Data warehousing
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:
TwinFin(i) – Netezza’s version of a parallel analytic platform
Much like Aster Data did in Aster 4.0 and now Aster 4.5, Netezza is announcing a general parallel big data analytic platform strategy. It is called Netezza TwinFin(i), it is a chargeable option for the Netezza TwinFin appliance, and many announced details are on the vague side, with Netezza promising more clarity at or before its Enzee Universe conference in June. At a high level, the Aster and Netezza approaches compare/contrast as follows: Read more
| Categories: Aster Data, Data warehouse appliances, Data warehousing, Hadoop, MapReduce, Netezza, Predictive modeling and advanced analytics, SAS Institute, Teradata | 10 Comments |
Aster Data nCluster 4.5
Like Vertica, Netezza, and Teradata, Aster is using this week to pre-announce a forthcoming product release, Aster Data nCluster 4.5. Aster is really hanging its identity on “Big Data Analytics” or some variant of that concept, and so the two major named parts of Aster nCluster 4.5 are:
- Aster Data Analytic Foundation, a set of analytic packages prebuilt in Aster’s SQL-MapReduce
- Aster Data Developer Express, an Eclipse-based IDE (Integrated Development Environment) for developing and testing applications built on Aster nCluster, Aster SQL-MapReduce, and Aster Data Analytic Foundation
And in other Aster news:
- Along with the development GUI in Aster nCluster 4.5, there is also a new administrative GUI.
- Aster has certified that nCluster works with Fusion I/O boards, because at least one retail industry prospect cares. However, that in no way means that arm’s-length Fusion I/O certification is Aster’s ultimate solid-state memory strategy.
- I had the wrong impression about how far Aster/SAS integration has gotten. So far, it’s just at the connector level.
Aster Data Developer Express evidently does some cool stuff, like providing some sort of parallelism testing right on your desktop. It also generates lots of stub code, saving humans from the tedium of doing that. Useful, obviously.
But mainly, I want to write about the analytic packages. Read more
| Categories: Aster Data, Data warehousing, Investment research and trading, Predictive modeling and advanced analytics, RDF and graphs, SAS Institute, Teradata | 9 Comments |
Vertica 4.0
Vertica briefed me last month on its forthcoming Vertica 4.0 release. I think it’s fair to say that Vertica 4.0 is mainly a cleanup/catchup release, washing away some of the tradeoffs Vertica had previously made in support of its innovative DBMS architecture.
For starters, there’s a lot of new analytic functionality. This isn’t Aster/Netezza-style ambitious. Rather, there’s a lot more SQL-99 functionality, plus some time series extensions of the sort that financial services firms – an important market for Vertica – need and love. Vertica did suggest a couple of these time series extensions are innovative, but I haven’t yet gotten detail about those.
Perhaps even more important, Vertica is cleaning up a lot of its previous SQL optimization and execution weirdnesses. In no particular order, I was told: Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, Vertica Systems | 12 Comments |
Intelligent Enterprise’s Editors’/Editor’s Choice list for 2010
As he has before, Intelligent Enterprise Editor Doug Henschen
- Personally selected annual lists of 12 “Most influential” companies and 36 “Companies to watch” in analytics- and database-related sectors.
- Made it clear that these are his personal selections.
- Nonetheless has called it an Editors’ Choice list, rather than Editor’s Choice. 🙂
(Actually, he’s really called it an “award.”)
Comments on the Gartner 2009/2010 Data Warehouse Database Management System Magic Quadrant
February, 2011 edit: I’ve now commented on Gartner’s 2010 Data Warehouse Database Management System Magic Quadrant as well.
At intervals of a little over a year, Gartner Group publishes a Data Warehouse Database Management System Magic Quadrant. Gartner’s 2009 data warehouse DBMS Magic Quadrant — actually, January 2010 — is now out.* For many reasons, including those I noted in my comments on Gartner’s 2008 Data Warehouse DBMS Magic Quadrant, the Gartner quadrant pictures are a bad use of good research. Rather than rehash that this year, I’ll merely call out some points in the surrounding commentary that I find interesting or just plain strange. Read more
The Sybase Aleri RAP
Well, I got a quick Sybase/Aleri briefing, along with multiple apologies for not being prebriefed. (Main excuse: News was getting out, which accelerated the announcement.) Nothing badly contradicted my prior post on the Sybase/Aleri deal.
To understand Sybase’s plans for Aleri and CEP, it helps to understand Sybase’s current CEP-oriented offering, Sybase RAP. So far as I can tell, Sybase RAP has to date only been sold in the form of Sybase RAP: The Trading Edition. In that guise, Sybase RAP has been sold to >40 outfits since its May, 2008 launch, mainly big names in the investment banking and stock exchange sectors. If I understood correctly, the next target market for Sybase RAP is telcos, for real-time network tuning and management.
In addition to any domain-specific applications, Sybase RAP has three layers:
- CEP (Complex Event Processing). Sybase RAP CEP is based on a version of the Coral8 engine Sybase licensed and has been subsequently developing.
- In-memory DBMS. Sybase’s IMDB is part of (but I guess separable from) and has the same API as Sybase’s OLTP DBMS Adaptive Server Enterprise (ASE, aka Sybase Classic).
- Sybase IQ. Actually, Sybase used the phrase “based on Sybase IQ,” but I’m guessing it’s just Sybase IQ.
Open issues in database and analytic technology
The last part of my New England Database Summit talk was on open issues in database and analytic technology. This was closely intertwined with the previous section, and also relied on a lot that I’ve posted here. So I’ll just put up a few notes on that part, with lots of linkage to prior discussion of the same points. Read more
Interesting trends in database and analytic technology
My project for the day is blogging based on my “Database and analytic technology: State of the union” talk of a few days ago. (I called it that because of when it was given, because it mixed prescriptive and descriptive elements, and because I wanted to call attention to the fact that I cover the union of database and analytic technologies – the intersection of those two sectors is an area of particular focus, but is far from the whole of my coverage.)
One section covered recent/ongoing/near-future trends that I thought were particularly interesting, including: Read more
Flash, other solid-state memory, and disk
If there’s one subject on which the New England Database Summit changed or at least clarified my thinking,* it’s future storage technologies. Here’s what I now think:
- Solid-state memory will soon be the right storage technology for a large fraction of databases, OLTP and analytic alike. I’m not sure whether the initial cutoff in database size is best thought of as terabytes or 10s of terabytes, but it’s in that range. And it will increase over time, for the usual cheaper-parts reasons.
- That doesn’t necessarily mean flash. PCM (Phase-Change Memory) is coming down the pike, with perhaps 100X the durability of flash, in terms of the total number of writes it can tolerate. On the other hand, PCM has issues in the face of heat. More futuristically, IBM is also high on magnetic racetrack memory. IBM likes the term storage-class memory to cover all this — which I find regrettable, since the acronym SCM is way overloaded already. 🙂
- Putting a disk controller in front of solid-state memory is really wasteful. It wreaks havoc on I/O rates.
- Generic PCIe interfaces don’t suffice either, in many analytic use cases. Their I/O is better, but still not good enough. (Doing better yet is where Petascan – the stealth-mode company I keep teasing about – comes in.)
- Disk will long be useful for very large databases. Kryder’s Law, about disk capacity, has at least as high an annual improvement as Moore’s Law shows for chip capacity, the disk rotation speed bottleneck notwithstanding. Disk will long be much cheaper than silicon for data storage. And cheaper silicon in sensors will lead to ever more machine-generated data that fills up a lot of disks.
- Disk will long be useful for archiving. Disk is the new tape.
*When the first three people to the question microphone include both Mike Stonebraker and Dave DeWitt, your thinking tends to clarify in a hurry.
Related links
- A slide deck by Mohan of IBM similar to the one he presented at the NEDB Summit about storage-class memories.
- A much more detailed IBM presentation on storage-class memories.
- Oracle’s and Teradata’s beliefs about the importance of solid-state memory.
Other posts based on my January, 2010 New England Database Summit keynote address
- Data-based snooping — a huge threat to liberty that we’re all helping make worse
- Interesting trends in database and analytic technology
- Open issues in database and analytic technology
| Categories: Data warehousing, Michael Stonebraker, Presentations, Solid-state memory, Storage, Theory and architecture | 3 Comments |
The disk rotation speed bottleneck
I’ve been referring to the disk (rotation) speed bottleneck for years, but I don’t really have a clean link for it. Let me fix that right now.
The first hard disks ever were introduced by IBM in 1956. They rotated 1,200 times per minute. Today’s state-of-the-art disk drives rotate 15,000 times per minute. That’s a 12.5-fold improvement since the first term of the Eisenhower Administration. (I understand that the reason for this slow improvement is aerodynamic — a disk that spins too fast literally flies off the spindle.)
Unfortunately, random seek time is bounded below, on average, by 1/2 of a disk’s rotation time. Hence disk seek times can never get below 2 milliseconds.
15,000 RPM = 250 rotations/second, which implies 4 milliseconds/rotation.
From that, much about modern analytic DBMS design follows.