Cloud computing

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

August 24, 2013

Hortonworks business notes

Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes  from my call — for which Rob Bearden* didn’t bother showing up — include, in no particular order:

*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.

In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.  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 20, 2013

The refactoring of everything

I’ll start with three observations:

As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

May 20, 2013

Some stuff I’m working on

1. I have some posts up on Strategic Messaging. The most recent are overviews of messaging, pricing, and positioning.

2. Numerous vendors are blending SQL and JSON management in their short-request DBMS. It will take some more work for me to have a strong opinion about the merits/demerits of various alternatives.

The default implementation — one example would be Clustrix’s — is to stick the JSON into something like a BLOB/CLOB field (Binary/Character Large Object), index on individual values, and treat those indexes just like any others for the purpose of SQL statements. Drawbacks include:

IBM DB2 is one recent arrival to the JSON party. Unfortunately, I forgot to ask whether IBM’s JSON implementation was based on IBM DB2 pureXML when I had the chance, and IBM hasn’t gotten around to answering my followup query.

3. Nor has IBM gotten around to answering my followup queries on the subject of BLU, an interesting-sounding columnar option for DB2.

4. Numerous clients have asked me whether they should be active in DBaaS (DataBase as a Service). After all, Amazon, Google, Microsoft, Rackspace and salesforce.com are all in that business in some form, and other big companies have dipped toes in as well. Read more

April 14, 2013

Introduction to Deep Information Sciences and DeepDB

I talked Friday with Deep Information Sciences, makers of DeepDB. Much like TokuDB — albeit with different technical strategies — DeepDB is a single-server DBMS in the form of a MySQL engine, whose technology is concentrated around writing indexes quickly. That said:

*For reasons that do not seem closely related to product reality, DeepDB is marketed as if it supports “unstructured” data today.

Other NewSQL DBMS seem “designed for big data and the cloud” to at least the same extent DeepDB is. However, if we’re interpreting “big data” to include multi-structured data support — well, only half or so of the NewSQL products and companies I know of share Deep’s interest in branching out. In particular:

Edit: MySQL has some sort of an optional NoSQL interface, and hence so presumably do MySQL-compatible TokuDB, GenieDB, Clustrix, and MemSQL.

Also, some of those products do not today have the transparent scale-out that Deep plans to offer in the future.

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 17, 2013

Notes and links, February 17, 2013

1. It boggles my mind that some database technology companies still don’t view compression as a major issue. Compression directly affects storage and bandwidth usage alike — for all kinds of storage (potentially including RAM) and for all kinds of bandwidth (network, I/O, and potentially on-server).

Trading off less-than-maximal compression so as to minimize CPU impact can make sense. Having no compression at all, however, is an admission of defeat.

2. People tend to misjudge Hadoop’s development pace in either of two directions. An overly expansive view is to note that some people working on Hadoop are trying to make it be all things for all people, and to somehow imagine those goals will soon be achieved. An overly narrow view is to note an important missing feature in Hadoop, and think there’s a big business to be made out of offering it alone.

At this point, I’d guess that Cloudera and Hortonworks have 500ish employees combined, many of whom are engineers. That allows for a low double-digit number of 5+ person engineering teams, along with a number of smaller projects. The most urgently needed features are indeed being built. On the other hand, a complete monument to computing will not soon emerge.

3. Schooner’s acquisition by SanDisk has led to the discontinuation of Schooner’s SQL DBMS SchoonerSQL. Schooner’s flash-optimized key-value store Membrain continues. I don’t have details, but the Membrain web page suggests both data store and cache use cases.

4. There’s considerable personnel movement at Boston-area database technology companies right now. Please ping me directly if you care.

Read more

February 6, 2013

Key questions when selecting an analytic RDBMS

I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:

Let’s drill down. Read more

January 16, 2013

NuoDB marketing mishegas

I must start by apologizing for giving a quote in a press release whose contents I deplore. Unlike occasions on which I’ve posted about inaccurate quotes, in this case the fault is mine. The quote is quite accurate. And NuoDB didn’t mislead me about the release’s contents; I just neglected to ask.

NuoDB evidently subscribes to the marketing fallacy:

But to my taste, NuoDB’s worst travesty is not the deafening drumroll before launch (I asked off their mailing list months before), nor the claim that NuoDB’s launch would be a “big day” for the database industry (annoying but ordinary hype), nor the emergent flock of birds foofarah, nor even NuoDB’s overwrought benchmark marketing (distressingly many vendors do that).

Rather, I think NuoDB’s greatest marketing offense to date is its Codd-imitating “12 rules” for cloud database management. Read more

January 12, 2013

Introduction to NuoDB

NuoDB has an interesting NewSQL story. NuoDB’s core design goals seem to be:

Read more

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