Discussion of how data warehousing and analytic technologies are applied to clickstream analysis and other web analytics challenges. Related subjects include:
“Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.
1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:
- General streaming. Some of my posts on that subject are linked at the bottom of my August post on Flink.
- Low-latency ingest of data into structures from which it can be immediately analyzed. That helps drive the (re)integration of operational data stores, analytic data stores, and other analytic support — e.g. via Spark.
- Business intelligence that can be used quickly enough. This is a major ongoing challenge. My clients at Zoomdata may be thinking about this area more clearly than most, but even they are still in the early stages of providing what users need.
- Advanced analytics that can be done quickly enough. Answers there may come through developments in anomaly management, but that area is still in its super-early days.
- Alerting, which has been under-addressed for decades. Perhaps the anomaly management vendors will finally solve it.
|Categories: Business intelligence, Databricks, Spark and BDAS, In-memory DBMS, Investment research and trading, Log analysis, Streaming and complex event processing (CEP), Text, Web analytics, Zoomdata||2 Comments|
Then felt I like some watcher of the skies
When a new planet swims into his ken
— John Keats, “On First Looking Into Chapman’s Homer”
1. In June I wrote about why anomaly management is hard. Well, not only is it hard to do; it’s hard to talk about as well. One reason, I think, is that it’s hard to define what an anomaly is. And that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all?
Artificial intelligence is famously hard to define for similar reasons.
“Anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. But naming aside, the actual subject matter is important.
2. Anomaly analysis is clearly at the heart of several sectors, including:
- IT operations
- Factory and other physical-plant operations
Each of those areas features one or both of the frameworks:
- Surprises are likely to be bad.
- Coincidences are likely to be suspicious.
So if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look.
3. The “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. Already in the 1970s, Walmart observed that red clothing sold particularly well in Omaha, while orange flew off the shelves in Syracuse. And so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. They also noticed that fancy dresses for little girls sold especially well in Hispanic communities … specifically for girls at the age of First Communion.
|Categories: Business intelligence, Log analysis, Predictive modeling and advanced analytics, Web analytics||1 Comment|
1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:
- The Amazon cloud, Microsoft Azure, and their competitors, aka public cloud.
- Software as a service, aka SaaS.
- Co-location in off-premises data centers, aka colo.
- On-premises clusters (truly on-prem or colo as the case may be) designed to run a broad variety of applications, aka private cloud.
Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.
This is a good example of Monash’s Laws of Commercial Semantics.
2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.
This fact now seems to be widely understood.
I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:
- Yes, interactive computer response is crucial.
- Into the 1980s, many apps were batch-only. Demand for such apps dried up.
- Business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic RDBMS.
- Theoretical arguments about “true” real-time vs. near-real-time are often pointless.
- What matters in most cases is human users’ perceptions of speed.
- Most of the exceptions to that rule occur when machines race other machines, for example in automated bidding (high frequency trading or otherwise) or in network security.
A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:
- It respects the obvious point that different use cases require different levels of data freshness.
- It cautions against people who think they need fresh information but aren’t in a position to use it. (Such users have driven much bogus “real-time” demand in the past.)
- It covers cases of both human and automated decision-making.
Straightforward applications of this principle include: Read more
Databricks CEO Ali Ghodsi checked in because he disagreed with part of my recent post about Databricks. Ali’s take on Databricks’ position in the Spark world includes:
- What I called Databricks’ “secondary business” of “licensing stuff to Spark distributors” was really about second/third tier support. Fair enough. But distributors of stacks including Spark, for whatever combination of on-premise and cloud as the case may be, may in many cases be viewed as competitors to Databricks cloud-only service. So why should Databricks help them?
- Databricks’ investment in Spark Summit and similar evangelism is larger than I realized.
- Ali suggests that the fraction of Databricks’ engineering devoted to open source Spark is greater than I understood during my recent visit.
Ali also walked me through customer use cases and adoption in wonderful detail. In general:
- A large majority of Databricks customers have machine learning use cases.
- Predicting and preventing user/customer churn is a huge issue across multiple market sectors.
The story on those sectors, per Ali, is: Read more
Five years ago, in a taxonomy of analytic business benefits, I wrote:
A large fraction of all analytic efforts ultimately serve one or more of three purposes:
- Problem and anomaly detection and diagnosis
- Planning and optimization
That continues to be true today. Now let’s add a bit of spin.
1. A large fraction of analytics is adversarial. In particular: Read more
|Categories: Business intelligence, Investment research and trading, Log analysis, Predictive modeling and advanced analytics, RDF and graphs, Surveillance and privacy, Web analytics||3 Comments|
Numerous tussles fit the template:
- A government wants access to data contained in one or more devices (mobile/personal or server as the case may be).
- The computer’s manufacturer or operator doesn’t want to provide it, for reasons including:
- That’s what customers prefer.
- That’s what other governments require.
- Being pro-liberty is the right and moral choice. (Yes, right and wrong do sometimes actually come into play. )
As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more
|Categories: Amazon and its cloud, Google, Microsoft and SQL*Server, Surveillance and privacy, Web analytics||2 Comments|
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs — start: Read more
|Categories: Cache, Clustering, Couchbase, Data models and architecture, Databricks, Spark and BDAS, Exadata, Hadoop, MarkLogic, MongoDB, MySQL, NoSQL, Open source, Schema on need, Structured documents, Web analytics||1 Comment|
- My client Rocana is the renamed ScalingData, where Rocana is meant to signify ROot Cause ANAlysis.
- Rocana was founded by Omer Trajman, who I’ve referenced numerous times in the past, and who I gather is a former boss of …
- … cofounder Eric Sammer.
- Rocana recently told me it had 35 people.
- Rocana has a very small number of quite large customers.
Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:
- Actual operations — figuring out exactly what isn’t working, ASAP.
Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:
- The sort of network data you’d generally think of — “everything” except packet-inspection stuff.
- Firewall output.
- Database server logs.
- Point-of-sale data (at a retailer).
- “Application data”, whatever that means. (Edit: See Tom Yates’ clarifying comment below.)
|Categories: Business intelligence, Hadoop, Kafka and Confluent, Log analysis, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, Pricing, Rocana, Splunk, Web analytics||1 Comment|
A lot of what I hear and talk about boils down to “data is a mess”. Below is a very partial list of examples.
To a first approximation, one would expect operational data to be rather clean. After all, it drives and/or records business transactions. So if something goes awry, the result can be lost money, disappointed customers, or worse, and those are outcomes to be strenuously avoided. Up to a point, that’s indeed true, at least at businesses large enough to be properly automated. (Unlike, for example — — mine.)
Even so, operational data has some canonical problems. First, it could be inaccurate; somebody can just misspell or otherwise botch an entry. Further, there are multiple ways data can be unreachable, typically because it’s:
- Inconsistent, in which case humans might not know how to look it up and database JOINs might fail.
- Unintegrated, in which case one application might not be able to use data that another happily maintains. (This is the classic data silo problem.)
Inconsistency can take multiple forms, including: Read more