Vertica Systems

Analysis of columnar data warehouse DBMS vendor Vertica Systems. Related subjects include:

March 23, 2014

Wants vs. needs

In 1981, Gerry Chichester and Vaughan Merlyn did a user-survey-based report about transaction-oriented fourth-generation languages, the leading application development technology of their day. The report included top-ten lists of important features during the buying cycle and after implementation. The items on each list were very similar — but the order of the items was completely different. And so the report highlighted what I regard as an eternal truth of the enterprise software industry:

What users value in the product-buying process is quite different from what they value once a product is (being) put into use.

Here are some thoughts about how that comes into play today.

Wants outrunning needs

1. For decades, BI tools have been sold in large part via demos of snazzy features the CEO would like to have on his desk. First it was pretty colors; then it was maps; now sometimes it’s “real-time” changing displays. Other BI features, however, are likely to be more important in practice.

2. In general, the need for “real-time” BI data freshness is often exaggerated. If you’re a human being doing a job that’s also often automated at high speed — for example network monitoring or stock trading — there’s a good chance you need fully human real-time BI. Otherwise, how much does a 5-15 minute delay hurt? Even if you’re monitoring website sell-through — are your business volumes really high enough that 5 minutes matters much? eBay answered “yes” to that question many years ago, but few of us work for businesses anywhere near eBay’s scale.

Even so, the want for speed keeps growing stronger. :)

3. Similarly, some desires for elastic scale-out are excessive. Your website selling koi pond accessories should always run well on a single server. If you diversify your business to the point that that’s not true, you’ll probably rewrite your app by then as well.

4. Some developers want to play with cool new tools. That doesn’t mean those tools are the best choice for the job. In particular, boring old SQL has merits — such as joins! — that shiny NoSQL hasn’t yet replicated.

5. Some developers, on the other hand, want to keep using their old tools, on which they are their employers’ greatest experts. That doesn’t mean those tools are the best choice for the job either.

6. More generally, some enterprises insist on brand labels that add little value but lots of expense. Yes, there are many benefits to vendor consolidation, and you may avoid many headaches if you stick with not-so-cutting-edge technology. But “enterprise-grade” hardware failure rates may not differ enough from “consumer-grade” ones to be worth paying for.

Read more

March 17, 2014

Notes and comments, March 17, 2014

I have ever more business-advice posts up on Strategic Messaging. Recent subjects include pricing and stealth-mode marketing. Other stuff I’ve been up to includes:

The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.

Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing. :D

The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.

Basho had a major change in leadership. A Twitter exchange ensued. :) Joab Jackson offered a more sober — figuratively and literally — take.

Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.

*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.

WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.

Disclosure: My fingerprints are all over that deal.

In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.

I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.

I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.

I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.

*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.

December 5, 2013

Vertica 7

It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:

Other Vertica 7 enhancements include:

Overall, two recurring themes in our discussion were:

Read more

October 18, 2013

Entity-centric event series analytics

Much of modern analytic technology deals with what might be called an entity-centric sequence of events. For example:

Analytic questions are asked along the lines “Which sequences of events are most productive in terms of leading to the events we really desire?”, such as product sales. Another major area is sessionization, along with data preparation tasks that boil down to arranging data into meaningful event sequences in the first place.

A number of my clients are focused on such scenarios, including WibiData, Teradata Aster (e.g. via nPath), Platfora (in the imminent Platfora 3), and others. And so I get involved in naming exercises. The term entity-centric came along a while ago, because “user-centric” is too limiting. (E.g., the data may not be about a person, but rather specifically about the actions taken on her mobile device.) Now I’m adding the term event series to cover the whole scenario, rather than the “event sequence(s)” I might appear to have been hinting at above.

I decided on “event series” earlier this week, after noting that:  Read more

August 12, 2013

Things I keep needing to say

Some subjects just keep coming up. And so I keep saying things like:

Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.

Most generalizations about Hadoop are false. Reasons include:

Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.

Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.

Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)

Read more

July 2, 2013

Notes and comments, July 2, 2013

I’m not having a productive week, part of the reason being a hard drive crash that took out early drafts of what were to be last weekend’s blog posts. Now I’m operating from a laptop, rather than my preferred dual-monitor set-up. So please pardon me if I’m concise even by comparison to my usual standards.

*Basic and unavoidable ETL (Extract/Transform/Load) of course excepted.

**I could call that ABC (Always Be Comparing) or ABT (Always Be Testing), but they each sound like – well, like The Glove and the Lions.

June 10, 2013

Where things stand in US government surveillance

Edit: Please see the comment thread below for updates. Please also see a follow-on post about how the surveillance data is actually used.

US government surveillance has exploded into public consciousness since last Thursday. With one major exception, the news has just confirmed what was already thought or known. So where do we stand?

My views about domestic data collection start:

*Recall that these comments are US-specific. Data retention legislation has been proposed or passed in multiple countries to require recording of, among other things, all URL requests, with the stated goal of fighting either digital piracy or child pornography.

As for foreign data: Read more

April 29, 2013

More on Actian/ParAccel/VectorWise/Versant/etc.

My quick reaction to the Actian/ParAccel deal was negative. A few challenges to my views then emerged. They didn’t really change my mind.

Amazon Redshift

Amazon did a deal with ParAccel that amounted to:

Some argue that this is great for ParAccel’s future prospects. I’m not convinced.

No doubt there are and will be Redshift users, evidently including Infor. But so far as I can tell, Redshift uses very standard SQL, so it doesn’t seed a ParAccel market in terms of developer habits. The administration/operation story is similar. So outside of general validation/bragging rights, Redshift is not a big deal for ParAccel.

OEMs and bragging rights

It’s not just Amazon and Infor; there’s also a MicroStrategy deal to OEM ParAccel — I think it’s the real ParAccel software in that case — for a particular service, MicroStrategy Wisdom. But unless I’m terribly mistaken, HP Vertica, Sybase IQ and even Infobright each have a lot more OEMs than ParAccel, just as they have a lot more customers than ParAccel overall.

This OEM success is a great validation for the idea of columnar analytic RDBMS in general, but I don’t see where it’s an advantage for ParAccel vs. the columnar leaders. Read more

April 25, 2013

Goodbye VectorWise, farewell ParAccel?

Actian, which already owns VectorWise, is also buying ParAccel. The argument for why this kills VectorWise is simple. ParAccel does most things VectorWise does, more or less as well. It also does a lot more:

One might conjecture that ParAccel is bad at highly concurrent, single-node use cases, and VectorWise is better at them — but at the link above, ParAccel bragged of supporting 5,000 concurrent connections. Besides, if one is just looking for a high-use reporting server, why not get Sybase IQ?? Anyhow, Actian hasn’t been investing enough in VectorWise to make it a major market player, and they’re unlikely to start now that they own ParAccel as well.

But I expect ParAccel to fail too. Reasons include:

Read more

February 5, 2013

Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations

To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.

Gartner seems confused about Kognitio’s products and history alike.

Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.

* non-existent

In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more

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