I’ve talked with many companies recently that believe they are:
- Focused on building a great data management and analytic stack for log management …
- … unlike all the other companies that might be saying the same thing …
- … and certainly unlike expensive, poorly-scalable Splunk …
- … and also unlike less-focused vendors of analytic RDBMS (which are also expensive) and/or Hadoop distributions.
At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.
Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.
A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with: Read more
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
Edit: Any further anonymous comments to this post will be deleted. Signed comments are permitted as always.
Most of what I get paid for is in some form or other consulting. (The same would be true for many other analysts.) And so I can be a bit stingy with my advice toward non-clients. But my non-clients are a distinguished and powerful group, including in their number Oracle, IBM, Microsoft, and most of the BI vendors. So here’s a bit of advice for them too.
Oracle. On the plus side, you guys have been making progress against your reputation for untruthfulness. Oh, I’ve dinged you for some past slip-ups, but on the whole they’ve been no worse than other vendors.’ But recently you pulled a doozy. The analyst reports section of your website fails to distinguish between unsponsored and sponsored work.* That is a horrible ethical stumble. Fix it fast. Then put processes in place to ensure nothing that dishonest happens again for a good long time.
*Merv Adrian’s “report” listed high on that page is actually a sponsored white paper. That Merv himself screwed up by not labeling it clearly as such in no way exonerates Oracle. Besides, I’m sure Merv won’t soon repeat the error — but for Oracle, this represents a whole pattern of behavior.
Oracle. And while I’m at it, outright dishonesty isn’t your only unnecessary credibility problem. You’re also playing too many games in analyst relations.
HP. Neoview will never succeed. Admit it to yourselves. Go buy something that can. Read more
|Categories: Actian and Ingres, Business intelligence, Data warehouse appliances, Data warehousing, Exadata, HP and Neoview, Information Builders, Kalido, MarkLogic, NoSQL, Objectivity and Infinite Graph, Oracle, SenSage, Tableau Software||46 Comments|
|Categories: Analytic technologies, Aster Data, Cloudera, Data warehousing, Google, Hadoop, MapReduce, SenSage, Splunk||8 Comments|
Information preservation* DBMS vendor Clearpace officially changed its name to RainStor this week. RainStor is also relocating its CEO John Bantleman and more generally its headquarters to San Francisco. This all led to a visit with John and his colleague Ramon Chen, highlights of which included: Read more
|Categories: Archiving and information preservation, Market share and customer counts, Oracle, Rainstor, SenSage, Telecommunications||1 Comment|
I visited with SenSage on my two most recent trips to San Francisco. Both visits were, through no fault of SenSage’s, hasty. Still, I think I have enough of a handle on SenSage basics to be worth writing up.
General SenSage highlights include: