Data warehouse appliances

Analysis of data warehouse appliances – i.e., of hardware/software bundles optimized for fast query and analysis of large volumes of (usually) relational data. Related subjects include:

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 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

November 24, 2013

Thoughts on SaaS

Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:

For smaller enterprises, the core outsourcing argument is compelling. How small? Well:

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.*

Read more

November 10, 2013

RDBMS and their bundle-mates

Relational DBMS used to be fairly straightforward product suites, which boiled down to:

Now, however, most RDBMS are sold as part of something bigger.

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

August 6, 2013

Hortonworks, Hadoop, Stinger and Hive

I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger —  but at my request we cycled through a bunch of other topics as well. Company-specific notes include:

Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:

*By the way — Teradata seems serious about pushing the UDA as a core message.

Ecosystem notes, in Hortonworks’ perception, included:

I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.

Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:

Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.

Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include:  Read more

April 15, 2013

Notes on Teradata systems

Teradata is announcing its new high-end systems, the Teradata 6700 series. Notes on that include:

Teradata is also talking about data integration and best-of-breed systems, with buzzwords such as:

Read more

March 24, 2013

Appliances, clusters and clouds

I believe:

I shall explain.

Arguments for hosting applications on some kind of cluster include:

Arguments specific to the public cloud include:

That’s all pretty compelling. However, these are not persuasive reasons to put everything on a SINGLE cluster or cloud. They could as easily lead you to have your VMware cluster and your Exadata rack and your Hadoop cluster and your NoSQL cluster and your object storage OpenStack cluster — among others — all while participating in several different public clouds as well.

Why would you not move work into a cluster at all? First, if ain’t broken, you might not want to fix it. Some of the cluster options make it easy for you to consolidate existing workloads — that’s a central goal of VMware and Exadata — but others only make sense to adopt in connection with new application projects. Second, you might just want device locality. I have a gaming-class PC next to my desk; it drives a couple of monitors; I like that arrangement. Away from home I carry a laptop computer instead. Arguments can be made for small remote-office servers as well.

Read more

February 27, 2013

Hadoop distributions

Elephants! Elephants!
One elephant went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.

Elephants! Elephants!
Two elephants went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.

Elephants! Elephants!
Three elephants went out to play
Etc.

–  Popular children’s song

It’s Strata week, with much Hadoop news, some of which I’ve been briefed on and some of which I haven’t. Rather than delve into fine competitive details, let’s step back and consider some generalities. First, about Hadoop distributions and distro providers:

Most of the same observations could apply to Hadoop appliance vendors.

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