Cloud computing
Analysis of cloud computing, especially as applied to database management and analytics. Related subjects include:
Comments on Oracle’s third quarter 2012 earnings call
Various reporters have asked me about Oracle’s third quarter 2012 earnings conference call. Specific Q&A includes:
What did Oracle do to have its earnings beat Wall Street’s estimates?
Have a bad second quarter and then set Wall Street’s expectations too low for Q3. This isn’t about strong results; it’s about modest expectations.
Can Oracle be a leader in both hardware and software?
- It’s not inconceivable.
- The observation that Oracle, IBM, and Teradata all are pushing hardware-software combinations has been intriguing ever since IBM bought Netezza. (SAP really isn’t, however; ditto Microsoft.)
- I do think Oracle may be somewhat overoptimistic as to how cooperative the Sun user base will be in buying more high-end product and in paying more in maintenance for the gear they already have.
Beyond that, please see below.
What about Oracle in the cloud?
MySQL is an important cloud supplier. But Oracle overall hasn’t demonstrated much understanding of what cloud technology and business are all about. An expensive SaaS acquisition here or there could indeed help somewhat, but it seems as if Oracle still has a very long way to go.
Other comments
Other comments on the call, whose transcript is available, include: Read more
| Categories: Cloud computing, Exadata, Humor, In-memory DBMS, Oracle, SAP AG, Software as a Service (SaaS) | 5 Comments |
Analytic trends in 2012: Q&A
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
Are there any remaining reasons to put new OLTP applications on disk?
Once again, I’m working with an OLTP SaaS vendor client on the architecture for their next-generation system. Parameters include:
- 100s of gigabytes of data at first, growing to >1 terabyte over time.
- High peak loads.
- Public cloud portability (but they have private data centers they can use today).
- Simple database design — not a lot of tables, not a lot of columns, not a lot of joins, and everything can be distributed on the same customer_ID key.
- Stream the data to a data warehouse, that will grow to a few terabytes. (Keeping only one year of OLTP data online actually makes sense in this application, but of course everything should go into the DW.)
So I’m leaning to saying: Read more
Remote machine-generated data
I refer often to machine-generated data, which is commonly generated inexpensively and in log-like formats, and is often best aggregated in a big bit bucket before you try to do much analysis on it. The term has caught on, to the point that perhaps it’s time to distinguish more carefully among different kinds of machine-generated data. In particular, I think it may be useful to distinguish between:
- Log-stream machine-generated data, when what you’re looking at — at least initially — is the entire output of verbose logging systems.
- Remote machine-generated data.
Here’s what I’m thinking of for the second category. I rather frequently hear of cases in which data is generated by large numbers of remote machines, which occasionally send messages home. For example: Read more
| Categories: Analytic technologies, Cloud computing, Log analysis, MySQL, Netezza, Splunk, Truviso | 2 Comments |
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
What to think about BEFORE you make a technology decision
When you are considering technology selection or strategy, there are a lot of factors that can each have bearing on the final decision — a whole lot. Below is a very partial list.
In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.
Multitenancy is particularly interesting, because it has numerous implications. Read more
Quick thoughts on Oracle-on-Amazon
Amazon has a page up for what it calls Amazon RDS for Oracle Database. You can rent Amazon instances suitable for running Oracle, and bring your own license (BYOL), or you can rent a “License Included” instance that includes Oracle Standard Edition One (a cheap version of Oracle that is limited to two sockets).
My quick thoughts start:
- Mainly, this isn’t for production usage. But exceptions might arise when:
- An application, from creation to abandonment, is only expected to have a short lifespan, in support of a specific project.
- There is an extreme internal-politics bias to operating versus capital expenses, or something like that, forcing a user department to cloud production deployment even when it doesn’t make much rational sense.
- An application is small enough, or the situation is sufficiently desperate, that any inefficiencies are outweighed by convenience.
- There is non-production appeal. In particular:
- Spinning up a quick cloud instance can make a lot of sense for a developer.
- The same goes if you want to sell an Oracle-based application and need to offer demo/test capabilities.
- The same might go for off-site replication/disaster recovery.
Of course, those are all standard observations every time something that’s basically on-premises software is offered in the cloud. They’re only reinforced by the fact that the only Oracle software Amazon can actually license you is a particularly low-end edition.
And Oracle is indeed on-premises software. In particular, Oracle is hard enough to manage when it’s on your premises, with a known hardware configuration; who would want to try to manage a production instance of Oracle in the cloud?
| Categories: Amazon and its cloud, Cloud computing, Oracle | 7 Comments |
Introduction to SnapLogic
I talked with the SnapLogic team last week, in connection with their SnapReduce Hadoop-oriented offering. This gave me an opportunity to catch up on what SnapLogic is up to overall. SnapLogic is a data integration/ETL (Extract/Transform/Load) company with a good pedigree: Informatica founder Gaurav Dillon invested in and now runs SnapLogic, and VC Ben Horowitz is involved. SnapLogic company basics include:
- SnapLogic has raised about $18 million from Gaurav Dillon and Andreessen Horowitz.
- SnapLogic has almost 60 people.
- SnapLogic has around 150 customers.
- Based in San Mateo, SnapLogic has an office in the UK and is growing its European business.
- SnapLogic has both SaaS (Software as a Service) and on-premise availability, but either way you pay on a subscription basis.
- Typical SnapLogic deal size is under $20K/year. Accordingly, SnapLogic sells over the telephone.
- SnapReduce is in beta with about a dozen customers, and slated for release by year-end.
SnapLogic’s core/hub product is called SnapCenter. In addition, for any particular kind of data one might want to connect, there are “snaps” which connect to — i.e. snap into — SnapCenter.
SnapLogic’s market position(ing) sounds like Cast Iron’s, by which I mean: Read more
| Categories: Cloud computing, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, SnapLogic, Software as a Service (SaaS) | 1 Comment |
Attensity update
I talked with Michelle de Haaff and Ian Hersey of Attensity back in February. We covered a lot of ground, so let’s start with a very high-level view.
- Two years ago, Attensity merged with two other companies in somewhat related businesses, thus expanding 4X or so in size.
- Due to the merger, Attensity now has two core lines of business:
- Text analytics.
- Driving actions, such as call center or social media response, based on text analytics.
- The combined Attensity is part American, part German.
- Attensity’s German part compels it to do some public financial reporting. Attensity will do $50-60 million in 2011 revenue.
- Attensity crunches text in 17 languages. English is preeminent. #2 is — you guessed it! — German.
- A big part of Attensity’s business (or at least of its value proposition) is analyzing the text in social media. Attensity boasts coverage of 75 million social media sources, such as blogs, forums, or review sites.
The four most interesting technical points were probably:
- Attensity has changed how it does exhaustive extraction. I’m having some trouble writing that part up, so for now I’ll just refer you to Attensity’s own description of the new way of doing things.
- Attensity has development work underway meant to address some of the problems in text analytics/other analytics integration. I don’t feel I got enough detail to want to talk about that yet.
- Attensity runs its own data centers, with approximately 60 Hadoop/HBase nodes and 30 nodes of Apache Solr (open source text search). More on that below.
- Attensity now OEMs Vertica. More on that below too.
Some more specific notes include: Read more
| Categories: Analytic technologies, Cloud computing, Hadoop, HBase, Predictive modeling and advanced analytics, Software as a Service (SaaS), Sybase, Vertica Systems | 7 Comments |
Three kinds of software innovation, and whether patents could possibly work for them
In connection with an attempt to articulate my views on software patents (more on those below), I was thinking about the different ways in which software development can be innovative. And it turns out that most forms of software innovation can, at their core, be assigned to one or more of three overlapping categories: Read more