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
Three broad categories of data
People often try to draw a distinction between:
- Traditional data of the sort that’s stored in relational databases, aka “structured.”
- Everything else, aka “unstructured” or “semi-structured” or “complex.”
There are plenty of problems with these formulations, not the least of which is that the supposedly “unstructured” data is the kind that actually tends to have interesting internal structures. But of the many reasons why these distinctions don’t tend to work very well, I think the most important one is that:
Databases shouldn’t be divided into just two categories. Even as a rough-cut approximation, they should be divided into three, namely:
- Human/Tabular data –i.e., human-generated data that fits well into relational tables or arrays
- Human/Nontabular data — i.e., all other data generated by humans
- Machine-Generated data
Even that trichotomy is grossly oversimplified, for reasons such as:
- These categories overlap.
- There are kinds of data that get into fuzzy border zones.
- Not all data in each category has all the same properties.
But at least as a starting point, I think this basic categorization has some value. Read more
| Categories: Database diversity, Investment research and trading, Log analysis, Telecommunications, Web analytics | 6 Comments |
A framework for thinking about data warehouse growth
There are only three ways that the amount of data stored in data warehouses can grow:
- The same kinds of data are stored as before, with more being added over time.
- The same kinds of data are stored as before, but in more detail.
- New kinds of data are stored.
| Categories: Analytic technologies, Application areas, Data warehousing, Investment research and trading, Log analysis, Solid-state memory, Storage, Telecommunications, Text, Web analytics | 7 Comments |
Webinar on MapReduce for complex analytics (Thursday, December 3, 10 am and 2 pm Eastern)
The second in my two-webinar series for Aster Data will occur tomorrow, twice (both live), at 10 am and 2 pm Eastern time. The other presenters will be Jonathan Goldman, who was Principal Scientist at LinkedIn but now has joined Aster himself, and Steve Wooledge of Aster (playing host). Key links are:
- Registration for tomorrow’s webinars
- Replay of the first webinar
- My slides from the first webinar
The main subjects of the webinar will be:
- Some review of material from the first webinar (all three presenters)
- Discussion of how MapReduce can help with three kinds of analytics:
- Pattern matching (Jonathan will give detail)
- Number-crunching (I’ll cover that, and it will be short)
- Graph analytics (I haven’t written the slides yet, but my starting point will be some of the relationship analytics ideas we discussed in August)
Arguably, aspects of data transformation fit into each of those three categories, which may help explain why data transformation has been so prominent among the early applications of MapReduce.
As you can see from Aster’s title for the webinar (which they picked while I was on vacation), at least their portion will be focused on customer analytics, e.g. web analytics.
| Categories: Analytic technologies, Aster Data, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, MapReduce, RDF and graphs, Web analytics | 2 Comments |
Boston Big Data Summit keynote outline
Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.
Three big myths about MapReduce
Once again, I find myself writing and talking a lot about MapReduce. But I suspect that MapReduce-related conversations would go better if we overcame three fairly common MapReduce myths:
- MapReduce is something very new
- MapReduce involves strict adherence to the Map-Reduce programming paradigm
- MapReduce is a single technology
| Categories: Analytic technologies, Aster Data, Cloudera, Data warehousing, Google, Greenplum, Hadoop, Log analysis, MapReduce, Michael Stonebraker, Parallelization, Web analytics | 11 Comments |
Technical introduction to Splunk
As noted in my other introductory post, Splunk sells software called Splunk, which is used for log analysis. These can be logs of various kinds, but for the purpose of understanding Splunk technology, it’s probably OK to assume they’re clickstream/network event logs. In addition, Splunk seems to have some aspirations of having its software used for general schema-free analytics, but that’s in early days at best.
Splunk’s core technology indexes text and XML files or streams, especially log files. Technical highlights of that part include: Read more
| Categories: Analytic technologies, Log analysis, MapReduce, Native XML, Splunk, Text, Web analytics | 9 Comments |
General introduction to Splunk
I dropped by log analysis software vendor Splunk a few weeks ago for a chat with Marketing VP Steve Sommer (who some you may know from Cognos and/or Informix), Product Management VP Christina Noren, and above all co-founder/CTO Erik Swan. Splunk turns out to be a pretty interesting company, from both business and technical standpoints. For one thing, Splunk seems highly regarded by most people I mention it to.
Splunk’s technical stories include:
- Text search over log files.
- Business intelligence over text search. (That part sounds a lot like Attivio.)
- MapReduce with schema flexibility and smart multi-stage execution plans. (That part sounds a lot like Aster Data.)
More on those in a separate post.
Less technical Splunk highlights include: Read more
| Categories: Analytic technologies, Fox and MySpace, Investment research and trading, Log analysis, Splunk, Telecommunications, Text, Web analytics | 1 Comment |
Infobright notes
I had lunch w/ Bob Zurek and Susan Davis of Infobright today. This wasn’t primarily a briefing, but a few takeaways are:
- Infobright now has >100 paying customers.
- Typical database size is from the low 100s of gigabytes to the low single-digit number of terabytes.
- Agile development is at or approaching two-week release cycles.
- Like Kickfire, Infobright has a multi-year deal with MySQL that insulates it against many potential Oracle/MySQL shenanigans.
- From an industry perspective, Infobright’s customer base sounds a lot like other vendors’:
- Data mart outsourcing/online analytics
- Log files for websites
- Telecommunications
- Financial services
- OEM, especially in the markets cited above
- “Hey, we’re beginning to see the occasional energy deal”
- A few random others
- Infobright is seeing some household-name customers, who surely have big-name analytic DBMS products, but who also have a policy that open source is the default choice, and if open source can get the job done then the favorite closed-source choices aren’t used.
- Infobright has the usual open-source community story — lots of involvement and engagement in the forums, but contributions are limited mainly to connectivity, utility scripts, etc. (Maybe some national language translation too; I’m not sure.)
How 30+ enterprises are using Hadoop
MapReduce is definitely gaining traction, especially but by no means only in the form of Hadoop. In the aftermath of Hadoop World, Jeff Hammerbacher of Cloudera walked me quickly through 25 customers he pulled from Cloudera’s files. Facts and metrics ranged widely, of course:
- Some are in heavy production with Hadoop, and closely engaged with Cloudera. Others are active Hadoop users but are very secretive. Yet others signed up for initial Hadoop training last week.
- Some have Hadoop clusters in the thousands of nodes. Many have Hadoop clusters in the 50-100 node range. Others are just prototyping Hadoop use. And one seems to be “OEMing” a small Hadoop cluster in each piece of equipment sold.
- Many export data from Hadoop to a relational DBMS; many others just leave it in HDFS (Hadoop Distributed File System), e.g. with Hive as the query language, or in exactly one case Jaql.
- Some are household names, in web businesses or otherwise. Others seem to be pretty obscure.
- Industries include financial services, telecom (Asia only, and quite new), bioinformatics (and other research), intelligence, and lots of web and/or advertising/media.
- Application areas mentioned — and these overlap in some cases — include:
- Log and/or clickstream analysis of various kinds
- Marketing analytics
- Machine learning and/or sophisticated data mining
- Image processing
- Processing of XML messages
- Web crawling and/or text processing
- General archiving, including of relational/tabular data, e.g. for compliance
Yahoo wants to do decapetabyte-scale data warehousing in Hadoop
My old client Mark Tsimelzon moved over to Yahoo after Coral8 was acquired, and I caught up with him last month. He turns out to be running development for a significant portion of Yahoo’s Hadoop effort — everything other than HDFS (Hadoop Distributed File System). Yahoo evidently plans to, within a year or so, get Hadoop to the point that it is managing 10s of petabytes of data for Yahoo, with reasonable data warehousing functionality.
Highlights of our visit included:
- There are dozens of people at Yahoo doing Hadoop development that will wind up getting open sourced. (Full-time or close to it.) In particular, everything Mark’s team does goes to open source.
- Yahoo is moving as much of its analytics to Hadoop as possible. Much of this is being moved away from Oracle and from Yahoo’s own Everest.
- A column store is being put on top of HDFS, based on Yahoo technology. Columns will be striped across nodes. Perhaps that’s why the effort is called Project Zebra.
- Mark believes that in a year Hadoop will be much further along in meeting traditional data warehousing requirements, in areas such as:
- Metadata
- SLAs/high availability/other workload management
- Data retention policies
- Security/privacy*
- Yahoo views the time-to-market benefits of Hadoop as being more important than TCO.