Analytic technologies
Discussion of technologies related to information query and analysis. Related subjects include:
- Business intelligence
- Data warehousing
- (in Text Technologies) Text mining
- (in The Monash Report) Data mining
- (in The Monash Report) General issues in analytic technology
Libraries in Teradata Aster
I recently wrote (emphasis added):
My clients at Teradata Aster probably see things differently, but I don’t think their library of pre-built analytic packages has been a big success. The same goes for other analytic platform vendors who have done similar (generally lesser) things. I believe that this is because such limited libraries don’t do enough of what users want.
The bolded part has been, shall we say, confirmed. As Randy Lea tells it, Teradata Aster sales qualification includes the determination that at least one SQL-MR operator — be relevant to the use case. (“Operator” seems to be the word now, rather than “function”.) Randy agreed that some users prefer hand-coding, but believes a large majority would like to push work to data analysts/business analysts who might have strong SQL skills, but be less adept at general mathematical programming.
This phrasing will all be less accurate after the release of Aster 6, which extends Aster’s capabilities beyond the trinity of SQL, the SQL-MR library, and Aster-supported hand-coding.
Randy also said:
- A typical Teradata Aster production customer uses 8-12 of the prebuilt functions (but now they seem to be called operators).
- nPath is used in almost every Aster account. (And by now nPath has morphed into a family of about 5 different things.)
- The Aster collaborative filtering operator is used in almost every account.
- Ditto a/the text operator.
- Several business intelligence vendors are partnering for direct access to selected Teradata Aster operators — mentioned were Tableau, TIBCO Spotfire, and Alteryx.
- I don’t know whether this is on the strength of a specific operator or not, but Aster is used to help with predictive parts failure applications in multiple industries.
And Randy seemed to agree when I put words in his mouth to the effect that the prebuilt operators save users months of development time.
Meanwhile, Teradata Aster has started a whole new library for relationship analytics.
| Categories: Application areas, Aster Data, Data warehousing, Predictive modeling and advanced analytics, Teradata, Text | 1 Comment |
What matters in investigative analytics?
In a general pontification on positioning, I wrote:
every product in a category is positioned along the same set of attributes,
and went on to suggest that summary attributes were more important than picky detailed ones. So how does that play out for investigative analytics?
First, summary attributes that matter for almost any kind of enterprise software include:
- Performance and scalability. I write about analytic performance and scalability a lot. Usually that’s in the context of analytic DBMS, but it also arises in analytic stacks such as Platfora, Metamarkets or even QlikView, and also in the challenges of making predictive modeling scale.
- Reliability, availability and security.* This is more crucial for short-request applications than analytic ones, but even your analytic systems shouldn’t leak data or crash.
- Goodness of fit with legacy systems. I hate that one, because enterprises often sacrifice way too much in favor of that benefit.
- Price. Duh.
*I picked up that phrase when — abbreviated as RAS — it was used to characterize the emphasis for Oracle 8. I like it better than a general and ambiguous concept of “enterprise-ready”.
The reason I’m writing this post, however, is to call out two summary attributes of special importance in investigative analytics — which regrettably which often conflict with each other — namely:
- Agility. People don’t want to submit requests for reports or statistical analyses; they want to get answers as soon as the questions come to mind.
- Completeness of feature set — for a particular use case, that is. There’s no such thing as an investigative analytics offering with a feature set that’s close to complete for all purposes; even SAS, IBM and other behemoths fall short.
Much of what I work on boils down to those two subjects. For example: Read more
| Categories: Aster Data, Business intelligence, Data warehousing, KXEN, Predictive modeling and advanced analytics, SAS Institute, Teradata | 11 Comments |
Visualization or navigation?
I’ve suggested in the past, approximately, that the platform technology side of business intelligence is more significant than the user interface. That formulation, however, doesn’t exactly capture what I believe. To be more precise, let’s differentiate between a couple aspects of business intelligence UI.
It might seem that a lot of the action in business intelligence revolves around ever-better visualization. After all, Tableau is clearly identified as a visualization-centric technology; who’s hotter than Tableau? And numerous other vendors talk of “visualizations” too. But I don’t think that’s exactly right — rather, I see navigation as being a much bigger deal. And unlike most pure visualization, navigation usually depends strongly on underlying platform capabilities.
Examples of what I mean by innovative navigation — all of which have been developed or have gained prominence over the past decade or so — include:
- QlikView’s core behavior — all that associative navigation.
- QlikView’s collaboration, and every other BI collaboration capability I know of.
- ClearStory, although you won’t get to see what I mean until the launch next month.
- BI search or faceted-search UIs. (E.g. Endeca.)
- BI that is launched from operational applications.
| Categories: Business intelligence, Data warehousing, Tableau Software | 8 Comments |
Thoughts on in-memory columnar add-ons
Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:
- Oracle, IBM (Edit: See the rebuttal comment below), and Microsoft are all doing something similar …
- … because it makes sense.
- The basic idea is to take the technology that manages indexes — which are basically columns+pointers — and massage it into an actual column store. However …
- … the devil is in the details. See, for example, my May post on IBM’s version, called BLU, outlining all the engineering IBM did around that feature.
- Notwithstanding certain merits of this approach, I don’t believe in complete alternatives to analytic RDBMS. The rise of analytic DBMS oriented toward multi-structured data just strengthens that point.
I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.
Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:
- I argued back in 2011 that traditional databases will wind up in RAM, basically because …
- … Moore’s Law will make it ever cheaper to store them there.
- Still, cheaper != cheap, so this is a technology only to use with your most valuable data — i.e., that transactional stuff.
- These are very tabular technologies, without much in the way of multi-structured data support.
| Categories: Columnar database management, Data warehousing, IBM and DB2, Memory-centric data management, Microsoft and SQL*Server, OLTP, Oracle, SAP AG, Workday | 6 Comments |
Schema-on-need
Two years ago I wrote about how Zynga managed analytic data:
Data is divided into two parts. One part has a pretty ordinary schema; the other is just stored as a huge list of name-value pairs. (This is much like eBay‘s approach with its Teradata-based Singularity, except that eBay puts the name-value pairs into long character strings.) … Zynga adds data into the real schema when it’s clear it will be needed for a while.
What was then the province of a few huge web companies is now poised to be a broader trend. Specifically:
- Relational DBMS are adding or enhancing their support for complex datatypes, to accommodate various kinds of machine-generated data.
- MongoDB-compatible JSON is the flavor of the day on the short-request side, but alternatives include other JSON, XML, other key-value, or text strings.
- It is often possible to index on individual attributes inside the complex datatype.
- The individual attributes inside the complex datatypes amount to virtual columns, which can play similar roles in SQL statements as physical columns do.
- Over time, the DBA may choose to materialize virtual columns as additional physical columns, to boost query performance.
That migration from virtual to physical columns is what I’m calling “schema-on-need”. Thus, schema-on-need is what you invoke when schema-on-read no longer gets the job done. 😉
| Categories: Data models and architecture, Data warehousing, MongoDB, PostgreSQL, Schema on need, Structured documents | 10 Comments |
Trends in predictive modeling
I talked with Teradata about a bunch of stuff yesterday, including this week’s announcements in in-database predictive modeling. The specific news was about partnerships with Fuzzy Logix and Revolution Analytics. But what I found more interesting was the surrounding discussion. In a nutshell:
- Teradata is finally seeing substantial interest in in-database modeling, rather than just in-database scoring (which has been important for years) and in-database data preparation (which is a lot like ELT — Extract/Load/transform).
- Teradata is seeing substantial interest in R.
- It seems as if similar groups of customers are interested in both parts of that, such as:
- Usual-suspect consumer marketing sectors (telecom, credit card, retail).*
- Semiconductor manufacturing.**
- Parallelized SAS modeling on Teradata seems to be limited by the small number of algorithms that are parallelized. (SAS scoring, I presume, is a different matter.)
This is the strongest statement of perceived demand for in-database modeling I’ve heard. (Compare Point #3 of my July predictive modeling post.) And fits with what I’ve been hearing about R.
| Categories: EAI, EII, ETL, ELT, ETLT, Parallelization, Predictive modeling and advanced analytics, Revolution Analytics, SAS Institute, Telecommunications, Teradata | 1 Comment |
SAP is buying KXEN
First, some quick history.
- I first heard of KXEN 7-8 years ago from Roman Bukary, then of SAP. He positioned KXEN as an easy-to-embed predictive modeling tool, which was getting various interesting partnerships and OEM deals.
- Returning those near-roots, KXEN is being bought (Q4 expected close) by SAP.
- I say “near roots” because KXEN’s original story had something to do with SVMs (Support Vector Machines).
- But that was already old news back in 2006, and KXEN had pivoted to a simpler and more automated modeling approach. Presumably, this ease of modeling was part of the reason for KXEN’s OEM/partnership appeal.
However, I don’t want to give the impression that KXEN is the second coming of Crystal Reports. Most of what I heard about KXEN’s partnership chops, after Roman’s original heads-up, came from Teradata. Even KXEN itself didn’t seem to see that as a major part of their strategy.
And by the way, KXEN is yet another example of my observation that fancy math rarely drives great enterprise software success.
KXEN’s most recent strategies are perhaps best described by contrasting it to the vastly larger SAS. Read more
| Categories: KXEN, Predictive modeling and advanced analytics, SAP AG, SAS Institute | 5 Comments |
The Hemisphere program
Another surveillance slide deck has emerged, as reported by the New York Times and other media outlets. This one is for the Hemisphere program, which apparently:
- Stores CDRs (Call Detail Records), many or all of which are collected via …
- … some kind of back door into the AT&T switches that many carriers use. (See Slide 2.)
- Has also included “subscriber information” for AT&T phones since July, 2012.
- Contains “long distance and international” CDRs back to 1987.
- Currently adds 4 billion CDRs per day.
- Is administered by a Federal drug-related law enforcement agency but …
- … is used to combat many non-drug-related crimes as well. (See Slides 21-26.)
Other notes include:
- The agencies specifically mentioned on Slide 16 as making numerous Hemisphere requests are the DEA (Drug Enforcement Agency) and DHS (Department of Homeland Security).
- “Roaming” data giving city/state is mentioned in the deck, but more precise geo-targeting is not.
I’ve never gotten a single consistent figure, but typical CDR size seems to be in the 100s of bytes range. So I conjecture that Project Hemisphere spawned one of the first petabyte-scale databases ever.
Hemisphere Project unknowns start: Read more
| Categories: Data warehousing, GIS and geospatial, Petabyte-scale data management, Specific users, Surveillance and privacy, Telecommunications | Leave a Comment |
Cloudera Hadoop strategy and usage notes
When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.
- The four biggies are:
- MapReduce. Duh.
- SQL, specifically Impala. This is as opposed to the uneasy Hive/MapReduce layering.
- Search.
- “Math” , which seems to mainly be through partnerships with SAS and Revolution Analytics. I don’t know a lot about how these work, but I presume they bypass MapReduce, in which case I could imagine them greatly outperforming Mahout.
- Stream processing (Storm) is next in line.
- Graph — e.g. Giraph — rises to at least the proof-of-concept level. Again, the hope would be that this well outperforms graph-on-MapReduce.
- Charles is also seeing at least POC interest in Spark.
- But MPI (Message Passing Interface) on Hadoop isn’t going anywhere fast, except to the extent it’s baked into SAS or other “math” frameworks. Generic MPI use cases evidently turn out to be a bad fit for Hadoop, due to factors such as:
- Low data volumes.
- Latencies in various parts of the system
HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.
Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more
Why privacy laws should be based on data use, not data possession
For years I’ve argued three points about privacy intrusions and surveillance:
- Privacy intrusions are a huge threat to liberty. Since the Snowden revelations started last June, this view has become more widely accepted.
- Much of the problem is the very chilling effects they can have upon the exercise of day-to-day freedoms. Fortunately, I’m not as alone in saying that as I once feared. For example, Christopher Slobogin made that point in a recent CNN article, and then pointed me to a paper* citing other people echoing it, including Sonia Sotomayor.
- Liberty can’t be effectively protected just by controls on the collection, storage, or dissemination of data; direct controls are needed on the use of data as well. Use-based data controls are much more robust in the face of technological uncertainty and change than possession-based ones are.
Since that last point is still very much a minority viewpoint,** I’ll argue it one more time below. Read more