Discussion of how data warehousing and analytic technologies are applied to logfile analysis. Related subjects include:
- The use of analytic technologies to study web and network event data
Business intelligence dashboards are frequently bashed. I slammed them back in 2006 and 2007. Mark Smith dropped the hammer last August. EIS, the most dashboard-like pre-1990s analytic technology, was also the most reviled. There are reasons for this disdain, but even so dashboards shouldn’t be dismissed entirely.
In essence, I’d say:
- Dashboards are overrated and oversold.
- They are useful even so.
- Their usefulness is ebbing as technology advances.
In particular: Read more
I recently proposed a 2×2 matrix of BI use cases:
- Is there an operational business process involved?
- Is there a focus on root cause analysis?
Let me now introduce another 2×2 matrix of analytic scenarios:
- Is there a compelling need for super-fresh data?
- Who’s consuming the results — humans or machines?
My point is that there are at least three different cool things people might think about when they want their analytics to be very fast:
- Fast investigative analytics — e.g., business intelligence with great query response.
- Computations on very fresh data, presented to humans — e.g. “heartbeat” graphics monitoring a network.
- Computations on very fresh data, presented back to a machine — e.g., a recommendation engine that includes makes good use of data about a user’s last few seconds of actions.
There’s also one slightly boring one that however drives a lot of important applications: Read more
|Categories: Business intelligence, Complex event processing (CEP), Games and virtual worlds, Log analysis, Predictive modeling and advanced analytics, Splunk, WibiData||4 Comments|
Informatica, Splunk, and IBM are all public companies, and correspondingly reticent to talk about product futures. Hence, anything I might suggest about product futures from any of them won’t be terribly detailed, and even the vague generalities are “the Good Lord willin’ an’ the creek don’ rise”.
Never let a rising creek overflow your safe harbor.
1. Hadoop can be an awesome ETL (Extract/Transform/Load) execution engine; it can handle huge jobs and perform a great variety of transformations. (Indeed, MapReduce was invented to run giant ETL jobs.) Thus, if one offers a development-plus-execution stack for ETL processes, it might seem appealing to make Hadoop an ETL execution option. And so:
- I’ve already posted that BI-plus-light-ETL vendors Pentaho and Datameer are using Hadoop in that way.
- Informatica will be using Hadoop as an execution option too.
Informatica told me about other interesting Hadoop-related plans as well, but I’m not sure my frieNDA allows me to mention them at all.
IBM, however, is standing aside. Specifically, IBM told me that it doesn’t see the point of doing the same thing, as its ETL engine — presumably derived from the old Ascential product line — is already parallel and performant enough.
2. Last year, I suggested that Splunk and Hadoop are competitors in managing machine-generated data. That’s still true, but Splunk is also preparing a Hadoop co-opetition strategy. To a first approximation, it’s just Hadoop import/export. However, suppose you view Splunk as offering a three-layer stack: Read more
|Categories: EAI, EII, ETL, ELT, ETLT, Hadoop, IBM and DB2, Informatica, Log analysis, MapReduce, Splunk||9 Comments|
In a short October, 2011 post about Datameer, I wrote:
Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).
That’s all still mainly true, although with the recent Datameer 2.0:
- You can run Datameer and the underlying Hadoop on a desktop or workgroup group.
- There are some infographics pretty-picture-drawing capabilities, which will surely delight those who like vector-based HTML 5 pictures of coffee cups, saucers and macaroons.
- No doubt Datameer has been generally enhanced on multiple fronts.
In essence, Datameer has two positionings.
- One is “OK, you’ve got Hadoop — now wouldn’t you like to do something useful with it?” That can include both business intelligence and ETL.
- Beyond that, Datameer founder/CEO Stefan Groschupf’s core argument is that schema-on-read is really, really useful, even at the cost of absorbing a potentially large performance hit. In other words, he’s making a case for a form of non-relational BI.
|Categories: Business intelligence, Data models and architecture, Datameer, EAI, EII, ETL, ELT, ETLT, Hadoop, Log analysis, Market share and customer counts, Web analytics||8 Comments|
I previously dropped a few hints about my clients at Metamarkets, mentioning that they:
- Have built vertical-market analytic platform technology.
- Use a lot of Hadoop.
- Throw good parties. (That’s where the background photo on my Twitter page comes from.)
But while they’re a joy to talk with, writing about Metamarkets has been frustrating, with many hours and pages of wasted of effort. Even so, I’m trying again, in a three-post series:
Much like Workday, Inc., Metamarkets is a SaaS (Software as a Service) company, with numerous tiers of servers and an affinity for doing things in RAM. That’s where most of the similarities end, however, as Metamarkets is a much smaller company than Workday, doing very different things.
Metamarkets’ business is SaaS (Software as a Service) business intelligence, on large data sets, with low latency in both senses (fresh data can be queried on, and the queries happen at RAM speed). As you might imagine, Metamarkets is used by digital marketers and other kinds of internet companies, whose data typically wants to be in the cloud anyway. Approximate metrics for Metamarkets (and it may well have exceeded these by now) include 10 customers, 100,000 queries/day, 80 billion 100-byte events/month (before summarization), 20 employees, 1 popular CEO, and a metric ton of venture capital.
To understand how Metamarkets’ technology works, it probably helps to start by realizing: Read more
Edit: Multiple errors in the post below have been corrected in a follow-on post about DataStax Enterprise and Cassandra.
My client DataStax is announcing DataStax Enterprise 2.0. The big point of the release is that there’s a bunch of stuff integrated together, including at least:
- Cassandra — the NoSQL DBMS, which DataStax sometimes calls “DataStax Server”. Edit: That’s not really a fair criticism of DataStax’s messaging.
- Hadoop MapReduce, which DataStax sometimes calls “Hadoop”. Edit: That is indeed fair.
- Sqoop — the general way to connect relational DBMS to Hadoop, which DataStax sometimes calls “RDBMS integration”.
- Solr — the search-centric Apache project, or big parts of it, which DataStax generally calls either “Solr” or “Solr compatibility”.
- log4j – an Apache project that has something or other to do with logging, or parts of it, which DataStax sometimes calls “log file integration”.
- DataStax OpsCenter — some management tools and so on around Cassandra and the rest of the product line.
DataStax stresses that all this runs on the same cluster, with the same administrative tools and so on. For example, on a single cluster:
- You can manage the interactive data for a web site.
- You can store the logs for that website.
- You can analyze all of the above in Hadoop.
|Categories: Cassandra, Clustering, DataStax, EAI, EII, ETL, ELT, ETLT, Games and virtual worlds, Hadoop, Log analysis, Market share and customer counts, NoSQL, Parallelization, Text, Web analytics||5 Comments|
The most straightforward approach to the applications business is:
- Take general-purpose technology and think through how to apply it to a specific application domain.
- Produce packaged application software accordingly.
However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:
- Analytic applications of that kind are rarely complete.
- Incomplete applications rarely sell well.
I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.
Reasons that analytic applications are commonly less complete than the transactional kind include: Read more
|Categories: Business intelligence, Business Objects, Data mart outsourcing, Investment research and trading, Log analysis, Metamarkets and Druid, Oracle, SAP AG, SAS Institute, Web analytics, WibiData||16 Comments|
I talked with the Sumo Logic folks for an hour Thursday. Highlights included:
- Sumo Logic does SaaS (Software as a Service) log management.
- Sumo Logic is text indexing/Lucene-based. Thus, it is reasonable to think of Sumo Logic as “Splunk-like”. (However, Sumo Logic seems to have a stricter security/trouble-shooting orientation than Splunk, which is trying to branch out.)
- Sumo Logic has hacked Lucene for faster indexing, and says 10-30 second latencies are typical.
- Sumo Logic’s main differentiation is automated classification of events.
- There’s some kind of streaming engine in the mix, to update counters and drive alerts.
- Sumo Logic has around 30 “customers,” free (mainly) or paying (around 5) as the case may be.
- A truly typical Sumo Logic customer has single to low double digits of gigabytes of log data per day. However, Sumo Logic seems highly confident in its ability to handle a terabyte per customer per day, give or take a factor of 2.
- When I asked about the implications of shipping that much data to a remote data center, Sumo Logic observed that log data compresses really well.
- Sumo Logic recently raised a bunch of venture capital.
- Sumo Logic’s founders are out of ArcSight, a log management company HP paid a bunch of money for.
- Sumo Logic coined a marketing term “LogReduce”, but it has nothing to do with “MapReduce”. Sumo Logic seems to find this amusing.
What interests me about Sumo Logic is that automated classification story. I thought I heard Sumo Logic say: Read more
|Categories: Log analysis, Market share and customer counts, Predictive modeling and advanced analytics, Software as a Service (SaaS), Text||6 Comments|
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|
Recently, I observed that Big Data terminology is seriously broken. It is reasonable to reduce the subject to two quasi-dimensions:
- Bigness — Volume, Velocity, size
- Structure — Variety, Variability, Complexity
- High-velocity “big data” problems are usually high-volume as well.*
- Variety, variability, and complexity all relate to the simply-structured/poly-structured distinction.
But the conflation should stop there.
*Low-volume/high-velocity problems are commonly referred to as “event processing” and/or “streaming”.
When people claim that bigness and structure are the same issue, they oversimplify into mush. So I think we need four pieces of terminology, reflective of a 2×2 matrix of possibilities. For want of better alternatives, my suggestions are:
- Relational big data is data of high volume that fits well into a relational DBMS.
- Multi-structured big data is data of high volume that doesn’t fit well into a relational DBMS. Alternative: Poly-structured big data.
- Conventional relational data is data of not-so-high volume that fits well into a relational DBMS. Alternatives: Ordinary/normal/smaller relational data.
- Smaller poly-structured data is data for which dynamic schema capabilities are important, but which doesn’t rise to “big data” volume.
|Categories: Cassandra, Data models and architecture, Data warehousing, Exadata, Facebook, Google, Hadoop, HBase, Log analysis, Market share and customer counts, MarkLogic, NewSQL, NoSQL, Oracle, Splunk, Yahoo||10 Comments|