Application areas
Posts focusing on the use of database and analytic technologies in specific application domains. Related subjects include:
- Any subcategory
- (in Text Technologies) Specific application areas for text analytics
Big Data is Watching You!
There’s a boom in large-scale analytics. The subjects of this analysis may be categorized as:
- People
- Financial trades
- Electronic networks
- Everything else
The most varied, interesting, and valuable of those four categories is the first one.
| Categories: Analytic technologies, Aster Data, Data warehousing, Investment research and trading, Log analysis, MapReduce, RDF and graphs, Specific users, Telecommunications, Web analytics | 3 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 | 5 Comments |
Cassandra technical overview
Back in March, I talked with Jonathan Ellis of Rackspace, who runs the Apache Cassandra project. I started drafting a blog post then, but never put it up. Then Jonathan cofounded Riptano, a company to commercialize Cassandra, and so I talked with him again in May. Well, I’m finally finding time to clear my Cassandra/Riptano backlog. I’ll cover the more technical parts below, and the more business- or usage-oriented ones in a companion Cassandra/Riptano post.
Jonathan’s core claims for Cassandra include:
- Cassandra is shared-nothing.
- Cassandra has good approaches to replication and partitioning, right out of the box.
- In particular, Cassandra is good for use cases that distribute a database around the world and want to access it at “local” latencies. (Indeed, Jonathan asserts that non-local replication is a significant non-big-data Cassandra use case.)
- Cassandra’s scale-out is application-transparent, unlike sharded MySQL’s.
- Cassandra is fast at both appends and range queries, which would be hard to accomplish in a pure key-value store.
In general, Jonathan positions Cassandra as being best-suited to handle a small number of operations at high volume, throughput, and speed. The rest of what you do, as far as he’s concerned, may well belong in a more traditional SQL DBMS. Read more
| Categories: Amazon and its cloud, Cassandra, Facebook, Google, Log analysis, NoSQL, Open source, Parallelization, Riptano | 4 Comments |
Why you should go to XLDB4
Scientific data commonly:
- Comes in large volumes
- Is machine-generated
- Is augmented by synthetic and/or derived data
- Has a spatial and/or temporal structure
In those respects, it is akin to some of the hottest areas for big data analytics, including:
- Investment trade data – big, partly machine generated, augmented (often), temporal
- Web/network log data – big, machine-generated, post-processed into derived form, temporal
- Marketing analytic data – big, post-processed into derived form
- Genomic data
So when Jacek Becla started the XLDB conferences on the premise that scientific and big data analytic challenges have a lot in common, he had a point. There are several tough database problems that the science-focused folks have taken the leading in thinking about, but which are soon going to matter to the commercial world as well. And that’s one of two big reasons why you should consider participating in XLDB4, October 6-7, at the SLAC facility in Menlo Park, CA, as an attendee, sponsor, or both.
The other big reason is that it is important for the world that XLDB succeed. Read more
| Categories: Investment research and trading, Log analysis, Scientific research, Web analytics | Leave a Comment |
Cloudera Enterprise and Hadoop evolution
I talked with Cloudera a couple of weeks ago in connection with the impending release of Cloudera Enterprise. I’d say: Read more
The most important part of the “social graph” is neither social nor a graph
“Social graph” is a highly misleading term, and so is “social network analysis.” By this I mean:
There’s something akin to “social graphs” and “social network analysis” that is more or less worthy of all the current hype – but graphs and network analysis are only a minor part of the whole story.
In particular, the most important parts of the Facebook “social graph” are neither social nor a graph. Rather, what’s really important is an aggregate Profile of Revealed Preferences, of which person-to-person connections or other things best modeled by a graph play only a small part.
| Categories: Analytic technologies, Facebook, Games and virtual worlds, Liberty and privacy, RDF and graphs, Web analytics | 7 Comments |
VoltDB finally launches
VoltDB is finally launching today. As is common for companies in sectors I write about, VoltDB — or just “Volt” — has discovered the virtues of embargoes that end 12:01 am. Let’s go straight to the technical highlights:
- VoltDB is based on the H-Store technology, which I wrote about in February, 2009. Most of what I said about H-Store then applies to VoltDB today.
- VoltDB is a no-apologies ACID relational DBMS, which runs entirely in RAM.
- VoltDB has rather limited SQL. (One example: VoltDB can’t do SUMs in SQL.) However, VoltDB guy Tim Callaghan (Mark Callaghan’s lesser-known but nonetheless smart brother) asserts that if you code up the missing functionality, it’s almost as fast as if it were present in the DBMS to begin with, because there’s no added I/O from the handoff between the DBMS and the procedural code. (The data’s in RAM one way or the other.)
- VoltDB’s Big Conceptual Performance Story is that it does away with most locks, latches, logs, etc., and also most context switching.
- In particular, you’re supposed to partition your data and architect your application so that most transactions execute on a single core. When you can do that, you get VoltDB’s performance benefits. To the extent you can’t, you’re in two-phase-commit performance land. (More precisely, you’re doing 2PC for multi-core writes, which is surely a major reason that multi-core reads are a lot faster in VoltDB than multi-core writes.)
- VoltDB has a little less than one DBMS thread per core. When the data partitioning works as it should, you execute a complete transaction in that single thread. Poof. No context switching.
- A transaction in VoltDB is a Java stored procedure. (The early idea of Ruby on Rails in lieu of the Java/SQL combo didn’t hold up performance-wise.)
- Solid-state memory is not a viable alternative to RAM for VoltDB. Too slow.
- Instead, VoltDB lets you snapshot data to disk at tunable intervals. “Continuous” is one of the options, wherein a new snapshot starts being made as soon as the last one completes.
- In addition, VoltDB will also spool a kind of transaction log to the target of your choice. (Obvious choice: An analytic DBMS such as Vertica, but there’s no such connectivity partnership actually in place at this time.)
More on Sybase IQ, including Version 15.2
Back in March, Sybase was kind enough to give me permission to post a slide deck about Sybase IQ. Well, I’m finally getting around to doing so. Highlights include but are not limited to:
- Slide 2 has some market success figures and so on. (>3100 copies at >1800 users, >200 sales last year)
- Slides 6-11 give more detail on Sybase’s indexing and data access methods than I put into my recent technical basics of Sybase IQ post.
- Slide 16 reminds us that in-database data mining is quite competitive with what SAS has actually delivered with its DBMS partners, even if it doesn’t have the nice architectural approach of Aster or Netezza. (I.e., Sybase IQ’s more-than-SQL advanced analytics story relies on C++ UDFs — User Defined Functions — running in-process with the DBMS.) In particular, there’s a data mining/predictive analytics library — modeling and scoring both — licensed from a small third party.
- A number of the other later slides also have quite a bit of technical crunch. (More on some of those points below too.)
Sybase IQ may have a bit of a funky architecture (e.g., no MPP), but the age of the product and the substantial revenue it generates have allowed Sybase to put in a bunch of product features that newer vendors haven’t gotten around to yet.
More recently, Sybase volunteered permission for me to preannounce Sybase IQ Version 15.2 by a few days (it’s scheduled to come out this week). Read more
Notes on SciDB and scientific data management
I firmly believe that, as a community, we should look for ways to support scientific data management and related analytics. That’s why, for example, I went to XLDB3 in Lyon, France at my own expense. Eight months ago, I wrote about issues in scientific data management. Here’s some of what has transpired since then.
The main new activity I know of has been in the open source SciDB project. Read more
| Categories: Analytic technologies, Data warehousing, GIS and geospatial, Microsoft and SQL*Server, SciDB, Scientific research, Web analytics, eBay | 3 Comments |
The Clustrix story
After my recent post, the Clustrix guys raised their hands and briefed me. Takeaways included: Read more