Data mart outsourcing
Discussion of services that analyze large databases on an outsourced basis. Related subjects include:
- Data warehousing
- SaaS (Software as a Service)
- 1010data
- TEOCO
- (in The Monash Report) Verix
- (in Text Technologies) Text mining SaaS
Notes on the transition to the cloud
1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:
- The Amazon cloud, Microsoft Azure, and their competitors, aka public cloud.
- Software as a service, aka SaaS.
- Co-location in off-premises data centers, aka colo.
- On-premises clusters (truly on-prem or colo as the case may be) designed to run a broad variety of applications, aka private cloud.
Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.
This is a good example of Monash’s Laws of Commercial Semantics.
2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.
This fact now seems to be widely understood.
Differentiation in business intelligence
Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:
- Both kinds of products query and aggregate data.
- Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists.
- You really, really, really don’t want your customer data to leak via a security breach in either kind of product.
That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:
- BI is less mission-critical than some other database uses.
- BI has done a lot less than DBMS to deal with multi-structured data.
- Scalability demands on BI are less than those on DBMS — indeed, they’re the ones that are left over after the DBMS has done its data crunching first.
And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.
Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more
| Categories: Business intelligence, Buying processes, ClearStory Data, Data mart outsourcing, Pricing, QlikTech and QlikView, Rocana, Tableau Software | Leave a Comment |
Snowflake Computing
I talked with the Snowflake Computing guys Friday. For starters:
- Snowflake is offering an analytic DBMS on a SaaS (Software as a Service) basis.
- The Snowflake DBMS is built from scratch (as opposed, to for example, being based on PostgreSQL or Hadoop).
- The Snowflake DBMS is columnar and append-only, as has become common for analytic RDBMS.
- Snowflake claims excellent SQL coverage for a 1.0 product.
- Snowflake, the company, has:
- 50 people.
- A similar number of current or past users.
- 5 referenceable customers.
- 2 techie founders out of Oracle, plus Marcin Zukowski.
- Bob Muglia as CEO.
Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*
*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.
In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:
- Ingest formats are JSON, XML or AVRO for now.
- I gather that the system automagically decides which fields/attributes are sufficiently repeated to be broken out as separate columns; also, there’s a column for the documents themselves.
I don’t know enough details to judge whether I’d call that an example of schema-on-need.
A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more
Data as an asset
We all tend to assume that data is a great and glorious asset. How solid is this assumption?
- Yes, data is one of the most proprietary assets an enterprise can have. Any of the Goldman Sachs big three* — people, capital, and reputation — are easier to lose or imitate than data.
- In many cases, however, data’s value diminishes quickly.
- Determining the value derived from owning, analyzing and using data is often tricky — but not always. Examples where data’s value is pretty clear start with:
- Industries which long have had large data-gathering research budgets, in areas such as clinical trials or seismology.
- Industries that can calculate the return on mass marketing programs, such as internet advertising or its snail-mail predecessors.
*”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.
This all raises the idea — if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include: Read more
| Categories: Data mart outsourcing, eBay, Health care, Investment research and trading, Log analysis, Scientific research, Text, Web analytics | 9 Comments |
Thoughts on SaaS
Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:
- SaaS has been around for over half a century, and at times has been the dominant mode of application delivery.
- The term multi-tenancy is being used in several different ways.
- Multi-tenancy, in the purest sense, is inessential to SaaS. It’s simply an implementation choice that has certain benefits for the SaaS provider. And by the way, …
- … salesforce.com, the chief proponent of the theory that true multi-tenancy is the hallmark of true SaaS, abandoned that position this week.
- Internet-based services are commonly, if you squint a little, SaaS. Examples include but are hardly limited to Google, Twitter, Dropbox, Intuit, Amazon Web Services, and the company that hosts this blog (KnownHost).
- Some of the core arguments for SaaS’ rise, namely the various efficiencies of data center outsourcing and scale, apply equally to the public cloud, to SaaS, and to AEaaS (Anything Else as a Service).
- These benefits are particularly strong for inherently networked use cases. For example, you really don’t want to be hosting your website yourself. And salesforce.com got its start supporting salespeople who worked out of remote offices.
- In theory and occasionally in practice, certain SaaS benefits, namely the outsourcing of software maintenance and updates, could be enjoyed on-premises as well. Whether I think that could be a bigger deal going forward will be explored in future posts.
For smaller enterprises, the core outsourcing argument is compelling. How small? Well:
- What’s the minimum level of IT operations headcount needed for mission-critical systems? Let’s just say “several”.
- What does that cost? Fully burdened, somewhere in the six figures.
- What fraction of the IT budget should such headcount be? As low a double digit percentage as possible.
- What fraction of revenues should be spent on IT? Some single-digit percentage.
So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*
Amazon Redshift and its implications
Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:
- Amazon Redshift is missing parts of ParAccel, notably the extensibility framework.
- ParAccel did some engineering to make its DBMS run better in the cloud.
- Amazon did some engineering in the areas it knows better than ParAccel — cloud provisioning, cloud billing, and so on.
“We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.
The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.
So who should and will use Amazon Redshift? For starters, I’d say: Read more
Introduction to Metamarkets and Druid
I previously dropped a few hints about my clients at Metamarkets, mentioning that they:
- Have built vertical-market analytic platform technology.
- Use a lot of Hadoop.
- Throw good parties. (That’s where the background photo on my Twitter page comes from.)
But while they’re a joy to talk with, writing about Metamarkets has been frustrating, with many hours and pages of wasted of effort. Even so, I’m trying again, in a three-post series:
- Introduction to Metamarkets and Druid (this post)
- Druid overview
- Metamarkets’ back-end technology
Much like Workday, Inc., Metamarkets is a SaaS (Software as a Service) company, with numerous tiers of servers and an affinity for doing things in RAM. That’s where most of the similarities end, however, as Metamarkets is a much smaller company than Workday, doing very different things.
Metamarkets’ business is SaaS (Software as a Service) business intelligence, on large data sets, with low latency in both senses (fresh data can be queried on, and the queries happen at RAM speed). As you might imagine, Metamarkets is used by digital marketers and other kinds of internet companies, whose data typically wants to be in the cloud anyway. Approximate metrics for Metamarkets (and it may well have exceeded these by now) include 10 customers, 100,000 queries/day, 80 billion 100-byte events/month (before summarization), 20 employees, 1 popular CEO, and a metric ton of venture capital.
To understand how Metamarkets’ technology works, it probably helps to start by realizing: Read more
Thinking about market segments
It is a reasonable (over)simplification to say that my business boils down to:
- Advising vendors what/how to sell.
- Advising users what/how to buy.
One complication that commonly creeps in is that different groups of users have different buying practices and technology needs. Usually, I nod to that point in passing, perhaps by listing different application areas for a company or product. But now let’s address it head on. Whether or not you care about the particulars, I hope the sheer length of this post reminds you that there are many different market segments out there.
Last June I wrote:
In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.
I’d further say that it matters whether the buyer:
- Is a large central IT organization.
- Is the well-staffed IT organization of a particular business department.
- Is a small, frazzled IT organization.
- Has strong engineering or technical skills, but less in the way of IT specialists.
- Is trying to skate by without much technical knowledge of any kind.
Now let’s map those considerations (and others) to some specific market segments. Read more
Notes on the ClearStory Data launch, including an inaccurate quote from me
ClearStory Data launched, with nice coverage in the New York Times, Computerworld, and elsewhere. But from my standpoint, there were some serious problems:
- (Bad.) I was planning to cover the launch as well, in a split exclusive, but that plan was changed, costing me considerable wasted work.
- (Worse.) I wasn’t told of the change as soon as it was known. Indeed, I wasn’t told at all; I was left to infer it from the fact that I was now being asked to talk with other reporters.
- (Horrific.) I was quoted in the ClearStory launch press release, but while the sentiments were reasonably in line with my own, the quote was incorrect.*
I’m utterly disgusted with this whole mess, although after talking with her a lot I’m fine with CEO Sharmila Mulligan’s part in it, which is to say with ClearStory’s part in general.
*I avoid the term “platform” as much as possible; indeed, I still don’t really know what the “new platforms” part was supposed to refer to. The Frankenquote wound up with some odd grammar as well.
Actually, in principle I’m a pretty close adviser to ClearStory (for starters, they’re one of my stealth-mode clients). That hasn’t really ramped up yet; in particular, I haven’t had a technical deep dive. So for now I’ll just say:
| Categories: Business intelligence, ClearStory Data, Data integration and middleware, Data mart outsourcing | 1 Comment |
Applications of an analytic kind
The most straightforward approach to the applications business is:
- Take general-purpose technology and think through how to apply it to a specific application domain.
- Produce packaged application software accordingly.
However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:
- Analytic applications of that kind are rarely complete.
- Incomplete applications rarely sell well.
I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.
Reasons that analytic applications are commonly less complete than the transactional kind include: Read more