Oracle
Analysis of software titan Oracle and its efforts in database management, analytics, and middleware. Related subjects include:
- Oracle TimesTen
- (in The Monash Report)Operational and strategic issues for Oracle
- (in Software Memories) Historical notes on Oracle
- Most of what’s written about in this blog
Big Data hype?
A reporter wrote in to ask whether investor interest in “Big Data” was justified or hype. (More precisely, that’s how I reinterpreted his questions. 
) His examples were Splunk’s IPO, Teradata’s stock price increase, and Birst’s financing. In a nutshell:
- My comments, lightly edited, are in plain text below.
- Further thoughts are in italics.
- Of course I also linked him to my post “Big Data” has jumped the shark.
- Overall, my responses boil down to “Of course there’s some hype.”
1. A great example of hype is that anybody is calling Birst a “Big Data” or “Big Data analytics” company. If anything, Birst is a “little data” analytics company that claims, as a differentiating feature, that it can handle ordinary-sized data sets as well. Read more
| Categories: Business intelligence, Data warehousing, IBM and DB2, Microsoft and SQL*Server, Oracle, Splunk | 14 Comments |
Many kinds of memory-centric data management
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more
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 |
Juggling analytic databases
I’d like to survey a few related ideas:
- Enterprises should each have a variety of different analytic data stores.
- Vendors — especially but not only IBM and Teradata — are acknowledging and marketing around the point that enterprises should each have a number of different analytic data stores.
- In addition to having multiple analytic data management technology stacks, it is also desirable to have an agile way to spin out multiple virtual or physical relational data marts using a single RDBMS. Vendors are addressing that need.
- Some observers think that the real essence of analytic data management will be in data integration, not the actual data management.
Here goes. Read more
The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- (This post) Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be. Read more
Business intelligence industry trends
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- (This post) Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
Besides company-specific comments, the 2011/2012 Gartner Magic Quadrant for Business Intelligence (BI) Platforms offered observations on overall BI trends in a “Market Overview” section. I have mixed feelings about Gartner’s list. In particular:
- Not inconsistently with my comments on departmental analytics, Gartner sees actual BI business users as favoring ease of getting the job done, while IT departments are more concerned about full feature sets, integration, corporate standards, and license costs.
- However, Gartner says as a separate point that all kinds of users want to relieve some of the complexity of BI, and really of analytics in general. I agree, but don’t think Gartner did a great job in outlining how this complexity reduction could really work.
- Gartner is bullish on mobile business intelligence, but doesn’t really contradict my more skeptical take. Even as it confesses that mobile BI use cases are somewhat thin (my word, not Gartner’s, and no pun intended), it sees mobile BI rapidly becoming mainstream technology.
- Gartner makes a distinction between “data discovery” tools and “enterprise BI” platforms. By “data discovery” I think Gartner means what I’d call the “pattern discovery” focus of investigative analytics. Anyhow, it seems that Gartner:
- Sees users as being confused about how the traditional pattern-monitoring kinds of BI fit with the newer emphasis on investigative analytics, and …
- … shares that confusion itself.
- Gartner observes that “Most BI platforms are deployed as systems of performance measurement, not for decision support.” It evidently sees this as a bad tendency, which is thankfully changing. Automated decisioning is part of the fix Gartner sees, along with collaboration. While I agree on both counts, Gartner oddly doesn’t also connect this to the general rise of investigative analytics.
- Gartner also had a catch-all trend of “new use cases”, listing some examples, but also sort of confessing it wasn’t doing a great job of articulating the point. I think that part of the difficulty is contortions as to what is or isn’t BI; Gartner seems to run into expositional difficulties whenever it touches on the core point that analytics isn’t all about performance-monitoring BI. Another problem is that Gartner doesn’t seem to have really thought through what does and doesn’t work in the area of analytic applications.
Here’s the forest that I suspect Gartner is missing for the trees:
- Even though all-in-one enterprise BI platforms are great at getting data to a multitude of endpoints …
- … and even though the number of endpoints for data are increasing (more users, more devices) …
- … all-in-one enterprise BI platforms fall short in helping the data be used once it arrives …
- … and all-in-one enterprise BI platform vendors will find it hard to catch up with other vendors’ data-use capabilities.
| Categories: Business intelligence, Business Objects, IBM and DB2, Microsoft and SQL*Server, MicroStrategy, Oracle, SAP AG | 10 Comments |
Applications of an analytic kind
The most straightforward approach to the applications business is:
- Take general-purpose technology and think through how to apply it to a specific application domain.
- Produce packaged application software accordingly.
However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:
- Analytic applications of that kind are rarely complete.
- Incomplete applications rarely sell well.
I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.
Reasons that analytic applications are commonly less complete than the transactional kind include: Read more
| Categories: Business intelligence, Business Objects, Data mart outsourcing, Investment research and trading, Log analysis, Oracle, SAP AG, SAS Institute, Web analytics, WibiData | 13 Comments |
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Microsoft SQL Server 2012 and enterprise database choices in general
Microsoft is launching SQL Server 2012 on March 7. An IM chat with a reporter resulted, and went something like this.
Reporter: [Care to comment]?
CAM: SQL Server is an adequate product if you don’t mind being locked into the Microsoft stack. For example, the ColumnStore feature is very partial, given that it can’t be updated; but Oracle doesn’t have columnar storage at all.
Reporter: Is the lock-in overall worse than IBM DB2, Oracle?
CAM: Microsoft locks you into an operating system, so yes.
Reporter: Is this release something larger Oracle or IBM shops could consider as a lower-cost alternative a co-habitation scenario, in the event they’re mulling whether to buy more Oracle or IBM licenses?
CAM: If they have a strong Microsoft-stack investment already, sure. Otherwise, why?
Reporter: [How about] just cost?
CAM: DB2 works just as well to keep Oracle honest as SQL Server does, and without a major operating system commitment. For analytic databases you want an analytic DBMS or appliance anyway.
Best is to have one major vendor of OTLP/general-purpose DBMS, a web DBMS, a DBMS for disposable projects (that may be the same as one of the first two), plus however many different analytic data stores you need to get the job done.
By “web DBMS” I mean MySQL, NewSQL, or NoSQL. Actually, you might need more than one product in that area.
| Categories: Data warehousing, IBM and DB2, Microsoft and SQL*Server, Mid-range, MySQL, NoSQL, Oracle | 7 Comments |
Notes on the Oracle Big Data Appliance
Oracle announced its Big Data Appliance. Specs may be found in the Oracle Big Data Appliance press release. Beyond that:
- The most important software on the Oracle Big Data Appliance is a full set of Cloudera Enterprise code. Oracle will do Tier 1 Cloudera/Hadoop support, while Cloudera handles Tiers 2 and 3.
- The key spec ratios are 1 core/4 GB RAM/3 TB raw disk. That’s reasonably in line with Cloudera figures I published in June, 2010.
- This is really Oracle’s multi-structured big data appliance. Oracle’s relational big data appliance is Exadata, which has been out for years and has comparable capacity to Oracle’s new “Big Data Appliance.” (Chris Preimesberger made a similar point.)
- The Oracle Big Data Appliance list price is $450,000 for 18 12-core servers, plus $54,000/year maintenance.
- That’s around $25,000 per server (and associated storage).
- That’s also around $2,000/core.
- That’s also around $500/TB of spinning disk, before compression.
- None of those per-unit figures sounds ridiculous …
- … but because of Oracle’s appliance configuration there’s indeed a hefty minimum initial purchase.