Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
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 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 and 10gen, MySQL, Netezza, NewSQL, NoSQL, Oracle, Predictive modeling and advanced analytics, QlikTech and QlikView, Tableau Software||13 Comments|
My July 2 comments on predictive modeling were far from my best work. Let’s try again.
1. Predictive analytics has two very different aspects.
Developing models, aka “modeling”:
- Is a big part of investigative analytics.
- May or may not be difficult to parallelize and/or integrate into an analytic RDBMS.
- May or may not require use of your whole database.
- Generally is done by humans.
- Often is done by people with special skills, e.g. “statisticians” or “data scientists”.
More precisely, some modeling algorithms are straightforward to parallelize and/or integrate into RDBMS, but many are not.
Using models, most commonly:
- Is done by machines …
- … that “score” data according to the models.
- May be done in batch or at run-time.
- Is embarrassingly parallel, and is much more commonly integrated into analytic RDBMS than modeling is.
2. Some people think that all a modeler needs are a few basic algorithms. (That’s why, for example, analytic RDBMS vendors are proud of integrating a few specific modeling routines.) Other people think that’s ridiculous. Depending on use case, either group can be right.
3. If adoption of DBMS-integrated modeling is high, I haven’t noticed.
|Categories: Data warehousing, Hadoop, Health care, IBM and DB2, KXEN, Predictive modeling and advanced analytics, SAS Institute||2 Comments|
I’m not having a productive week, part of the reason being a hard drive crash that took out early drafts of what were to be last weekend’s blog posts. Now I’m operating from a laptop, rather than my preferred dual-monitor set-up. So please pardon me if I’m concise even by comparison to my usual standards.
- My recent posts based on surveillance news have been partly superseded by – well, by more news. Some of that news, along with some good discussion, may be found in the comment threads.
- The same goes for my recent Hadoop posts.
- The replay for my recent webinar on real-time analytics is now available. My part ran <25 minutes.
- One of my numerous clients using or considering a “real-time analytics” positioning is Sqrrl, the company behind the NoSQL DBMS Accumulo. Last month, Derrick Harris reported on a remarkable Accumulo success story – multiple US intelligence instances managing 10s of petabytes each, and supporting a variety of analytic (I think mainly query/visualization) approaches.
- Several sources have told me that MemSQL’s Zynga sale is (in part) for Membase replacement. This is noteworthy because Zynga was the original pay-for-some-of-the-development Membase customer.
- More generally, the buzz out of Couchbase is distressing. Ex-employees berate the place; job-seekers check around and then decide not to go there; rivals tell me of resumes coming out in droves. Yes, there’s always some of that, even at obviously prospering companies, but this feels like more than the inevitable low-level buzz one hears anywhere.
- I think the predictive modeling state of the art has become:
- Cluster in some way.
- Model separately on each cluster.
- And if you still want to do something that looks like a regression – linear or otherwise – then you might want to use a tool that lets you shovel training data in WITHOUT a whole lot of preparation* and receive a model back out. Even if you don’t accept that as your final model, it can at least be a great guide to feature selection (in the statistical sense of the phrase) and the like.
- Champion/challenger model testing is also a good idea, at least if you’re in some kind of personalization/recommendation space, and have enough traffic to test like that.**
- Most companies have significant turnover after being acquired, perhaps after a “golden handcuff” period. Vertica is no longer an exception.
- Speaking of my clients at HP Vertica – they’ve done a questionable job of communicating that they’re willing to price their product quite reasonably. (But at least they allowed me to write about $2K/terabyte for hardware/software combined.)
- I’m hearing a little more Amazon Redshift buzz than I expected to. Just a little.
- StreamBase was bought by TIBCO. The rumor says $40 million.
*Basic and unavoidable ETL (Extract/Transform/Load) of course excepted.
**I could call that ABC (Always Be Comparing) or ABT (Always Be Testing), but they each sound like – well, like The Glove and the Lions.
- Cloudera changed CEOs last week. Tom Reilly, late of ArcSight, is the new guy (I don’t know him), while Mike Olson’s titles become Chairman and Chief Strategy Officer. Mike told me Friday that Reilly had secretly been working with him for months.
- Mike shared good-sounding numbers with me. But little is for public disclosure except the stat >400 employees.
- There are always rumors of infighting at Cloudera, perhaps because from earliest days Cloudera was a place where tempers are worn on sleeves. That said, Mike denied stories of problems between him and COO Kirk Dunn, and greatly praised Kirk’s successes at large-account sales.
- Cloudera now self-identifies pretty clearly as an analytic data management company. The vision is multiple execution engines – MapReduce, Impala, something more memory-centric, etc. – talking to any of a variety of HDFS file formats. While some formats may be optimized for specific engines – e.g. Parquet for Impala – anything can work with more or less anything.*
- Mike told me that Cloudera didn’t have any YARN users in production, but thought there would be some by year-end. Even so, he thinks it’s fair to say that Cloudera users have substantial portions of Hadoop 2 in production, for example NameNode failover and HDFS (Hadoop Distributed File System) performance enhancements. Ditto HCatalog.
*Of course, there will always be exceptions. E.g., some formats can be updated on a short-request basis, while others can only be written to via batch conversions.
- There’s a widespread belief that Hortonworks is being shopped. Numerous folks – including me — believe the rumor of an Intel offer for $700 million. Higher figures and alternate buyers aren’t as widely believed.
- Views of MapR market traction, never high, are again on the downswing.
- IBM Big Insights seems to have some traction.
- In case there was any remaining doubt — DBMS vendors are pretty unanimous in agreeing that it makes sense to have Hadoop too. To my knowledge SAP hasn’t been as clear about showing a markitecture incorporating Hadoop as most of the others have … but then, SAP’s markitecture is generally less clear than other vendors’.
- Folks I talk with are generally wondering where and why Datameer lost its way. That still leaves Datameer ahead of other first-generation Hadoop add-on vendors (Karmasphere, Zettaset, et al.), in that I rarely hear them mentioned at all.
- I visited with my client Platfora. Things seem to be going very well.
- My former client Revelytix seems to have racked up some nice partnerships. (I had something to do with that. )
|Categories: Cloudera, Data warehousing, Datameer, Hadoop, Hortonworks, IBM and DB2, Intel, MapR, Market share and customer counts, Platfora, SAP AG, Zettaset||11 Comments|
I visited Cloudera Friday for, among other things, a chat about Impala with Marcel Kornacker and colleagues. Highlights included:
- Impala is meant to someday be a competitive MPP (Massively Parallel Processing) analytic RDBMS.
- At the moment, it is not one. For example, Impala lacks any meaningful form of workload management or query optimization.
- While Impala will run against any HDFS (Hadoop Distributed File System) file format, claims of strong performance assume that the data is in Parquet …
- … which is the replacement for the short-lived Trevni …
- … and which for most practical purposes is true columnar.
- Impala is also meant to be more than an RDBMS; Parquet and presumably in the future Impala can accommodate nested data structures.
- Just as Impala runs against most or all HDFS file formats, Parquet files can be used by most Hadoop execution engines, and of course by Pig and Hive.
- The Impala roadmap includes workload management, query optimization, data skipping, user-defined functions, hash distribution, two turtledoves, and a partridge in a pear tree.
Data gets into Parquet via batch jobs only — one reason it’s important that Impala run against multiple file formats — but background format conversion is another roadmap item. A single table can be split across multiple formats — e.g., the freshest data could be in HBase, with the rest is in Parquet.
A few days ago I posted Daniel Abadi’s thoughts in a discussion of Hadapt, Microsoft PDW (Parallel Data Warehouse)/PolyBase, Pivotal/Greenplum Hawq, and other SQL-Hadoop combinations. This is Dave DeWitt’s response. Emphasis mine.
|Categories: Benchmarks and POCs, Cloudera, Clustering, Data warehousing, Greenplum, Hadapt, Hadoop, MapReduce, Microsoft and SQL*Server, PostgreSQL, SQL/Hadoop integration||5 Comments|
The genesis of this post is:
- Dave DeWitt sent me a paper about Microsoft Polybase.
- I argued with Dave about the differences between Polybase and Hadapt.
- I asked Daniel Abadi for his opinion.
- Dan agreed with Dave, in a long email …
- … that he graciously permitted me to lightly-edit and post.
I love my life.
Per Daniel (emphasis mine): Read more
|Categories: Aster Data, Data warehousing, Greenplum, Hadapt, Hadoop, MapReduce, Microsoft and SQL*Server, SQL/Hadoop integration, Theory and architecture||10 Comments|
I had a good chat with IBM about IBM BLU, aka BLU Accelerator or Acceleration. BLU basics start:
- BLU is a part of DB2.
- BLU works like a columnar analytic DBMS.
- If you want to do a join combining BLU and non-BLU tables, all the BLU tables are joined first, and the result set is joined to the other tables by the rest of DB2.
And yes — that means Oracle is now the only major relational DBMS vendor left without a true columnar story.
BLU’s maturity and scalability basics start:
- BLU is coming out in IBM DB2 10.5, this quarter.
- BLU will initially be single-server, but …
- … IBM claims “near-linear” scalability up to 64 cores, and further says that …
- … scale-out for BLU is coming “soon”.
- IBM already thinks all your analytically-oriented DB2 tables should be in BLU.
- IBM describes the first version of BLU as being optimized for 10 TB databases, but capable of handling 20 TB.
BLU technical highlights include: Read more
|Categories: Columnar database management, Data pipelining, Data warehousing, Database compression, IBM and DB2, Workload management||20 Comments|
Way back in 2006, I wrote about a cool Netezza feature called the zone map, which in essence allows you to do partition elimination even in the absence of strict range partitioning.
Netezza’s substitute for range partitioning is very simple. Netezza features “zone maps,” which note the minimum and maximum of each column value (if such concepts are meaningful) in each extent. This can amount to effective range partitioning over dates; if data is added over time, there’s a good chance that the data in any particular date range is clustered, and a zone map lets you pick out which data falls in the desired data range.
I further wrote
… that seems to be the primary scenario in which zone maps confer a large benefit.
But I now think that part was too pessimistic. For example, in bulk load scenarios, it’s easy to imagine ways in which data can be clustered or skewed. And in such cases, zone maps can let you skip a large fraction of potential I/O.
Over the years I’ve said that other things were reminiscent of Netezza zone maps, e.g. features of Infobright, SenSage, InfiniDB and even Microsoft SQL Server. But truth be told, when I actually use the phrase “zone map”, people usually give me a blank look.
In a recent briefing about BLU, IBM introduced me to a better term — data skipping. I like it and, unless somebody comes up with a good reason not to, I plan to start using it myself.