DBMS product categories

Analysis of database management technology in specific product categories. Related subjects include:

August 31, 2014

Notes from a visit to Teradata

I spent a day with Teradata in Rancho Bernardo last week. Most of what we discussed is confidential, but I think the non-confidential parts and my general impressions add up to enough for a post.

First, let’s catch up with some personnel gossip. So far as I can tell:

The biggest change in my general impressions about Teradata is that they’re having smart thoughts about the cloud. At least, Oliver is. All details are confidential, and I wouldn’t necessarily expect them to become clear even in October (which once again is the month for Teradata’s user conference). My main concern about all that is whether Teradata’s engineering team can successfully execute on Oliver’s directives. I’m optimistic, but I don’t have a lot of detail to support my good feelings.

In some quick-and-dirty positioning and sales qualification notes, which crystallize what we already knew before:

Also: Read more

July 14, 2014

21st Century DBMS success and failure

As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.

DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.

In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.

Buyer inertia is a greater concern.

A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.

Otherwise I’d say:  Read more

June 18, 2014

Using multiple data stores

I’m commonly asked to assess vendor claims of the kind:

So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:

Horses for courses

It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:

Vendors are part of this consensus; already in 2005 I observed

For all practical purposes, there are no DBMS vendors left advocating single-server strategies.

Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.

Multiple data stores for a single application

We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree:  Read more

May 6, 2014

Notes and comments, May 6, 2014

After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

Here is a catch-all post to complete the set.  Read more

May 1, 2014

MemSQL update

I stopped by MemSQL last week, and got a range of new or clarified information. For starters:

On the more technical side: Read more

March 28, 2014

NoSQL vs. NewSQL vs. traditional RDBMS

I frequently am asked questions that boil down to:

The details vary with context — e.g. sometimes MySQL is a traditional RDBMS and sometimes it is a new kid — but the general class of questions keeps coming. And that’s just for short-request use cases; similar questions for analytic systems arise even more often.

My general answers start:

In particular, migration away from legacy DBMS raises many issues:  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.

February 9, 2014

Distinctions in SQL/Hadoop integration

Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):

In particular:

Let’s go to some examples. Read more

February 2, 2014

Some stuff I’m thinking about (early 2014)

From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:

Other stuff on my mind includes but is not limited to:

1. Certain categories of buying organizations are inherently leading-edge.

Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.

Read more

November 29, 2013

SaaS appliances, SaaS data centers, and customer-premises SaaS

Conclusions

I think that most sufficiently large enterprise SaaS vendors should offer an appliance option, as an alternative to the core multi-tenant service. In particular:

How I reached them

Core reasons for selling or using SaaS (Software as a Service) as opposed to licensed software start:

Conceptually, then, customer-premises SaaS is not impossible, even though one of the standard Big Three SaaS benefits is lost. Indeed:

But from an enterprise standpoint, that’s all (relatively) simple stuff. So we’re left with a more challenging question — does customer-premises SaaS make sense in the case of enterprise applications or other server software?

Read more

Next Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.