QlikTech and QlikView
Analysis of QlikTech (now called Qlik Technologies), vendor of the memory-centric QlikView business intelligence products. Related subjects include:
0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.
1. In line with that theme:
- Relational query languages, at their core, subset data. Yes, they all also do arithmetic, and many do more math or other processing than just that. But it all starts with the set theory.
- Underscoring the power of this approach, other data architectures over which analytics is done usually wind up with SQL or “SQL-like” language access as well.
2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.
*I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.
3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:
- Divide your data into clusters.
- Model each cluster separately.
That continues to be tough work. Attempts to productize shortcuts have not caught fire.
|Categories: Business intelligence, Data warehousing, Databricks, Spark and BDAS, Google, Log analysis, Memory-centric data management, Predictive modeling and advanced analytics, QlikTech and QlikView, Tableau Software, Web analytics||Leave a Comment|
Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:
- Both kinds of products query and aggregate data.
- Both are offered by big “enterprise standard” behemoth companies and also by younger, nimbler specialists.
- You really, really, really don’t want your customer data to leak via a security breach in either kind of product.
That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:
- BI is less mission-critical than some other database uses.
- BI has done a lot less than DBMS to deal with multi-structured data.
- Scalability demands on BI are less than those on DBMS — indeed, they’re the ones that are left over after the DBMS has done its data crunching first.
And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.
Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more
|Categories: Business intelligence, Buying processes, ClearStory Data, Data mart outsourcing, Pricing, QlikTech and QlikView, Rocana, Tableau Software||Leave a Comment|
Let’s start with some terminology biases:
- I dislike the term “big data” but like the Vs that define it — Volume, Velocity, Variety and Variability.
- Though I think it’s silly, I understand why BI innovators flee from the term “business intelligence” (they’re afraid of not sounding new).
So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.
It turns out that:
- Zoomdata does its processing server-side, which allows for load-balancing and scale-out. Scale-out and claims of great query speed are relevant when data is of high volume.
- Zoomdata depends heavily on Spark.
- Zoomdata’s UI assumes data can be a mix of historical and streaming, and that if looking at streaming data you might want to also check history. This addresses velocity.
- Zoomdata assumes data can be in a variety of data stores, including:
- Relational (operational RDBMS, analytic RDBMS, or SQL-on-Hadoop).
- Files (generic HDFS — Hadoop Distributed File System or S3).*
- NoSQL (MongoDB and HBase were mentioned).
- Search (Elasticsearch was mentioned among others).
- Zoomdata also tries to detect data variability.
- Zoomdata is OEM/embedding-friendly.
*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.
Core aspects of Zoomdata’s technical strategy include: Read more
I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:
- An actual automation and management user interface, as opposed to the current management style, which is almost entirely via scripts (except for the monitoring UI).
- That’s scheduled for public beta in May, and general availability later this year.
- It will include some kind of integrated provisioning with VMware, OpenStack, et al.
- One goal is to let you apply database changes, software upgrades, etc. without taking the cluster down.
- A reasonable backup strategy.
- A snapshot copy is made of the database.
- A copy of the log is streamed somewhere.
- Periodically — the default seems to be 6 hours — the log is applied to create a new current snapshot.
- For point-in-time recovery, you take the last snapshot prior to the point, and roll forward to the desired point.
- A reasonable locking strategy!
- Document-level locking is all-but-promised for MongoDB 2.8.
- That means what it sounds like. (I mention this because sometimes an XML database winds up being one big document, which leads to confusing conversations about what’s going on.)
- Security. My eyes glaze over at the details, but several major buzzwords have been checked off.
- A general code rewrite to allow for (more) rapid addition of future features.
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:
- The UI aspect is why BI tends to be sold to business departments; the platform aspect is why it also makes sense to sell BI to IT shops attempting to establish enterprise standards.
- The UI aspect is why it makes sense to sell and market BI much as one would applications; the platform aspect is why it makes sense to sell and market BI much as one would database technology.
- The UI aspect is why vendors want to integrate BI with transaction-processing applications; the platform aspect is, I suppose, why they have so much trouble making the integration work.
- The UI aspect is why BI is judged on … well, on snazzy UIs and demos. The platform aspect is a big reason why the snazziest UI doesn’t always win.
*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.
Key aspects of BI platform technology can include:
- Query and data management. That’s the area I most commonly write about, for example in the cases of Platfora, QlikView, or Metamarkets. It goes back to the 1990s — notably the Business Objects semantic layer and Cognos PowerPlay MOLAP (MultiDimensional OnLine Analytic Processing) engine — and indeed before that to the report writers and fourth-generation languages of the 1970s. This overlaps somewhat with …
- … data integration and metadata management. Business Objects, Qlik, and other BI vendors have bought data integration vendors. Arguably, there was a period when Information Builders’ main business was data connectivity and integration. And sometimes the main value proposition for a BI deal is “We need some way to get at all that data and bring it together.”
- Security and access control – authentication, authorization, and all the additional As.
- Scheduling and delivery. When 10s of 1000s of desktops are being served, these aren’t entirely trivial. Ditto when dealing with occasionally-connected mobile devices.
|Categories: Business intelligence, Business Objects, ClearStory Data, Cognos, Data warehousing, Endeca, Information Builders, Metamarkets and Druid, MOLAP, Platfora, Predictive modeling and advanced analytics, QlikTech and QlikView||11 Comments|
I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify.
- Market leaders serve high-end customers with complex, high-end products and services, often distributed through a costly sales channel.
- Upstarts serve a different market segment, often cheaply and/or simply, perhaps with a different business model (e.g. a different sales channel).
- Upstarts expand their offerings, and eventually attack the leaders in their core markets.
In response (this is the Innovator’s Solution part):
- Leaders expand their product lines, increasing the value of their offerings in their core markets.
- In particular, leaders expand into adjacent market segments, capturing margins and value even if their historical core businesses are commoditized.
- Leaders may also diversify into direct competition with the upstarts, but that generally works only if it’s via a separate division, perhaps acquired, that has permission to compete hard with the main business.
But not all cleverness is “disruption”.
- Routine product advancement by leaders — even when it’s admirably clever — is “sustaining” innovation, as opposed to the disruptive stuff.
- Innovative new technology from small companies is not, in itself, disruption either.
Here are some of the examples that make me think of the whole subject. Read more
|Categories: Business intelligence, Data warehousing, Hadoop, Microsoft and SQL*Server, MongoDB, MySQL, Netezza, NewSQL, NoSQL, Oracle, Predictive modeling and advanced analytics, QlikTech and QlikView, Tableau Software||13 Comments|
If I had my way, the business intelligence part of investigative analytics — i.e. , the class of business intelligence tools exemplified by QlikView and Tableau — would continue to be called “data exploration”. Exploration what’s actually going on, and it also carries connotations of the “fun” that users report having with the products. By way of contrast, I don’t know what “data discovery” means; the problem these tools solve is that the data has been insufficiently explored, not that it hasn’t been discovered at all. Still “data discovery” seems to be the term that’s winning.
Confusingly, the Teradata Aster library of functions is now called “Discovery” as well, although thankfully without the “data” modifier. Further marketing uses of the term “discovery” will surely follow.
Enough terminology. What sets exploration/discovery business intelligence tools apart? I think these products have two essential kinds of feature:
- Query modification.
- Query result revisualization.*
|Categories: Business intelligence, Endeca, Memory-centric data management, QlikTech and QlikView, Tableau Software||8 Comments|
The cardinal rules of DBMS development
Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.
That’s if things go extremely well.
Rule 2: You aren’t an exception to Rule 1.
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
DBMS with Hadoop underpinnings …
… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more
A number of people and companies are using the term “iterative analytics”. This is confusing, because it can mean at least three different things:
- You analyze something quickly, decide the result is not wholly satisfactory, and try again. Examples might include:
- Aggressive use of drilldown, perhaps via an advanced-interface business intelligence tool such as Tableau or QlikView.
- Any case where you run a query or a model, think about the results, and run another one after that.
- You develop an intermediate analytic result, and using it as input to the next round of analysis. This is roughly equivalent to saying that iterative analytics refers to a multi-step analytic process involving a lot of derived data.
- #1 and #2 conflated/combined. This is roughly equivalent to saying that iterative analytics refers to all of to investigative analytics, sometimes known instead as exploratory analytics.
Based both on my personal conversations and a quick Google check, it’s reasonable to say #1 and #3 seem to be the most common usages, with #2 trailing a little bit behind.
But often it’s hard to be sure which of the various possible meanings somebody has in mind.
Monash’s First and Third Laws of Commercial Semantics state:
|Categories: Analytic technologies, Business intelligence, QlikTech and QlikView, Tableau Software||3 Comments|