Scientific research
Discussion of how database and related technologies are used to support scientific research. Related subjects include:
MarkLogic 5, and why you might care
MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:
- More-of-the-same in line with MarkLogic’s core positioning.
- A new bi-directional Hadoop connector.
- A free MarkLogic Express edition, limited in license terms more than in actual features, as per Slide 27 of the deck MarkLogic graciously supplied for me to post.
Also, MarkLogic is early with a feature that most serious DBMS vendors will soon have – support for tiered storage, with writes going first to solid-state storage, then being flushed to disk via a caching-style algorithm.* And as befits a sometime search-engine-substitute, MarkLogic has finally licensed a large set of document filters, from an Australian company called Isys. Apparently, the special virtue of the Isys filters is that they’re good at extracting not only text, but metadata as well.
*If there’s a caching algorithm that doesn’t contain a major element of LRU (Least Recently Used), I don’t recall ever hearing about it.
MarkLogic seems to have settled on a positioning that, although distressingly buzzword-heavy, is at least partly based upon reality. The real part includes:
- MarkLogic is a serious, enterprise-class DBMS (see for example Slide 12 of the MarkLogic deck) …
- … which has been optimized from the getgo for poly-structured data.
- MarkLogic can and does scale out to handle large amounts of data.
- MarkLogic is a general-purpose DBMS, suitable for both short-request and analytic tasks.
- MarkLogic is particularly well suited for analyses with long chains of “progressive enhancement” (MarkLogic’s favorite term when talking about derived data).
- MarkLogic often plays the role of a content assembler and/or search engine, and the people who use MarkLogic in those ways are commonly doing things that can be described as research and analysis.
Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.
| Categories: Hadoop, MarkLogic, Market share and customer counts, Scientific research, Solid-state memory, Structured documents, Text | 1 Comment |
Commercial software for academic use
As Jacek Becla explained:
- Academic scientists like their software to be open source, for reasons that include both free-like-speech and free-like-beer.
- What’s more, they like their software to be dead-simple to administer and use, since they often lack the dedicated human resources for anything else.
Even so, I think that academic researchers, in the natural and social sciences alike, commonly overlook the wealth of commercial software that could help them in their efforts.
I further think that the commercial software industry could do a better job of exposing its work to academics, where by “expose” I mean:
- Give your stuff to academics for free.
- Call their attention to your free offering.
Reasons to do so include:
- Public benefit. Scientific research is important.
- Training future customers. There’s huge academic/commercial crossover, especially as students join the for-profit workforce.
| Categories: Business intelligence, Data warehousing, Infobright, Petabyte-scale data management, Predictive modeling and advanced analytics, Scientific research | 7 Comments |
IBM is buying parallelization expert Platform Computing
IBM is acquiring Platform Computing, a company with which I had one briefing, last August. Quick background includes: Read more
| Categories: Hadoop, IBM and DB2, Investment research and trading, MapReduce, Parallelization, Scientific research | 5 Comments |
XLDB: The one conference I like to attend
I’m not a big fan of conferences, but I really like XLDB. Last year I got a lot out of XLDB, even though I couldn’t stay long (my elder care issues were in full swing). The year before I attended the whole thing — in Lyon, France, no less — and learned a lot more. This year’s XLDB conference is at SLAC — the organization formerly known as the Stanford Linear Accelerator Center — on Sand Hill Road in Menlo Park, October 18-19. As of right now, I plan to be there, at least on the first day. XLDB’s agenda and registration details (inexpensive) can be found on the XLDB conference website.
The only reason I wouldn’t go is if that turned out to be a lousy week for me to travel to California.
The people who go XLDB tend to be really smart — either research scientists, hardcore database technologists, or others who can hold their own with those folks. Audience participation can be intense; the most talkative members I can recall were Mike Stonebraker, Martin Kersten, Michael McIntire, and myself. Even the vendor folks tend to the smart — past examples include Stephen Brobst, Jeff Hammerbacher, Luke Lonergan, and IBM Fellow Laura Haas. When we had a datageek bash on my last trip to the SF area, several guys said they were planning to attend XLDB as well.
XLDB stands for eXtremely Large DataBases, and those are indeed what gets talked about there. Read more
| Categories: Data warehousing, Predictive modeling and advanced analytics, Scientific research | 5 Comments |
Hadoop notes
I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.
The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:
- Internet businesses.
- Traditional enterprises ‘ internet operations.
- Traditional enterprises’ other operations.
Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.
| Categories: Cloudera, Hadoop, Health care, Hortonworks, Investment research and trading, Log analysis, MapR, MapReduce, Market share and customer counts, Scientific research, Web analytics | 5 Comments |
Petabyte-scale Hadoop clusters (dozens of them)
I recently learned that there are 7 Vertica clusters with a petabyte (or more) each of user data. So I asked around about other petabyte-scale clusters. It turns out that there are several dozen such clusters (at least) running Hadoop.
Cloudera can identify 22 CDH (Cloudera Distribution [of] Hadoop) clusters holding one petabyte or more of user data each, at 16 different organizations. This does not count Facebook or Yahoo, who are huge Hadoop users but not, I gather, running CDH. Meanwhile, Eric Baldeschwieler of Hortonworks tells me that Yahoo’s latest stated figures are:
- 42,000 Hadoop nodes …
- … holding 180-200 petabytes of data.
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
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
A few notes from XLDB 4
As much as I believe in the XLDB conferences, I only found time to go to (a big) part of one day of XLDB 4 myself. In general: Read more
| Categories: Analytic technologies, Health care, Liberty and privacy, Michael Stonebraker, MySQL, Open source, Parallelization, Petabyte-scale data management, Scientific research | 2 Comments |
Nested data structures keep coming up, especially for log files
Nested data structures have come up several times now, almost always in the context of log files.
- Google has published about a project called Dremel. Per Tasso Agyros, one of Dremel’s key concepts is nested data structures.
- Those arrays that the XLDB/SciDB folks keep talking about are meant to be nested data structures. Scientific data is of course log-oriented. eBay was very interested in that project too.
- Facebook’s log files have a big nested data structure flavor.
I don’t have a grasp yet on what exactly is happening here, but it’s something.
| Categories: Facebook, Google, Log analysis, Scientific research, Theory and architecture, eBay | 7 Comments |