Software as a Service (SaaS)
Analysis of software-as-a-service offerings with a database or analytic focus, or data connectivity tools focused on SaaS. Related subjects include:
- Data mart outsourcing
- (in Text Technologies) Text analytics SaaS
- (in The Monash Report) Strategic issues in SaaS
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 |
Soundbites: the Facebook/MySQL/NoSQL/VoltDB/Stonebraker flap, continued
As a follow-up to the latest Stonebraker kerfuffle, Derrick Harris asked me a bunch of smart followup questions. My responses and afterthoughts include:
- Facebook et al. are in effect Software as a Service (SaaS) vendors, not enterprise technology users. In particular:
- They have the technical chops to rewrite their code as needed.
- Unlike packaged software vendors, they’re not answerable to anybody for keeping legacy code alive after a rewrite. That makes migration a lot easier.
- If they want to write different parts of their system on different technical underpinnings, nobody can stop them. For example …
- … Facebook innovated Cassandra, and is now heavily committed to HBase.
- It makes little sense to talk of Facebook’s use of “MySQL.” Better to talk of Facebook’s use of “MySQL + memcached + non-transparent sharding.” That said:
- It’s hard to see why somebody today would use MySQL + memcached + non-transparent sharding for a new project. At least one of Couchbase or transparently-sharded MySQL is very likely a superior alternative. Other alternatives might be better yet.
- As noted above in the example of Facebook, the many major web businesses that are using MySQL + memcached + non-transparent sharding for existing projects can be presumed able to migrate away from that stack as the need arises.
Continuing with that discussion of DBMS alternatives:
- If you just want to write to the memcached API anyway, why not go with Couchbase?
- If you want to go relational, why not go with MySQL? There are many alternatives for scaling or accelerating MySQL — dbShards, Schooner, Akiban, Tokutek, ScaleBase, ScaleDB, Clustrix, and Xeround come to mind quickly, so there’s a great chance that one or more will fit your use case. (And if you don’t get the choice of MySQL flavor right the first time, porting to another one shouldn’t be all THAT awful.)
- If you really, really want to go in-memory, and don’t mind writing Java stored procedures, and don’t need to do the kinds of joins it isn’t good at, but do need to do the kinds of joins it is, VoltDB could indeed be a good alternative.
And while we’re at it — going schema-free often makes a whole lot of sense. I need to write much more about the point, but for now let’s just say that I look favorably on the Big Four schema-free/NoSQL options of MongoDB, Couchbase, HBase, and Cassandra.
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 |
Object-oriented database management systems (OODBMS)
There seems to be a fair amount of confusion about object-oriented database management systems (OODBMS). Let’s start with a working definition:
An object-oriented database management system (OODBMS, but sometimes just called “object database”) is a DBMS that stores data in a logical model that is closely aligned with an application program’s object model. Of course, an OODBMS will have a physical data model optimized for the kinds of logical data model it expects.
If you’re guessing from that definition that there can be difficulties drawing boundaries between the application, the application programming language, the data manipulation language, and/or the DBMS — you’re right. Those difficulties have been a big factor in relegating OODBMS to being a relatively niche technology to date.
Examples of what I would call OODBMS include: Read more
| Categories: Cache, In-memory DBMS, Intersystems and Cache', Memory-centric data management, Objectivity and Infinite Graph, OLTP, Software as a Service (SaaS), Starcounter | 20 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 |
MySQL soundbites
Oracle announced MySQL enhancements, plus intentions to use MySQL to compete against Microsoft SQL Server. My thoughts, lightly edited from an instant message Q&A, include:
- Given how hard Oracle fought the antitrust authorities to keep MySQL around the time of the acquisition, we always knew they were serious about the business.
- We’ll know they’re even more serious if they buy MySQL enhancements such as Infobright, dbShards, or Schooner MySQL.
- Oracle-quality MySQL’s most obvious target is SQL Server.
- But if you’ve bought into the Windows stack, why not stay bought-in?
- MySQL vs. SQL Server competition is mainly about new applications; few users will actually switch.
- A lot of SaaS vendors use Oracle Standard Edition, and have some MySQL somewhere as well. They don’t want to pay up for Oracle Enterprise Edition or Exadata. Good MySQL could suit them.
- Mainly, I see the Short Request Processing market as being a battle between MySQL versions and NoSQL systems. (I’m a VoltDB pessimist.)
The last question was “Is there an easy shorthand to describe how Oracle DB is superior to MySQL even with these improvements?” My responses, again lightly edited, were: Read more
| Categories: Analytic technologies, Exadata, MySQL, NoSQL, Oracle, Software as a Service (SaaS) | 2 Comments |
Workday comments on its database architecture
In my discussion of Workday’s technology, I gave an estimate that Workday’s database, if relationally designed, would require “1000s” of tables. That estimate came from Workday, Inc. CTO Stan Swete, in a thoughtful email that made several points about Workday’s database strategy. Workday kindly gave me permission to quote it below.
Read more