Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:
- I spent three weeks in California on a hybrid personal/business trip. I had a bunch of meetings, but not three weeks’ worth.
- The timing was awkward for most companies I wanted to see. No blame accrues to those who didn’t make themselves available.
- I came back with a nasty cough. Follow-up phone calls aren’t an option until next week.
- I’m impatient to start writing. Hence tonight’s posts. But it’s difficult for a man and his cough to be productive at the same time.
A running list of recent posts is:
- As a companion to this post, I’m publishing a very long one on vendor lock-in.
- Spark and Databricks are both prospering, and of course enhancing their technology as well.
- Ditto DataStax.
- Flink is interesting as the streaming technology it’s now positioned to be, rather than the overall Spark alternative it used to be positioned as but which the world didn’t need.
Subjects I’d like to add to that list include:
- MemSQL, Zoomdata, and Neo Technology (also prospering).
- Cloudera (multiple topics, as usual).
- Analytic SQL engines (“traditional” analytic RDBMS aren’t doing well).
- Microsoft’s reinvention (it feels real).
- Metadata (it’s ever more of a thing).
- Machine learning (it’s going to be a big portion of my research going forward).
- Transitions to the cloud — this subject affects almost everything else.
As I observed yet again last week, much of analytics is concerned with anomaly detection, analysis and response. I don’t think anybody understands the full consequences of that fact,* but let’s start with some basics.
An anomaly, for our purposes, is a data point or more likely a data aggregate that is notably different from the trend or norm. If I may oversimplify, there are three kinds of anomalies:
- Important signals. Something is going on, and it matters. Somebody — or perhaps just an automated system — needs to know about it. Time may be of the essence.
- Unimportant signals. Something is going on, but so what?
- Pure noise. Even a fair coin flip can have long streaks of coming up “heads”.
Two major considerations are:
- Whether the recipient of a signal can do something valuable with the information.
- How “costly” it is for the recipient to receive an unimportant signal or other false positive.
What I mean by the latter point is:
- Something that sets a cell phone buzzing had better be important, to the phone’s owner personally.
- But it may be OK if something unimportant changes one small part of a busy screen display.
Anyhow, the Holy Grail* of anomaly management is a system that sends the right alerts to the right people, and never sends them wrong ones. And the quest seems about as hard as that for the Holy Grail, although this one uses more venture capital and fewer horses. 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|
Whenever somebody asks for my help on application technology strategy, I start by trying to ascertain three things. The absolute first is actually a prerequisite to almost any kind of useful conversation, which is to ascertain in general terms what the hell it is that we are talking about.
My second goal is to ascertain technology constraints. Three common types are:
- Compatible with legacy systems and/or enterprise standards.
- Cheap, free and/or open source.
- Proven, vetted by sufficiently many references, and/or generally having an “enterprise-y” reputation.
That’s often a short and straightforward discussion, except in those awkward situations when all three of my bullet points above are applicable at once.
The third item is usually more interesting. I try to figure out what is to be accomplished. That’s usually not a simple matter, because the initial list of goals and requirements is almost never accurate. It’s actually more common that I have to tell somebody to be more ambitious than that I need to rein them in.
Commonly overlooked needs include:
- If you want to sell something and have happy users, you need a good UI.
- You will also soon need tools and a UI for administration.
- Customers demand low-latency/fresh data. Your explanation of why they don’t really need it doesn’t contradict the fact that they want it.
- Providing data access and saying “You can hook up any BI tool you want and build charts” is not generally regarded as offering a good UI.
- When “adding analytics” to something previously focused on short-request processing, it is common to underestimate the variety of things users will soon want to do. (One common reason for this under-estimate is that after years of being told it can’t be done, they’ve learned not to ask.)
And if you take one thing away from this post, then take this:
- If you “know” exactly which features are or aren’t helpful to users, …
- .. and if you supply only what you “know” they should use, …
- … then you will discover that what you “knew” wasn’t really accurate.
I guarantee it.
|Categories: Business intelligence, Buying processes, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics||2 Comments|
Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.
Making Cloudera run in the cloud has three major aspects:
- Cloudera’s usual software, ported to run on the cloud platform(s).
- Cloudera Director, which for example launches cloud instances.
- Points of integration, e.g. taking information about security-oriented roles from the platform and feeding then to the role-based security that is specific to Cloudera Enterprise.
Features new in this week’s release of Cloudera Director include:
- An API for job submission.
- Support for spot and preemptable instances.
- High availability.
- Some cluster repair.
- Some cluster cloning.
I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.
As for porting, let me start by noting: Read more
I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
|Categories: Business intelligence, Business Objects, Cognos, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Information Builders, MicroStrategy, Software as a Service (SaaS), Teradata||5 Comments|
Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:
- They’re both titanic figures in the database industry.
- They both gave me testimonials on the home page of my business website.
- They both have been known to use the present tense when the future tense would be more accurate.
I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.
But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**
*When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.
**Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.
Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as: Read more
I only have mixed success at getting my clients to reach out to me for messaging advice when they’re introducing something new. Cloudera Navigator Optimizer, which is being announced along with Cloudera 5.5, is one of my failures in that respect; I heard about it for the first time Tuesday afternoon. I hate the name. I hate some of the slides I saw. But I do like one part of the messaging, namely the statement that this is about “refactoring” queries.
All messaging quibbles aside, I think the Cloudera Navigator Optimizer story is actually pretty interesting, and perhaps not just to users of SQL-on-Hadoop technologies such as Hive (which I guess I’d put in that category for simplicity) or Impala. As I understand Cloudera Navigator Optimizer:
- It’s all about analytic SQL queries.
- Specifically, it’s about reducing duplicated work.
- It is not an “optimizer” in the ordinary RDBMS sense of the word.
- It’s delivered via SaaS (Software as a Service).
- Conceptually, it’s not really tied to SQL-on-Hadoop. However, …
- … in practice it likely will be used by customers who want to optimize performance of Cloudera’s preferred styles of SQL-on-Hadoop, either because they’re already using SQL-on-Hadoop or in connection with an initial migration.
|Categories: Business intelligence, Cloudera, Data pipelining, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, SQL/Hadoop integration||4 Comments|
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|
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 (this post) is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.
*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes.
- Kudu is an alternative to HDFS (Hadoop Distributed File System), or to HBase.
- Kudu is meant to be the underpinning for Impala, Spark and other analytic frameworks or engines.
- Kudu is not meant for OLTP (OnLine Transaction Processing), at least in any foreseeable release. For example:
- Kudu doesn’t support multi-row transactions.
- There are no active efforts to front-end Kudu with an engine that is fast at single-row queries.
- Kudu is rather columnar, except for transitory in-memory stores.
- Kudu’s core design points are that it should:
- Accept data very quickly.
- Immediately make that data available for analytics.
- More specifically, Kudu is meant to accept, along with slower forms of input:
- Lots of fast random writes, e.g. of web interactions.
- Streams, viewed as a succession of inserts.
- Updates and inserts alike.
- The core “real-time” use cases for which Kudu is designed are, unsurprisingly:
- Low-latency business intelligence.
- Predictive model scoring.
- Kudu is designed to work fine with spinning disk, and indeed has been tested to date mainly on disk-only nodes. Even so, Kudu’s architecture is optimized for the assumption that there will be at least some flash on the node.
- Kudu is designed primarily to support relational/SQL processing. However, Kudu also has a nested-data roadmap, which of course starts with supporting the analogous capabilities in Impala.
|Categories: Business intelligence, Cloudera, Columnar database management, Database compression, Databricks, Spark and BDAS, Hadoop, HBase, Predictive modeling and advanced analytics, Solid-state memory, SQL/Hadoop integration||7 Comments|