QlikTech and QlikView
Analysis of QlikTech (now called Qlik Technologies), vendor of the memory-centric QlikView business intelligence products. Related subjects include:
Terminology: Relationship analytics
This post is part of a series on managing and analyzing graph data. Posts to date include:
- Graph data model basics
- Relationship analytics definition (this post)
- Relationship analytics applications
- Analysis of large graphs
In late 2005, I encountered a company called Cogito that was using a graphical data manager to analyze relationships. They called this “relational analytics”, which I thought was a terrible name for something that they were trying to claim should NOT be done in a relational DBMS. On the spot, I coined relationship analytics as an alternative. A business relationship ensued, which included a short white paper. Cogito didn’t do so well, however, and for a while the term “relationship analytics” faltered too. But recently it’s made a bit of a comeback, having been adopted by Objectivity, Qlik Tech, Yarcdata and others.
“Relationship analytics” is not a perfect name, both because it’s longish and because it might over-connote a social-network focus. But then, no other term would be perfect either. So we might as well stick with it.
In that case, “relationship analytics” could use an actual definition, preferably one a little heftier than just:
Analytics on graphs.
| Categories: Cogito and 7 Degrees, Objectivity and Infinite Graph, QlikTech and QlikView, RDF and graphs, Yarcdata and Cray | 6 Comments |
Many kinds of memory-centric data management
I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:
- The desire for human real-time interactive response naturally leads to keeping data in RAM.
- Many databases will be ever cheaper to put into RAM over time, thanks to Moore’s Law. (Most) traditional databases will eventually wind up in RAM.
- However, there will be exceptions, mainly on the machine-generated side. Where data creation and RAM data storage are getting cheaper at similar rates … well, the overall cost of RAM storage may not significantly decline.
Getting more specific than that is hard, however, because:
- The possibilities for in-memory data storage are as numerous and varied as those for disk.
- The individual technologies and products for in-memory storage are much less mature than those for disk.
- Solid-state options such as flash just confuse things further.
Consider, for example, some of the in-memory data management ideas kicking around. Read more
The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments
This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- (This post) Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be. Read more
Analytic trends in 2012: Q&A
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
QlikView 11 and the rise of collaborative BI
QlikView 11 came out last month. Let me start by pointing out:
- As one might expect, QlikView 11 contains fairly leading-edge stuff, but also some “better late than never” features.
- The leading-edge stuff is concentrated in the general area of “collaboration”.
- Additionally, QlikTech is always pushing the QlikView user interface ahead in various ways.
- The “Well, it’s about time!” feature list starts with the ability to load QlikView via third-party ETL tools (Informatica now, others coming).
- QlikTech is generally good at putting up pretty pictures of its product. You can find some in the “What’s New in QlikView 11″ document via a general QlikView resource page.*
- Stephen Swoyer wrote a good article summarizing QlikView 11.
*One confusing aspect to that paper: non-standard uses of the terms “analytic app” and “document”.
As QlikTech tells it, QlikView 11 adds two kinds of collaboration features:
- Integration with social media, which QlikTech calls “asynchronous integration.”
- Direct sharing of the QlikView UI, which QlikTech calls “synchronous integration.”
I’d add a third kind, because QlikView 11 also takes some baby steps toward what I regard as a key aspect of BI collaboration — the ability to define and track your own metrics. It’s way, way short of what I called for in metric flexibility in a post last year, but at least it’s a small start.
Eight kinds of analytic database (Part 1)
Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.
Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning. Read more
Updating our vendor client disclosures
Edit: This disclosure has been superseded by a March, 2012 version.
From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Included in the list below are two expired Monash Advantage members who haven’t said they will renew, as mentioned in my recent post on analyst bias. (You can probably imagine a couple of reasons for that obfuscation.)
With that said, our vendor client disclosures at this time are:
- Aster Data
- Cloudera
- CodeFutures/dbShards
- Couchbase
- EMC/Greenplum
- Endeca
- IBM/Netezza
- Infobright
- Intel
- MarkLogic
- ParAccel
- QlikTech
- salesforce.com/database.com
- SAND Technology
- SAP/Sybase
- Schooner Information Technology
- Skytide
- Splunk
- Teradata
- Vertica
The underlying technology of QlikView
QlikTech* finally decided both to become a client and, surely not coincidentally, to give me more technical detail about QlikView than it had when last we talked a couple of years ago. Indeed, I got to spend a couple of hours on the phone not just with Anthony Deighton, but also with QlikTech’s Hakan Wolge, who wrote 70-80% of the code in QlikView 1.0, and remains in effect QlikTech’s chief architect to this day.
*Or, as it now appears to be called, Qlik Technologies.
Let’s start with some quick reminders:
- QlikTech makes QlikView, a widely popular business intelligence (BI) tool suite.
- QlikView is distinguished by the flexibility of navigation through its user interface.
- To support this flexibility, QlikView preloads all data you might want to query into memory.
Let’s also dispose of one confusion right up front, namely QlikTech’s use of the word associative: Read more
| Categories: Business intelligence, Database compression, Memory-centric data management, QlikTech and QlikView | 26 Comments |
Intelligent Enterprise’s Editors’/Editor’s Choice list for 2010
As he has before, Intelligent Enterprise Editor Doug Henschen
- Personally selected annual lists of 12 “Most influential” companies and 36 “Companies to watch” in analytics- and database-related sectors.
- Made it clear that these are his personal selections.
- Nonetheless has called it an Editors’ Choice list, rather than Editor’s Choice.
(Actually, he’s really called it an “award.”)
Research agenda for 2010
As you may have noticed, I’ve been posting less research/analysis in November and December than during some other periods. In no particular order, reasons have included: Read more