Web analytics
Discussion of how data warehousing and analytic technologies are applied to clickstream analysis and other web analytics challenges. Related subjects include:
- The use of analytic technologies for logfile analysis
- (in Text Technologies) Online marketing
Big Data is Watching You!
There’s a boom in large-scale analytics. The subjects of this analysis may be categorized as:
- People
- Financial trades
- Electronic networks
- Everything else
The most varied, interesting, and valuable of those four categories is the first one.
| Categories: Analytic technologies, Aster Data, Data warehousing, Investment research and trading, Log analysis, MapReduce, RDF and graphs, Specific users, Telecommunications, Web analytics | 3 Comments |
Why you should go to XLDB4
Scientific data commonly:
- Comes in large volumes
- Is machine-generated
- Is augmented by synthetic and/or derived data
- Has a spatial and/or temporal structure
In those respects, it is akin to some of the hottest areas for big data analytics, including:
- Investment trade data – big, partly machine generated, augmented (often), temporal
- Web/network log data – big, machine-generated, post-processed into derived form, temporal
- Marketing analytic data – big, post-processed into derived form
- Genomic data
So when Jacek Becla started the XLDB conferences on the premise that scientific and big data analytic challenges have a lot in common, he had a point. There are several tough database problems that the science-focused folks have taken the leading in thinking about, but which are soon going to matter to the commercial world as well. And that’s one of two big reasons why you should consider participating in XLDB4, October 6-7, at the SLAC facility in Menlo Park, CA, as an attendee, sponsor, or both.
The other big reason is that it is important for the world that XLDB succeed. Read more
| Categories: Investment research and trading, Log analysis, Scientific research, Web analytics | Leave a Comment |
Cloudera Enterprise and Hadoop evolution
I talked with Cloudera a couple of weeks ago in connection with the impending release of Cloudera Enterprise. I’d say: Read more
The most important part of the “social graph” is neither social nor a graph
“Social graph” is a highly misleading term, and so is “social network analysis.” By this I mean:
There’s something akin to “social graphs” and “social network analysis” that is more or less worthy of all the current hype – but graphs and network analysis are only a minor part of the whole story.
In particular, the most important parts of the Facebook “social graph” are neither social nor a graph. Rather, what’s really important is an aggregate Profile of Revealed Preferences, of which person-to-person connections or other things best modeled by a graph play only a small part.
| Categories: Analytic technologies, Facebook, Games and virtual worlds, Liberty and privacy, RDF and graphs, Web analytics | 7 Comments |
Notes on SciDB and scientific data management
I firmly believe that, as a community, we should look for ways to support scientific data management and related analytics. That’s why, for example, I went to XLDB3 in Lyon, France at my own expense. Eight months ago, I wrote about issues in scientific data management. Here’s some of what has transpired since then.
The main new activity I know of has been in the open source SciDB project. Read more
| Categories: Analytic technologies, Data warehousing, GIS and geospatial, Microsoft and SQL*Server, SciDB, Scientific research, Web analytics, eBay | 3 Comments |
Truviso evidently reinvents itself
When Aleri bought Coral8 last year, I wrote that the independent CEP (Complex Event Processing) vendors were floundering. Aleri quickly threw in the towel and sold out to Sybase, which hardly changed my opinion. StreamBase actually is persevering, but not with any kind of breakout success. Big vendors, such as Microsoft and IBM, have at least some aspirations of eventually filling the gap.
Meanwhile, Truviso — which never got much market traction in the first place — was in hiding; Roman Bukary never did keep his promise to brief me on the company’s new and improved strategy. Then Truviso had yet another management change, amidst rumors that it was repositioning away from CEP. As per a press release Truviso emailed today, that’s now official, with Truviso’s main business being something to do with web analytics.
| Categories: Complex event processing (CEP), Truviso, Web analytics | 7 Comments |
Vertica update
Last month, Vertica’s CEO Ralph Breslauer quit,* and Vertica made it sound like there would be a new CEO late in April. And indeed, as of April 29, there was. He’s a guy I’ve never heard of before named Chris Lynch, apparently quite the sales machine builder. The most substance I’ve found is a pair of Mass High Tech articles — the latter exceedingly typo-ridden — to the general effect that:
- Vertica plans to build a massive, world-conquering sales force.
- If Vertica dips back into negative cash flow to do that and has to raise more venture capital, so be it.
- “Triple-digit” revenue growth is expected for this year.
| Categories: Analytic technologies, Columnar database management, Data warehousing, Games and virtual worlds, Market share, Specific users, Vertica Systems, Web analytics | 1 Comment |
Examples of machine-generated data
Not long ago I pointed out that much future Big Data growth will be in the area of machine-generated data, examples of which include: Read more
| Categories: Analytic technologies, Data warehousing, Games and virtual worlds, Investment research and trading, Log analysis, Oracle, Telecommunications, Web analytics | 11 Comments |
Notes on the evolution of OLTP database management systems
The past few years have seen a spate of startups in the analytic DBMS business. Netezza, Vertica, Greenplum, Aster Data and others are all reasonably prosperous, alongside older specialty product vendors Teradata and Sybase (the Sybase IQ part). OLTP (OnLine Transaction Processing) and general purpose DBMS startups, however, have not yet done as well, with such success as there has been (MySQL, Intersystems Cache’, solidDB’s exit, etc.) generally accruing to products that originated in the 20th Century.
Nonetheless, OLTP/general-purpose data management startup activity has recently picked up, targeting what I see as some very real opportunities and needs. So as a jumping-off point for further writing, I thought it might be interesting to collect a few observations about the market in one place. These include:
- Big-brand OLTP/general-purpose DBMS have more “stickiness” than analytic DBMS.
- By number, most of an enterprise’s OLTP/general-purpose databases are low-volume and low-value.
- Most interesting new OLTP/general-purpose data management products are either MySQL-based or NoSQL.
- It’s not yet clear whether MySQL will prevail over MySQL forks, or vice-versa, or whether they will co-exist.
- The era of silicon-centric relational DBMS is coming.
- The emphasis on scale-out and reducing the cost of joins spans the NoSQL and SQL-based worlds.
- Users’ instance on “free” could be a major problem for OLTP DBMS innovation.
I shall explain. Read more
The retention of everything
I’d like to reemphasize a point I’ve been making for a while about data retention: Read more