as strong as UNIX boxes were to IBM mainframes 20-25 years ago, but as noted above, traditional competitors are stronger in Oracle’s case than they were in IBM’s. Further, the transition to the cloud is a huge deal, currently in its early stages, and there’s no particular reason to think Oracle will hold any more share there than IBM did in the transition to UNIX.

Within its loyal customer base, IBM has been successful at selling a broad variety of new products (typically software) and services, often via acquired firms. Oracle, of course, has also extended its product lines immensely from RDBMS, to encompass “engineered systems” hardware, app server, apps, business intelligence and more. On the whole, this aspect of Oracle’s strategy is working well.

That said, in most respects Oracle is weaker at account control than peak IBM.

  • Oracle’s core competitors, IBM and Microsoft, are stronger than IBM’s were.
  • DB2 and SQL Server are much closer to Oracle compatibility than most mainframes were to IBM. (Amdahl is an obvious exception.) This is especially true as of the past 10-15 years, when it has become increasingly clear that reliance on stored procedures is a questionable programming practice. Edit: But please see the discussion below challenging this claim.
  • Oracle (the company) is widely hated, in a way that IBM generally wasn’t.
  • Oracle doesn’t dominate a data center the way hardware monopolist IBM did in a hardware-first era.

Above all, Oracle doesn’t have the “Trust us; we’ll make sure your IT works” story that IBM did. Appliances, aka “engineered systems”, are a step in that direction, but those are only — or at least mainly — to run Oracle software, which generally isn’t everything a customer has.

 

But think of the apps!

Oracle does have one area in which it has more account control power than IBM ever did — applications. If you run Oracle apps, you probably should be running the Oracle RDBMS and perhaps an Exadata rack as well. And perhaps you’ll use Oracle BI too, at least in use cases where you don’t prefer something that emphasizes a more modern UI.

As a practical matter, most enterprise app rip-and-replace happens in a few scenarios:

  • Merger/acquisition. An enterprise that winds up with different apps for the same functions may consolidate and throw the loser out. I’m sure Oracle loses a few customers this way to SAP every year, and vice-versa.
  • Drastic obsolescence. This can take a few forms, mainly:
    • Been there, done that.
    • Enterprise outgrows the capabilities of the current app suite. Oracle’s not going to lose much business that way.
    • Major platform shift. Going forward, that means SaaS/”cloud” (Software as a Service).

And so the main “opportunity” for Oracle to lose application market share is in the transition to the cloud.

 

Putting this all together …

A typical large-enterprise Oracle customer has 1000s of apps running on Oracle. The majority would be easy to port to some other system, but the exceptions to that rule are numerous enough to matter — a lot. Thus, Oracle has a secure place at that customer until such time as its applications are mainly swept away and replaced with something new.

But what about new apps? In many cases, they’ll arise in areas where Oracle’s position isn’t strong.

  • New third-party apps are likely to come from SaaS vendors. Oracle can reasonably claim to be a major SaaS vendor itself, and salesforce.com has a complex relationship with the Oracle RDBMS. But on the whole, SaaS vendors aren’t enthusiastic Oracle adopters.
  • New internet-oriented apps are likely to focus on customer/prospect interactions (here I’m drawing the (trans)action/interaction distinction) or even more purely machine-generated data (“Internet of Things”). The Oracle RDBMS has few advantages in those realms.
  • Further, new apps — especially those that focus on data external to the company — will in many cases be designed for the cloud. This is not a realm of traditional Oracle strength.

And that is why I think the answer to this post’s title question is probably “Yes”.

 

Related links

A significant fraction of my posts, in this blog and Software Memories alike, are probably at least somewhat relevant to this sweeping discussion. Particularly germane is my 2012 overview of Oracle’s evolution. Other posts to call out are my recent piece on transitioning to the cloud, and my series on enterprise application history.

Readings in Database Systems

Thu, 2015-12-10 06:26

Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:

  • They’re both titanic figures in the database industry.
  • They both gave me testimonials on the home page of my business website.
  • They both have been known to use the present tense when the future tense would be more accurate. :)

I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.

But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**

*When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.

**Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.

Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as: 

  • Logical hierarchical models can be OK in certain cases. In particular, JSON could be a somewhat useful datatype in an RDBMS.
  • Physical hierarchical models are horrible.
  • Rather, you should implement the logical hierarchical model over a columnar RDBMS.

My responses start:

  • Nested data structures are more important than Mike’s discussion seems to suggest.
  • Native XML and JSON stores are apt to have an index on every field. If you squint, that index looks a lot like a column store.
  • Even NoSQL stores should and I think in most cases will have some kind of SQL-like DML (Data Manipulation Language). In particular, there should be some ability to do joins, because total denormalization is not always a good choice.

In no particular order, here are some other thoughts about or inspired by the survey articles in Readings in Database Systems, 5th Edition.

  • I agree that OLTP (OnLine Transaction Processing) is transitioning to main memory.
  • I agree with the emphasis on “data in motion”.
  • While I needle him for overstating the speed of the transition, Mike is right that columnar architectures are winning for analytics. (Or you could say they’ve won, if you recognize that mop-up from the victory will still take 1 or 2 decades.)
  • The guys seem to really hate MapReduce, which is an old story for Mike, but a bit of a reversal for Joe.
  • MapReduce is many things, but it’s not a data model, and it’s also not something that Hadoop 1.0 was an alternative to. Saying each of those things was sloppy writing.
  • The guys characterize consistency/transaction isolation as a rather ghastly mess. That part was an eye-opener.
  • Mike is a big fan of arrays. I suspect he’s right in general, although I also suspect he’s overrating SciDB. I also think he’s somewhat overrating the market penetration of cube stores, aka MOLAP.
  • The point about Hadoop (in particular) and modern technologies in general showing the way to modularization of DBMS is an excellent one.
  • Joe and Mike disagreed about analytics; Joe’s approach rang truer for me. My own opinion is:
  • The challenge of whether anybody wants to do machine learning (or other advanced analytics) over a DBMS is sidestepped in part by the previously mentioned point about the modularization of a DBMS. Hadoop, for example, can be both an OK analytic DBMS (although not fully competitive with mature, dedicated products) and of course also an advanced analytics framework.
  • Similarly, except in the short-term I’m not worried about the limitations of Spark’s persistence mechanisms. Almost every commercial distribution of Spark I can think of is part of a package that also contains a more mature data store.
  • Versatile DBMS and analytic frameworks suffer strategic contention for memory, with different parts of the system wanting to use it in different ways. Raising that as a concern about the integration of analytic DBMS with advanced analytic frameworks is valid.
  • I used to overrate the importance of abstract datatypes, in large part due to Mike’s influence. I got over it. He should too. :) They’re useful, to the point of being a checklist item, but not a game-changer. A big part of the problem is what I mentioned in the previous point — different parts of a versatile DBMS would prefer to do different things with memory.
  • I used to overrate the importance of user-defined functions in an analytic RDBMS. Mike had nothing to do with my error. :) I got over it. He should too. They’re useful, to the point of being a checklist item, but not a game-changer. Looser coupling between analytics and data management seems more flexible.
  • Excellent points are made about the difficulties of “First we build the perfect schema” data warehouse projects and, similarly, MDM (Master Data Management).
  • There’s an interesting discussion that helps explain why optimizer progress is so slow (both for the industry in general and for each individual product).

Related links

  • I did a deep dive into MarkLogic’s indexing strategy in 2008, which informed my comment about XML/JSON stores above.
  • Again with MarkLogic as the focus, in 2010 I was skeptical about document stores not offering joins. MarkLogic has since capitulated.
  • I’m not current on SciDB, but I did write a bit about it in 2010.
  • I’m surprised that I can’t find a post to point to about modularization of DBMS. I’ll leave this here as a placeholder until I can.
  • Edit: As promised, I’ve now posted about the object-relational/abstract datatype boom of the 1990s.

Transitioning to the cloud(s)

Mon, 2015-12-07 11:48

There’s a lot of talk these days about transitioning to the cloud, by IT customers and vendors alike. Of course, I have thoughts on the subject, some of which are below.

1. The economies of scale of not running your own data centers are real. That’s the kind of non-core activity almost all enterprises should outsource. Of course, those considerations taken alone argue equally for true cloud, co-location or SaaS (Software as a Service).

2. When the (Amazon) cloud was newer, I used to hear that certain kinds of workloads didn’t map well to the architecture Amazon had chosen. In particular, shared-nothing analytic query processing was necessarily inefficient. But I’m not hearing nearly as much about that any more.

3. Notwithstanding the foregoing, not everybody loves Amazon pricing.

4. Infrastructure vendors such as Oracle would like to also offer their infrastructure to you in the cloud. As per the above, that could work. However:

  • Is all your computing on Oracle’s infrastructure? Probably not.
  • Do you want to move the Oracle part and the non-Oracle part to different clouds? Ideally, no.
  • Do you like the idea of being even more locked in to Oracle than you are now? [Insert BDSM joke here.]
  • Will Oracle do so much better of a job hosting its own infrastructure that you use its cloud anyway? Well, that’s an interesting question.

Actually, if we replace “Oracle” by “Microsoft”, the whole idea sounds better. While Microsoft doesn’t have a proprietary server hardware story like Oracle’s, many folks are content in the Microsoft walled garden. IBM has fiercely loyal customers as well, and so may a couple of Japanese computer manufacturers.

5. Even when running stuff in the cloud is otherwise a bad idea, there’s still:

  • Test and dev(elopment) — usually phrased that way, although the opposite order makes more sense.
  • Short-term projects — the most obvious examples are in investigative analytics.
  • Disaster recovery.

So in many software categories, almost every vendor should have a cloud option of some kind.

6. Reasons for your data to wind up in a plurality of remote data centers include:

  • High availability, and similarly disaster recovery. Duh.
  • Second-source/avoidance of lock-in.
  • Geo-compliance.
  • Particular SaaS offerings being hosted in different places.
  • Use of both true cloud and co-location for different parts of your business.

7. “Mostly compatible” is by no means the same as “compatible”, and confusing the two leads to tears. Even so, “mostly compatible” has stood the IT industry in good stead multiple times. My favorite examples are:

  • SQL
  • UNIX (before LINUX).
  • IBM-compatible PCs (or, as Ben Rosen used to joke, Compaq-compatible).
  • Many cases in which vendors upgrade their own products.

I raise this point for two reasons:

  • I think Amazon/OpenStack could be another important example.
  • A vendor offering both cloud and on-premises versions of their offering, with minor incompatibilities between the two, isn’t automatically crazy.

8. SaaS vendors, in many cases, will need to deploy in many different clouds. Reasons include:

That said, there are of course significant differences between, for example:

  • Deploying to Amazon in multiple regions around the world.
  • Deploying to Amazon plus a variety of OpenStack-based cloud providers around the world, e.g. some “national champions” (perhaps subsidiaries of the main telecommunications firms).*
  • Deploying to Amazon, to other OpenStack-based cloud providers, and also to an OpenStack-based system that resides on customer premises (or in their co-location facility).

9. The previous point, and the last bullet of the one before that, are why I wrote in a post about enterprise app history:

There’s a huge difference between designing applications to run on one particular technology stack, vs. needing them to be portable across several. As a general rule, offering an application across several different brands of almost-compatible technology — e.g. market-leading RDBMS or (before the Linux era) proprietary UNIX boxes — commonly works out well. The application vendor just has to confine itself to relying on the intersection of the various brands’ feature sets.*

*The usual term for that is the spectacularly incorrect phrase “lowest common denominator”.

Offering the “same” apps over fundamentally different platform technologies is much harder, and I struggle to think of any cases of great success.

10. Decisions on where to process and store data are of course strongly influenced by where and how the data originates. In broadest terms:

  • Traditional business transaction data at large enterprises is typically managed by on-premises legacy systems. So legacy issues arise in full force.
  • Internet interaction data — e.g. web site clicks — typically originates in systems that are hosted remotely. (Few enterprises run their websites on premises.) It is tempting to manage and analyze that data where it originates. That said:
    • You often want to enhance that data with what you know from your business records …
    • … which is information that you may or may not be willing to send off-premises.
  • “Phone-home” IoT (Internet of Things) data, from devices at — for example — many customer locations, often makes sense to receive in the cloud. Once it’s there, why not process and analyze it there as well?
  • Machine-generated data that originates on your premises may never need to leave them. Even if their origins are as geographically distributed as customer devices are, there’s a good chance that you won’t need other cloud features (e.g. elastic scalability) as much as in customer-device use cases.

Related link

Issues in enterprise application software

Wed, 2015-11-11 07:39

1. I think the next decade or so will see much more change in enterprise applications than the last one. Why? Because the unresolved issues are piling up, and something has to give. I intend this post to be a starting point for a lot of interesting discussions ahead.

2. The more technical issues I’m thinking of include:

  • How will app vendors handle analytics?
  • How will app vendors handle machine-generated data?
  • How will app vendors handle dynamic schemas?
  • How far will app vendors get with social features?
  • What kind of underlying technology stacks will app vendors drag along?

We also always have the usual set of enterprise app business issues, including:

  • Will the current leaders — SAP, Oracle and whoever else you want to include — continue to dominate the large-enterprise application market?
  • Will the leaders in the large-enterprise market succeed in selling to smaller markets?
  • Which new categories of application will be important?
  • Which kinds of vendors and distribution channels will succeed in serving small enterprises?

And perhaps the biggest issue of all, intertwined with most of the others, is:

  • How will the move to SaaS (Software as a Service) play out?

3. I’m not ready to answer those questions yet, but at least I’ve been laying some groundwork.

Along with this post, I’m putting up a three post series on the history of enterprise apps. Takeaways include but are not limited to:

  • Application software is a very diverse area. Different generalities apply to different parts of it.
  • A considerable fraction of application software has always been sold with the technology stack being under vendor control. Examples include most app software sold to small and medium enterprises, and much of the application software that Oracle sells.
  • Apps that are essentially distributed have often relied on different stacks than single-site apps. (Duh.)

4. Reasons I see for the enterprise apps area having been a bit dull in recent years include:

5. But I did do some work in the area even so. :) Besides posts linked above, other things I wrote relevant to the present discussion include:

 

Couchbase 4.0 and related subjects

Thu, 2015-10-15 10:17

I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.

Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.

  • 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
  • Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
  • A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
  • By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
  • Couchbase 4.0 has been in beta for a few months.

Technical notes on Couchbase 4.0 — and related riffs :) — start:

  • There’s a new SQL-like language called N1QL (pronounced like “nickel”). I’m hearing a lot about SQL-on-NoSQL these days. More on that below.
  • “Index”, “data” and “query” are three different services/tiers.
    • You can run them all on the same nodes or separately. Couchbase doesn’t have enough experience yet with the technology to know which choice will wind up as a best practice.
    • I’m hearing a lot about heterogeneous-node/multi-tier DBMS architectures these days, and would no longer stand by my 2009 statement that they are unusual. Other examples include Oracle Exadata, MySQL, MongoDB (now that it has pluggable storage engines), MarkLogic, and of course the whole worlds of Hadoop and Spark.
  • To be clear — the secondary indexes are global, and not tied to the same nodes as the data they index.
  • There’s a new back end called ForestDB, but if I understood correctly, it’s used just for the indexes, not for the underlying data.
  • ForestDB represents Couchbase indexes in something that resembles b-trees, but also relies on tries. Indeed, if I’m reading the relevant poster correctly, it’s based on a trie of b-trees.
  • In another increasingly common trend, Couchbase uses Bloom filters to help decide which partitions to retrieve for any particular query.

Up to a point, SQL-on-NoSQL stories can be fairly straightforward.

  • You define some kind of a table,* perhaps in a SQL-like DDL (Data Description Language).
  • SELECT, FROM and WHERE clauses work in the usual way.
  • Hopefully, if a column is going to have a lot of WHERE clauses on it, it also has an index.

For example, I think that’s the idea behind most ODBC/JDBC drivers for NoSQL systems. I think it’s also the idea behind most “SQL-like” languages that NoSQL vendors ship.

*Nobody I talk to about this ever wants to call it a “view”, but it sure sounds like a view to me — not a materialized view, of course, but a view nonetheless.

JOIN syntax can actually be straightforward as well under these assumptions. As for JOIN execution, Couchbase pulls all the data into the relevant tier, and nested loop execution there. My new clients at SequoiaDB have a similar strategy, by the way, although in their case there’s a hash join option as well.

But if things stopped there, they would miss an important complication: NoSQL has nested data. I.e., a value can actually be an array, whose entries are arrays themselves, and so on. That said, the “turtles all the way down” joke doesn’t quite apply, because at some point there are actual scalar or string values, and those are the ones SQL wants to actually operate on.

Most approaches I know of to that problem boil down to identifying particular fields as table columns, with or without aliases/renaming; I think that’s the old Hadapt/Vertica strategy, for example. Couchbase claims to be doing something a little different however, with a SQL-extending operator called UNNEST. Truth be told, I’m finding the N1QL language reference a bit terse, and haven’t figured out what the practical differences vs. the usual approach are, if any. But it sounds like there may be some interesting ideas in there somewhere.

Notes on analytic technology, May 13, 2015

Wed, 2015-05-13 21:38

1. There are multiple ways in which analytics is inherently modular. For example:

  • Business intelligence tools can reasonably be viewed as application development tools. But the “applications” may be developed one report at a time.
  • The point of a predictive modeling exercise may be to develop a single scoring function that is then integrated into a pre-existing operational application.
  • Conversely, a recommendation-driven website may be developed a few pages — and hence also a few recommendations — at a time.

Also, analytics is inherently iterative.

  • Everything I just called “modular” can reasonably be called “iterative” as well.
  • So can any work process of the nature “OK, we got an insight. Let’s pursue it and get more accuracy.”

If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.

2. In 2011, I wrote, in the context of agile predictive analytics, that

… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.

I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises. 

3. Speaking of Gartner, Mark Beyer tweeted

In data management’s future “hybrid” becomes a useless term. Data management is mutable, location agnostic and services oriented.

I replied

And that’s why I launched DBMS2 a decade ago, for “DataBase Management System SERVICES”. :)

A post earlier this year offers a strong clue as to why Mark’s tweet was at least directionally correct: The best structures for writing data are the worst for query, and vice-versa.

4. The foregoing notwithstanding, I continue to believe that there’s a large place in the world for “full-stack” analytics. Of course, some stacks are fuller than others, with SaaS (Software as a Service) offerings probably being the only true complete-stack products.

5. Speaking of full-stack vendors, some of the thoughts in this post were sparked by a recent conversation with Platfora. Platfora, of course, is full-stack except for the Hadoop underneath. They’ve taken to saying “data lake” instead of Hadoop, because they believe:

  • It’s a more benefits-oriented than geek-oriented term.
  • It seems to be more popular than the roughly equivalent terms “data hub” or “data reservoir”.

6. Platfora is coy about metrics, but does boast of high growth, and had >100 employees earlier this year. However, they are refreshingly precise about competition, saying they primarily see four competitors — Tableau, SAS Visual Analytics, Datameer (“sometimes”), and Oracle Data Discovery (who they view as flatteringly imitative of them).

Platfora seems to have a classic BI “land-and-expand” kind of model, with initial installations commonly being a few servers and a few terabytes. Applications cited were the usual suspects — customer analytics, clickstream, and compliance/governance. But they do have some big customer/big database stories as well, including:

  • 100s of terabytes or more (but with a “lens” typically being 5 TB or less).
  • 4-5 customers who pressed them to break a previous cap of 2 billion discrete values.

7. Another full-stack vendor, ScalingData, has been renamed to Rocana, for “root cause analysis”. I’m hearing broader support for their ideas about BI/predictive modeling integration. For example, Platfora has something similar on its roadmap.

Related links

  • I did a kind of analytics overview last month, which had a whole lot of links in it. This post is meant to be additive to that one.

Thoughts and notes, Thanksgiving weekend 2014

Sun, 2014-11-30 19:48

I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:

1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:

  • Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
  • Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,

The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.

What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.

2. Three years ago I posted about agile (predictive) analytics. One of the points was:

… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.

Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.

3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: 

Also, the flashiest application I know of for only-moderately-successful KXEN came when one or more large retailers decided to run separate models for each of thousands of stores.

4. MongoDB, the product, has been refactored to support pluggable storage engines. In connection with that, MongoDB does/will ship with two storage engines – the traditional one and a new one from WiredTiger (but not TokuMX). Both will be equally supported by MongoDB, the company, although there surely are some tiers of support that will get bounced back to WiredTiger.

WiredTiger has the same techie principals as SleepyKat – get the wordplay?! – which was Mike Olson’s company before Cloudera. When asked, Mike spoke of those techies in remarkably glowing terms.

I wouldn’t be shocked if WiredTiger wound up playing the role for MongoDB that InnoDB played for MySQL. What I mean is that there were a lot of use cases for which the MySQL/MyISAM combination was insufficiently serious, but InnoDB turned MySQL into a respectable DBMS.

5. Hadoop’s traditional data distribution story goes something like:

  • Data lives on every non-special Hadoop node that does processing.
  • This gives the advantage of parallel data scans.
  • Sometimes data locality works well; sometimes it doesn’t.
  • Of course, if the output of every MapReduce step is persisted to disk, as is the case with Hadoop MapReduce 1, you might create some of your own data locality …
  • … but Hadoop is getting away from that kind of strict, I/O-intensive processing model.

However, Cloudera has noticed that some large enterprises really, really like to have storage separate from processing. Hence its recent partnership to work with EMC Isilon. Other storage partnerships, as well as a better fit with S3/object storage kinds of environments, are sure to follow, but I have no details to offer at this time.

6. Cloudera’s count of Spark users in its customer base is currently around 60. That includes everything from playing around to full production.

7. Things still seem to be going well at MemSQL, but I didn’t press for any details that I would be free to report.

8. Speaking of MemSQL, one would think that at some point something newer would replace Oracle et al. in the general-purpose RDBMS world, much as Unix and Linux grew to overshadow the powerful, secure, reliable, cumbersome IBM mainframe operating systems. On the other hand:

  • IBM blew away its mainframe competitors and had pretty close to a monopoly. But Oracle has some close and somewhat newer competitors in DB2 and Microsoft SQL Server. Therefore …
  • … upstarts have three behemoths to outdo, not just one.
  • MySQL, PostgreSQL and to some extent Sybase are still around as well.

Also, perhaps no replacement will be needed. If we subdivide the database management world into multiple categories including:

  • General-purpose RDBMS.
  • Analytic RDBMS.
  • NoSQL.
  • Non-relational analytic data stores (perhaps Hadoop-based).

it’s not obvious that the general-purpose RDBMS category on its own requires any new entrants to ever supplant the current leaders.

All that said – if any of the current new entrants do pull off the feat, SAP HANA is probably the best (longshot) guess to do so, and MemSQL the second-best.

9. If you’re a PostgreSQL user with performance or scalability concerns, you might want to check what Citus Data is doing.

An idealized log management and analysis system — from whom?

Sun, 2014-09-07 07:38

I’ve talked with many companies recently that believe they are:

  • Focused on building a great data management and analytic stack for log management …
  • … unlike all the other companies that might be saying the same thing :)
  • … and certainly unlike expensive, poorly-scalable Splunk …
  • … and also unlike less-focused vendors of analytic RDBMS (which are also expensive) and/or Hadoop distributions.

At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.

Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.

A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with: 

  • Agents for any kind of machine that admits streams of data.
  • Parsers that:
    • Immediately identify explicit name-value pairs in popular formats such as JSON or XML.
    • Also immediately extract a significant fraction of all implicit fields in text strings — timestamps for sure, but also a lot else. (Splunk is the current gold standard for such capabilities.)
    • Allow you to easily write rules for more such extractions.
  • Immediate indexing in line with everything the parsers do.
  • Easy import of log files, relational tables, and other relevant data structures.
  • Queries that can exploit all the indexes, at least up to the functionality level of SQL 2003 analytics (including windowing) and StreamSQL, of course with …
  • … blazing scalable performance.
  • Strong workload management and concurrent performance support. (Teradata is the gold standard for such capabilities in the analytic sphere.)
  • Various other mature-DBMS features, e.g. in backup, manageability, and uptime.

Further, there would be numerous styles of business intelligence interface, at least including:

  • Generic BI like we generally see for tabular data.
  • Constantly-changing displays of streaming data.
  • BI with an event-series orientation.
  • Strong alerting.
  • Mobile versions of everything.

And there would be good support for quick-turnaround, easily-operationalized predictive analytics, of the sort that’s fairly central to the visions for Kiji and Spark.

The data management part of that is particularly hard, in that:

  • Different architectures seem naturally well-suited for different parts of the problem.
  • Maturing a new data management product is always difficult, costly and slow.

My thoughts on strengths and weaknesses of some obvious log data management contenders start:

  • Oracle, IBM, and Microsoft have a lot of heft in all things database. But while each of those vendors has great resources and occasionally impressive pieces of new database engineering, none shows much evidence of framing, let alone solving, the problem in the right way(s).
  • SAP owns Sybase, HANA, several old CEP companies, and Business Objects. Add them to the Oracle/IBM/Microsoft list.
  • Teradata has a lot going for them. Their core analytic data management strengths are obvious. They’ve owned Aster for a while, and Aster innovated nPath quite some time ago. They recently added Hadapt, a leader in schema-on-need, as well as Revelytix, which has some good ideas in dataset management. Like most other DBMS vendors, however, Teradata doesn’t yet have much of a story for streaming data, and anyhow the most optimistic case for Teradata involves the difficult task of stitching together disparate data management technologies.
  • HP Vertica has a decent position as well. Probably more proven in general concurrent, scalable performance than others in their peer group (Netezza, Greenplum, et al.), Vertica also was relatively early in innovations relevant to log analysis, including a range of time series/event series features and its own schema-on-need effort. Vertica was also founded by people who were also streaming pioneers (there were heavily overlapping groups of academics behind StreamBase, Vertica and VoltDB), but it’s not clear how that background is reflected in present Vertica product.
  • Splunk, of course, has a complete stack. At the data acquisition and parsing layers, it’s second to none, and it has a considerable set of log-appropriate BI capabilities as well. And for data management it in effect is stitching together two different inverted-list data stores, plus Hadoop.
  • Hadoop distribution vendors such as Cloudera, MapR or Hortonworks offer typically bundle a range of relevant capabilities. HDFS (Hadoop Distributed File System) is the default place to dump entire logs. In most distros, Spark offers a new approach to streaming. Impala, Drill and so on offer query. Flume gathers the log data in the first place. But a lot of the cooler capabilities are immature or unproven, and in some cases that’s putting it mildly.

In the interest of length, I’ll omit discussion of smaller vendors, except to say that Platfora’s integrated-stack event series analytics story deserves attention, and I’m disappointed that I never hear about Sumo Logic. And I don’t know a lot about companies positioned as SIEM (Security Information and Event Management), especially now that SenSage has left the scene.

Teradata bought Hadapt and Revelytix

Wed, 2014-07-23 03:29

My client Teradata bought my (former) clients Revelytix and Hadapt.* Obviously, I’m in confidentiality up to my eyeballs. That said — Teradata truly doesn’t know what it’s going to do with those acquisitions yet. Indeed, the acquisitions are too new for Teradata to have fully reviewed the code and so on, let alone made strategic decisions informed by that review. So while this is just a guess, I conjecture Teradata won’t say anything concrete until at least September, although I do expect some kind of stated direction in time for its October user conference.

*I love my business, but it does have one distressing aspect, namely the combination of subscription pricing and customer churn. When your customers transform really quickly, or even go out of existence, so sometimes does their reliance on you.

I’ve written extensively about Hadapt, but to review:

  • The HadoopDB project was started by Dan Abadi and two grad students.
  • HadoopDB tied a bunch of PostgreSQL instances together with Hadoop MapReduce. Lab benchmarks suggested it was more performant than the coyly named DBx (where x=2), but not necessarily competitive with top analytic RDBMS.
  • Hadapt was formed to commercialize HadoopDB.
  • After some fits and starts, Hadapt was a Cambridge-based company. Former Vertica CEO Chris Lynch invested even before he was a VC, and became an active chairman. Not coincidentally, Hadapt had a bunch of Vertica folks.
  • Hadapt decided to stick with row-based PostgreSQL, Dan Abadi’s previous columnar enthusiasm notwithstanding. Not coincidentally, Hadapt’s performance never blew anyone away.
  • Especially after the announcement of Cloudera Impala, Hadapt’s SQL-on-Hadoop positioning didn’t work out. Indeed, Hadapt laid off most or all of its sales and marketing folks. Hadapt pivoted to emphasize its schema-on-need story.
  • Chris Lynch, who generally seems to think that IT vendors are created to be sold, shopped Hadapt aggressively.

As for what Teradata should do with Hadapt:

  • My initial thought for Hadapt was to just double down, pushing the technology forward, presumably including a columnar option such as the one Citus Data developed.
  • But upon reflection, if it made technical sense to merge the Aster and Hadapt products, that would be better yet.

I herewith apologize to Aster co-founder and Hadapt skeptic Tasso Argyros (who by the way has moved on from Teradata) for even suggesting such heresy. :)

Complicating the story further:

  • Impala lets you treat data in HDFS (Hadoop Distributed File System) as if it were in a SQL DBMS. So does Teradata SQL-H. But Hadapt makes you decide whether the data is in HDFS or the SQL DBMS, and it can’t be in both at once. Edit: Actually, see Dan Abadi’s comments below.
  • Impala and Oracle’s new SQL-H competitor have daemons running on every data node. So does one option in Hadapt. But I don’t think SQL-H does that yet.

I was less involved with Revelytix that with Hadapt (although I’m told I served as the “catalyst” for the original Teradata/Revelytix partnership). That said, Teradata — like Oracle — is always building out a data integration suite to cover a limited universe of data stores. And Revelytix’ dataset management technology is a nice piece toward an integrated data catalog.

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