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
Perhaps we should remind ourselves of the many ways data models can be caused to churn. Here are some examples that are top-of-mind for me. They do overlap a lot — and the whole discussion overlaps with my post about schema complexity last January, and more generally with what I’ve written about dynamic schemas for the past several years..
Just to confuse things further — some of these examples show the importance of RDBMS, while others highlight the relational model’s limitations.
The old standbys
Product and service changes. Simple changes to your product line many not require any changes to the databases recording their production and sale. More complex product changes, however, probably will.
A big help in MCI’s rise in the 1980s was its new Friends and Family service offering. AT&T couldn’t respond quickly, because it couldn’t get the programming done, where by “programming” I mainly mean database integration and design. If all that was before your time, this link seems like a fairly contemporaneous case study.
Organizational changes. A common source of hassle, especially around databases that support business intelligence or planning/budgeting, is organizational change. Kalido’s whole business was based on accommodating that, last I checked, as were a lot of BI consultants’. Read more
|Categories: Data warehousing, Derived data, Kalido, Log analysis, Software as a Service (SaaS), Specific users, Text, Web analytics||2 Comments|
I talk with a lot of companies, and repeatedly hear some of the same application themes. This post is my attempt to collect some of those ideas in one place.
1. So far, the buzzword of the year is “real-time analytics”, generally with “operational” or “big data” included as well. I hear variants of that positioning from NewSQL vendors (e.g. MemSQL), NoSQL vendors (e.g. AeroSpike), BI stack vendors (e.g. Platfora), application-stack vendors (e.g. WibiData), log analysis vendors (led by Splunk), data management vendors (e.g. Cloudera), and of course the CEP industry.
Yeah, yeah, I know — not all the named companies are in exactly the right market category. But that’s hard to avoid.
Why this gold rush? On the demand side, there’s a real or imagined need for speed. On the supply side, I’d say:
- There are vast numbers of companies offering data-management-related technology. They need ways to differentiate.
- Doing analytics at short-request speeds is an obvious data-management-related challenge, and not yet comprehensively addressed.
2. More generally, most of the applications I hear about are analytic, or have a strong analytic aspect. The three biggest areas — and these overlap — are:
- Customer interaction
- Network and sensor monitoring
- Game and mobile application back-ends
Also arising fairly frequently are:
- Algorithmic trading
- Risk measurement
- Law enforcement/national security
- Stakeholder-facing analytics
I’m hearing less about quality, defect tracking, and equipment maintenance than I used to, but those application areas have anyway been ebbing and flowing for decades.
The third of my three MySQL-oriented clients I alluded to yesterday is MemSQL. When I wrote about MemSQL last June, the product was an in-memory single-server MySQL workalike. Now scale-out has been added, with general availability today.
MemSQL’s flagship reference is Zynga, across 100s of servers. Beyond that, the company claims (to quote a late draft of the press release):
Enterprises are already using distributed MemSQL in production for operational analytics, network security, real-time recommendations, and risk management.
All four of those use cases fit MemSQL’s positioning in “real-time analytics”. Besides Zynga, MemSQL cites penetration into traditional low-latency markets — financial services (various subsectors) and ad-tech.
Highlights of MemSQL’s new distributed architecture start: Read more
|Categories: Clustering, Database compression, Emulation, transparency, portability, Games and virtual worlds, Investment research and trading, Log analysis, MemSQL, MySQL, NewSQL, Transparent sharding, Zynga||6 Comments|
In typical debates, the extremists on both sides are wrong. “SQL vs. NoSQL” is an example of that rule. For many traditional categories of database or application, it is reasonable to say:
- Relational databases are usually still a good default assumption …
- … but increasingly often, the default should be overridden with a more useful alternative.
Reasons to abandon SQL in any given area usually start:
- Creating a traditional relational schema is possible …
- … but it’s tedious or difficult …
- … especially since schema design is supposed to be done before you start coding.
Some would further say that NoSQL is cheaper, scales better, is cooler or whatever, but given the range of NewSQL alternatives, those claims are often overstated.
Sectors where these reasons kick in include but are not limited to: Read more
|Categories: Health care, Investment research and trading, Log analysis, NewSQL, NoSQL, Web analytics||8 Comments|
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|