Cloud computing

Analysis of cloud computing, especially as applied to database management and analytics. Related subjects include:

June 30, 2017

Analytics on the edge?

There’s a theory going around to the effect that:

There’s enough truth to all that to make it worth discussing. But the strong forms of the claims seem overblown.

1. This story doesn’t even make sense except for certain new classes of application. Traditional business applications run all over the world, in dedicated or SaaSy modes as the case may be. E-commerce is huge. So is content delivery. Architectures for all those things will continue to evolve, but what we have now basically works.

2. When it comes to real-world appliances, this story is partially accurate. An automobile is a rolling network of custom Linux systems, each running hand-crafted real-time apps, a few of which also have minor requirements for remote connectivity. That’s OK as far as it goes, but there could be better support for real-time operational analytics. If something as flexible as Spark were capable of unattended operation, I think many engineers of real-world appliances would find great ways to use it.

3. There’s a case to be made for something better yet. I think the argument is premature, but it’s worth at least a little consideration.  Read more

June 14, 2017

Light-touch managed services

Cloudera recently introduced Cloudera Altus, a Hadoop-in-the-cloud offering with an interesting processing model:

Thus, you avoid a potential security risk (shipping your data to Cloudera’s service). I’ve tentatively named this strategy light-touch managed services, and am interested in exploring how broadly applicable it might or might not be.

For light-touch to be a good approach, there should be (sufficiently) little downside in performance, reliability and so on from having your service not actually control the data. That assumption is trivially satisfied in the case of Cloudera Altus, because it’s not an ordinary kind of app; rather, its whole function is to improve the job-running part of your stack. Most kinds of apps, however, want to operate on your data directly. For those, it is more challenging to meet acceptable SLAs (Service-Level Agreements) on a light-touch basis.

Let’s back up and consider what “light-touch” for data-interacting apps (i.e., almost all apps) would actually mean. The basics are:  Read more

June 14, 2017

Cloudera Altus

I talked with Cloudera before the recent release of Altus. In simplest terms, Cloudera’s cloud strategy aspires to:

In other words, Cloudera is porting its software to an important new platform.* And this port isn’t complete yet, in that Altus is geared only for certain workloads. Specifically, Altus is focused on “data pipelines”, aka data transformation, aka “data processing”, aka new-age ETL (Extract/Transform/Load). (Other kinds of workload are on the roadmap, including several different styles of Impala use.) So what about that is particularly interesting? Well, let’s drill down.

*Or, if you prefer, improving on early versions of the port.

Read more

October 3, 2016

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:

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.

Read more

August 28, 2016

Are analytic RDBMS and data warehouse appliances obsolete?

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.

Read more

July 31, 2016

Notes on Spark and Databricks — technology

During my recent visit to Databricks, I of course talked a lot about technology — largely with Reynold Xin, but a bit with Ion Stoica as well. Spark 2.0 is just coming out now, and of course has a lot of enhancements. At a high level:

The majority of Databricks’ development efforts, however, are specific to its cloud service, rather than being donated to Apache for the Spark project. Some of the details are NDA, but it seems fair to mention at least:

Two of the technical initiatives Reynold told me about seemed particularly cool. Read more

July 19, 2016

Notes from a long trip, July 19, 2016

For starters:

A running list of recent posts is:

Subjects I’d like to add to that list include:

Read more

January 22, 2016

Cloudera in the cloud(s)

Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.

Making Cloudera run in the cloud has three major aspects:

Features new in this week’s release of Cloudera Director include:

I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.

As for porting, let me start by noting: Read more

December 31, 2015

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

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:

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:

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. Read more

December 7, 2015

Transitioning to the cloud(s)

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:

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: Read more

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