Theory and architecture
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Boston Big Data Summit keynote outline
Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.
Calpont’s InfiniDB
Since its inception, Calpont has gone through multiple management teams, strategies, and investor groups. What it hadn’t done, ever, is actually shipped a product. Last week, however, Calpont introduced a free/open source DBMS, InfiniDB, with technical details somewhat reminiscent of what Calpont was promising last April. Highlights include:
- Like Infobright, Calpont’s InfiniDB is a columnar DBMS consisting of a MySQL front end and a columnar storage engine.
- Community edition InfiniDB runs on a single server.
- One of commercial/enterprise edition InfiniDB’s main claims to fame will be MPP support.
- There’s no announced time frame for commercial edition InfiniDB.
- InfiniDB’s current compression story is dictionary/token only, with decompression occurring before joins are executed. Improvement is a roadmap item.
- Indeed, InfiniDB has many roadmap items, a few of which can be found here. Also, a great overview of InfiniDB’s current state and roadmap can be found in this MySQL Performance Blog thread. (And follow the links there to find performance discussions of other free analytic DBMS.)
- One thing InfiniDB already has that is still a roadmap item for Infobright is the ability to run a query across multiple cores at once.
- One thing free InfiniDB has that Infobright only offers in its Enterprise Edition is ACID-compliant Insert/Update/Delete. (Note: I wish people would stop saying that Infobright Enterprise Edition isn’t ACID-compliant, since that point was cleared up a while ago.)
- InfiniDB has no indexes or materialized views.
- However, InfiniDB’s retrieval is expedited by something called “Extents,” which sounds a lot like Netezza’s zone maps.
Being on vacation, I’ll stop there for now. (If it weren’t for Tropical Storm/ depression Ida, I might not even be posting this much until I get back.)
| Categories: Analytic technologies, Calpont, Columnar database management, Data warehousing, Database compression, Infobright, MySQL, Open source | 3 Comments |
Aster Data 4.0 and the evolution of “advanced analytic(s) servers”
Since Linda and I are leaving on vacation in a few hours, Aster Data graciously gave me permission to morph its “12:01 am Monday, November 2” embargo into “late Friday night.”
Aster Data is officially announcing the 4.0 release of nCluster. There are two big pieces to this announcement:
- Aster is offering a slick vision for integrating big-database management and general analytic processing on the same MPP cluster, under the not-so-slick name “Data-Application Server.”
- Aster is also offering a sophisticated vision for workload management.
In addition, Aster has matured nCluster in various ways, for example cleaning up a performance problem with single-row updates.
Highlights of the Aster “Data-Application Server” story include: Read more
| Categories: Aster Data, Cloud computing, Data warehousing, EAI, EII, ETL, ELT, ETLT, MapReduce, Market share and customer counts, Teradata, Theory and architecture, Workload management | 9 Comments |
Teradata’s nebulous cloud strategy
As the pun goes, Teradata’s cloud strategy is – well, it’s somewhat nebulous. More precisely, for the foreseeable future, Teradata’s cloud strategy is a collection of rather disjointed parts, including:
- What Teradata calls the Teradata Agile Analytics Cloud, which is a combination of previously existing technology plus one new portlet called the Teradata Elastic Mart(s) Builder. (Teradata’s Elastic Mart(s) Builder Viewpoint portlet is available for download from Teradata’s Developer Exchange.)
- Teradata Data Mover 2.0, coming “Soon”, which will ease copying (ETL without any significant “T”) from one Teradata system to another.
- Teradata Express DBMS crippleware (1 terabyte only, no production use), now available on Amazon EC2 and VMware. (I don’t see where this has much connection to the rest of Teradata’s cloud strategy, except insofar as it serves to fill out a slide.)
- Unannounced (and so far as I can tell largely undesigned) future products.
Teradata openly admits that its direction is heavily influenced by Oliver Ratzesberger at eBay. Like Teradata, Oliver and eBay favor virtual data marts over physical ones. That is, Oliver and eBay believe that the ideal scenario is that every piece of data is only stored once, in an integrated Teradata warehouse. But eBay believes and Teradata increasingly agrees that users need a great deal of control over their use of this data, including the ability to import additional data into private sandboxes, and join it to the warehouse data already there. Read more
| Categories: Analytic technologies, Cloud computing, Data integration and middleware, Data warehousing, EAI, EII, ETL, ELT, ETLT, eBay, Teradata, Theory and architecture | 5 Comments |
This week at the Teradata Partners user conference
Teradata tells me that its press embargoes are ending at 9:00 this morning. Here are some highlights of what’s going on, although names, dates, and details will have to await conversations and press releases this week.
- Teradata is productizing “private cloud,” under names including “Teradata Enterprise Analytics Cloud,” “Teradata Agile Analytics Cloud,” and “Teradata Elastic Mart Builder.” I.e., Teradata hopes to leapfrog Greenplum in its “Enterprise Data Cloud” strategy. This is only fair, in that Greenplum lifted the idea from Teradata and eBay in the first place. It also provides major support for what I think is an extremely sensible trend. Give or take issues of who announces and ships what a couple months before or after a competitor, my early thinking is that the main differences between Greenplum and Teradata in this regard will be:
- Virtual as opposed to just physical data marts, based on robust workload management software. (Advantage: Teradata)
- Pricing, deployment options. (Advantage: Greenplum)
- Features that don’t directly relate to enterprise/private cloud. (Advantage: Either, often Teradata.)
- Teradata is generally strengthening its data movement technology, e.g. for making various appliances work in sync. I’m not too clear yet on the details of that. I think this is what Teradata’s phrase “ecosystem management” refers to.
- Teradata is (pre-)announcing – at least as a statement of direction — an appliance based on solid-state drives (SSDs). I’ve thought for a while that Teradata was a leader in thinking through the issues around solid-state memory in data warehousing, so it makes sense that they’re among the leaders in actually coming to market as well. I plan to say more after meeting with, e.g., Carson Schmidt.
- Teradata has achieved a 300%ish speed-up in geospatial processing. I gather this is largely a byproduct of the parallel analytics work Teradata did around strengthening its SAS integration. However, there don’t seem to be a lot of Teradata geospatial users yet.
- Teradata Express, Teradata’s free Windows-based crippleware, is being ported to Amazon EC2 and VMware as well. Presumably to avoid cannibalizing Teradata product sales, there are quite a few limitations on Teradata Express, including system capacity, database size, and “no production use.”
- Teradata continues to extend its optimizations to handle queries issued by business intelligence tools. Previously, the focus of what Teradata discussed in this regard was query rewrite. But soon automatic recommendation and creation of Aggregate Join Indexes – i.e.., materialized views – will be included as well.
Introduction to SenSage
I visited with SenSage on my two most recent trips to San Francisco. Both visits were, through no fault of SenSage’s, hasty. Still, I think I have enough of a handle on SenSage basics to be worth writing up.
General SenSage highlights include:
Kickfire capacity and pricing
Kickfire’s marketing communication efforts are still a work in progress. Kickfire did finally relax its secrecy about FPGA-vs.-custom-silicon – not coincidentally during Netezza’s recent publicity cycle. That wise choice helped Kickfire get some favorable attention recently for its technical and market strategy, e.g. from Daniel Abadi, Merv Adrian and, kicking things off — as it were — me. Weeks after a recent Kickfire product release, there’s finally a fairly accurate data sheet up, although there’s still one self-defeatingly misleading line I’ll comment on below. Pricing is a whole other area of confusion, although it seems that current list prices have been inadvertently* leaked in Merv’s post linked above, with only one inaccuracy that I can detect.**
*I gather from the company that they forgot to tell Merv pricing was NDA.
** Merv cited a price as “starting” that I believe to be top-of-the-line. No criticism of Merv is implied in that; Kickfire has not been very clear in communicating hard numbers.
All that said, if one takes Kickfire’s marketing statements literally, Kickfire list pricing is around $20-50K per terabyte for a few small, fixed, high-performance configurations. That’s all-in, for plug-and-play appliances. What’s more, that range is based on the actual published user data capacity numbers for various Kickfire models, which I think are low for several reasons:
- Kickfire doesn’t officially admit that its model with 14.4 terabytes of disk can manage more than 6 terabytes of data, even though it clearly can.
- Actually, those 14.4 terabytes of disk can be increased or lowered as you choose.
- The basic compression figures implied in those calculations seem conservative.
- Compression figures are a lot more conservative yet, in that Kickfire assumes you’ll have a lot of actual indexes on your data. I’m not sure that’s necessary for most workloads.
| Categories: Columnar database management, Data warehouse appliances, Data warehousing, Database compression, Kickfire, Pricing | 3 Comments |
Greenplum is going hybrid columnar as well
Over the past summer, Vertica, VectorWise, and Oracle all announced flavors of hybrid row/columnar storage. Now it’s Greenplum’s turn. Greenplum is actually offering true columnar storage, as opposed to Oracle’s PAX-like scheme — and also as opposed to the kind of Frankencolumn storage Daniel Abadi decries. For example, you don’t have to do a join to retrieve multiple columns; you just ask for them and there they are. Similarly, Greenplum doesn’t maintain explicit row IDs – whether in row-oriented or column-oriented append-only storage – relying instead on block-level header information. Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, Database compression, Greenplum, Theory and architecture | 12 Comments |
How 30+ enterprises are using Hadoop
MapReduce is definitely gaining traction, especially but by no means only in the form of Hadoop. In the aftermath of Hadoop World, Jeff Hammerbacher of Cloudera walked me quickly through 25 customers he pulled from Cloudera’s files. Facts and metrics ranged widely, of course:
- Some are in heavy production with Hadoop, and closely engaged with Cloudera. Others are active Hadoop users but are very secretive. Yet others signed up for initial Hadoop training last week.
- Some have Hadoop clusters in the thousands of nodes. Many have Hadoop clusters in the 50-100 node range. Others are just prototyping Hadoop use. And one seems to be “OEMing” a small Hadoop cluster in each piece of equipment sold.
- Many export data from Hadoop to a relational DBMS; many others just leave it in HDFS (Hadoop Distributed File System), e.g. with Hive as the query language, or in exactly one case Jaql.
- Some are household names, in web businesses or otherwise. Others seem to be pretty obscure.
- Industries include financial services, telecom (Asia only, and quite new), bioinformatics (and other research), intelligence, and lots of web and/or advertising/media.
- Application areas mentioned — and these overlap in some cases — include:
- Log and/or clickstream analysis of various kinds
- Marketing analytics
- Machine learning and/or sophisticated data mining
- Image processing
- Processing of XML messages
- Web crawling and/or text processing
- General archiving, including of relational/tabular data, e.g. for compliance
Oracle and Vertica on compression and other physical data layout features
In my recent post on Exadata pricing, I highlighted the importance of Oracle’s compression figures to the discussion, and the uncertainty about same. This led to a Twitter discussion featuring Greg Rahn* of Oracle and Dave Menninger and Omer Trajman of Vertica. I also followed up with Omer on the phone. Read more