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
Couchbase update
I checked in with James Phillips for a Couchbase update, and I understand better what’s going on. In particular:
- Give or take minor tweaks, what I wrote in my August, 2010 Couchbase updates still applies.
- Couchbase now and for the foreseeable future has one product line, called Couchbase.
- Couchbase 2.0, the first version of Couchbase (the product) to use CouchDB for persistence, has slipped …
- … because more parts of CouchDB had to be rewritten for performance than Couchbase (the company) had hoped.
- Think mid-year or so for the release of Couchbase 2.0, hopefully sooner.
- In connection with the need to rewrite parts of CouchDB, Couchbase has:
- Gotten out of the single-server CouchDB business.
- Donated its proprietary single-sever CouchDB intellectual property to the Apache Foundation.
- The 150ish new customers in 2011 Couchbase brags about are real, subscription customers.
- Couchbase has 60ish people, headed to >100 over the next few months.
| Categories: Basho and Riak, Cassandra, CouchDB, Couchbase, DataStax, Market share and customer counts, MongoDB and 10gen, NoSQL, Open source, Parallelization, Web analytics, Zynga | 3 Comments |
Splunk update
Splunk is announcing the Splunk 4.3 point release. Before discussing it, let’s recall a few things about Splunk, starting with:
- Splunk is first and foremost an analytic DBMS …
- … used to manage logs and similar multistructured data.
- Splunk’s DML (Data Manipulation Language) is based on text search, not on SQL.
- Splunk has extended its DML in natural ways (e.g., you can use it to do calculations and even some statistics).
- Splunk bundles some (very) basic, Splunk-specific business intelligence capabilities.
- The paradigmatic use of Splunk is to monitor IT operations in real time. However:
- There also are plenty of non-real-time uses for Splunk.
- Splunk is proudest of its growth in non-IT quasi-real-time uses, such as the marketing side of web operations.
As in any release, a lot of Splunk 4.3 is about “Oh, you didn’t have that before?” features and Bottleneck Whack-A-Mole performance speed-up. One performance enhancement is Bloom filters, which are a very hot topic these days. More important is a switch from Flash to HTML5, so as to accommodate mobile devices with less server-side rendering. Splunk reports that its users — especially the non-IT ones — really want to get Splunk information on the tablet devices. While this somewhat contradicts what I wrote a few days ago pooh-poohing mobile BI, let me hasten to point out:
- Splunk is used for a lot of (quasi) real-time monitoring.
- Splunk’s desktop user interfaces are, by BI standards, quite primitive.
That’s pretty much the ideal scenario for mobile BI: Timeliness matters and prettiness doesn’t.
| Categories: Business intelligence, Data models and architecture, Data warehousing, Log analysis, Specific users, Splunk, Structured documents, Web analytics | 3 Comments |
Agile predictive analytics – the heart of the matter
I’ve already suggested that several apparent issues in predictive analytic agility can be dismissed by straightforwardly applying best-of-breed technology, for example in analytic data management. At first blush, the same could be said about the actual analysis, which comprises:
- Data preparation, which is tedious unless you do a good job of automating it.
- Running the actual algorithms.
Numerous statistical software vendors (or open source projects) help you with the second part; some make strong claims in the first area as well (e.g., my clients at KXEN). Even so, large enterprises typically have statistical silos, commonly featuring expensive annual SAS licenses and seemingly slow-moving SAS programmers.
As I see it, the predictive analytics workflow goes something like this Read more
| Categories: Investment research and trading, Predictive modeling and advanced analytics, SAS Institute, Telecommunications, Web analytics | 19 Comments |
The cool aspects of Odiago WibiData
Christophe Bisciglia and Aaron Kimball have a new company.
- It’s called Odiago, and is one of my gratifyingly more numerous tiny clients.
- Odiago’s product line is called WibiData, after the justly popular We Be Sushi restaurants.
- We’ve agreed on a split exclusive de-stealthing launch. You can read about the company/founder/investor stuff on TechCrunch. But this is the place for — well, for the tech crunch.
WibiData is designed for management of, investigative analytics on, and operational analytics on consumer internet data, the main examples of which are web site traffic and personalization and their analogues for games and/or mobile devices. The core WibiData technology, built on HBase and Hadoop,* is a data management and analytic execution layer. That’s where the secret sauce resides. Also included are:
- REST APIs for interactive access.
- Import/export tools, including JDBC access.
- Management tools.
- Analytic libraries — data mining, predictive analytics, machine learning, and so on.
The whole thing is in beta, with about three (paying) beta customers.
*And Avro and so on.
The core ideas of WibiData include:
- ALL data pertaining to a single user (or mobile device) is kept in a single, possibly very long, HBase row.
- There are two primary operators in WibiData, Produce and Gather.
- Produce operates on single rows. It can operate on one row at HBase speed (milliseconds) if you need to inform an interactive user response. Or it can operate on the whole database in batch via Hadoop MapReduce.
- It is reasonable to think of Produce as mainly doing two things. One is the aforementioned serving of data out of WibiData into interactive applications. The other is scoring, classifying, recommending, etc. on individual users (i.e. rows), in line with an analytic model.
- Gather typically operates on all your rows at once, and emits suitable input for a MapReduce Reduce step. It is reasonable to think of Gather as being a key cog in the training of analytic models.
- HBase schema management is done at the WibiData system level, not directly in applications. There’s a WibiData HBase data dictionary, powered by a set of system tables, that specifies cell data types/record types and, in effect, primitive schemas.
| Categories: Data models and architecture, HBase, Hadoop, NoSQL, Odiago and WibiData, Predictive modeling and advanced analytics, Web analytics | 11 Comments |
More notes on Oracle NoSQL
A reporter asked me for some thoughts on Oracle’s new NoSQL product. For the most part, I stand by my previous comments on Oracle NoSQL. Still, NoSQL in general deserves a place in Oracle shops, so it makes sense for Oracle to try to coopt it.
Oracle’s core DBMS is not well suited to track interactions (e.g. web clicks), even in cases where it’s the choice for transactions; it’s unnecessarily heavyweight. What’s worse, using the same database to store actions and interactions can lead to serious reliability problems. If a better architecture is to dump the clicks into some NoSQL store, massage the information, and eventually put some derived data into a relational DBMS, then Oracle will naturally try to own each step of the data pipeline.
Dynamic schemas are another area of Oracle weakness, leading in some cases to outright Oracle replacements. However, pure key-value stores go too far to the opposite extreme; you should at least be able to index and retrieve data one field at a time. Based on what I’ve seen of Oracle’s marketing literature, that feature will be missing from the first release of Oracle’s NoSQL.* Until it’s in there, and until it works well, I don’t see why anybody should use Oracle’s NoSQL product.
*Frankly, that choice makes no sense to me on any level. Yet it’s the way Oracle seems to have elected to go — or, if it isn’t, then there’s somebody writing Oracle marketing collateral who’s clearly in the wrong line of work.
| Categories: NoSQL, Oracle, Web analytics | 3 Comments |
What those nested data structures are about
As I’ve noted before, the very big web companies have an issue with nested data structures. The subject came up in XLDB talks yesterday too, so my big goal for lunch was to finally understand what was being talked about. Sitting at a table full of eBay and LinkedIn folks turned out to be a good tactic.
The explanation was led by Oliver Ratzesberger, late of eBay*and progenitor of eBay’s Singularity project. In simplest terms, one event can spawn a lot of event attribute information, perhaps in the form of name-value pairs, which it then makes sense to store together in some way. The example Oliver dwelled on was that, on any given web page, there can be 100+ pieces of information to record, including:
- All 50 search results you were shown, and their positions in the search rankings.
- Every ad, image, or graphical element.
- An ID as to which test you were participating in (every page you see on eBay has some element being tested).
*Oliver is leaving eBay for a still-secret large company. I would conjecture that Michael McIntire is on the move too, either to replace Oliver or to go with him, but Oliver did a very good job of not commenting on the matter.
There are several reasons why one might wish to store this information in ways that grieve relational purists. First, reconstructing all this information via joins would be brutally expensive. What’s more, reconstructing all this information via joins could be impractical. Some comes from third party ad servers, which might not reproduce the same ads upon demand. Other is in the form of rankings, which can’t always be reliably reproduced from one query to the next. (That’s just one of several reasons text search and relational DBMS are an awkward fit.)
Also, there’s a strong dynamic schema flavor to these databases. The list of attributes for one web click might be very different in kind from the list for the next page. Forcing that kind of variability into a fixed relational schema, while theoretically possible, doesn’t necessarily make a lot of sense.
| Categories: Data models and architecture, Data warehousing, Log analysis, Web analytics, eBay | 2 Comments |
Hadoop notes
I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.
The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:
- Internet businesses.
- Traditional enterprises ‘ internet operations.
- Traditional enterprises’ other operations.
Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.
| Categories: Cloudera, Hadoop, Health care, Hortonworks, Investment research and trading, Log analysis, MapR, MapReduce, Market share and customer counts, Scientific research, Web analytics | 5 Comments |
Aster Data business trends
Last month, I reviewed with the Aster Data folks which markets they were targeting and selling into, subsequent to acquisition by their new orange overlords. The answers aren’t what they used to be. Aster no longer focuses much on what it used to call frontline (i.e., low-latency, operational) applications; those are of course a key strength for Teradata. Rather, Aster focuses on investigative analytics — they’ve long endorsed my use of the term — and on the batch run/scoring kinds of applications that inform operational systems.
| Categories: Analytic technologies, Application areas, Aster Data, Data warehousing, DataStax, Liberty and privacy, RDF and graphs, Teradata, Web analytics | 1 Comment |
Couchbase business update
I decided I needed some Couchbase drilldown, on business and technology alike, so I had solid chats with both CEO Bob Wiederhold and Chief Architect Dustin Sallings. Pretty much everything I wrote at the time Membase and CouchOne merged to form Couchbase (the company) still holds up. But I have more detail now. 
Context for any comments on customer traction includes:
- Membase went into limited production release in October, and full release in January. Similar things are true of CouchDB.
- Hence, most sales of Couchbase’s products have been made over the past 6 months.
- Couchbase (the merged product) is at this point only in a pre-production developer’s release.
- Couchbase has both a direct sales force and a classic open-source “funnel”-based online selling model. Naturally, Couchbase’s understanding of what its customers are doing is more solid with respect to the direct sales base.
- Most of Couchbase’s revenue to date seems to have come from a limited number of big-ticket “lighthouse” accounts (as opposed to, say, the larger number of smaller deals that come in through the online funnel).
That said,
- Most Membase purchases are for new applications, as opposed to memcached migrations. However, customers are the kinds of companies that probably also are using memcached elsewhere.
- Most other Membase purchases are replacements for the Membase/MySQL combination. Bob says those are easy sales with short sales cycles.
- Pure memcached support is a small but non-zero business for Couchbase, and a fine source of upsell opportunities.
- In the pipeline but not so much yet in the customer base are SaaS vendors and the like who use and may want to replace traditional DBMS such as Oracle. Other than among those, Couchbase doesn’t compete much yet with Oracle et al.
- Pure CouchDB isn’t all that much of a business, at least relative to community size, as CouchDB is a single-server product commonly used by people who are content not to pay for support.
Membase sales are concentrated in five kinds of internet-centric companies, which in declining order are: Read more
MongoDB users and use cases
I spoke with Eliot Horowitz and Max Schierson of 10gen last month about MongoDB users and use cases. The biggest clusters they came up with weren’t much over 100 nodes, but clusters an order of magnitude bigger were under development. The 100 node one we talked the most about had 33 replica sets, each with about 100 gigabytes of data, so that’s in the 3-4 terabyte range total. In general, the largest MongoDB databases are 20-30 TB; I’d guess those really do use the bulk of available disk space. Read more