Aster Database Release 5 and Teradata Aster appliance
It was obviously just a matter of time before there would be an Aster appliance from Teradata and some tuned bidirectional Teradata-Aster connectivity. These have now been announced. I didn’t notice anything particularly surprising in the details of either. About the biggest excitement is that Aster is traditionally a Red Hat shop, but for the purposes of appliance delivery has now embraced SUSE Linux.
Along with the announcements comes updated positioning such as:
- Better SQL than the MapReduce alternatives have.
- Better MapReduce than the SQL alternatives have.
- Easy(ier) way to do complex analytics on multi-structured data. (Aster has embraced that term.)
and of course
- Now also with Teradata’s beautifully engineered hardware and system management software!
| Categories: Aster Data, Data warehouse appliances, Data warehousing, Predictive modeling and advanced analytics, Teradata, Workload management | Leave a Comment |
Teradata Columnar and Teradata 14 compression
Teradata is pre-announcing Teradata 14, for delivery by the end of this year, where by “Teradata 14” I mean the latest version of the DBMS that drives the classic Teradata product line. Teradata 14’s flagship feature is Teradata Columnar, a hybrid-columnar offering that follows in the footsteps of Greenplum (now part of EMC) and Aster Data (now part of Teradata).
The basic idea of Teradata Columnar is:
- Each table can be stored in Teradata in row format, column format, or a mix.
- You can do almost anything with a Teradata columnar table that you can do with a row-based one.
- If you choose column storage, you also get some new compression choices.
| Categories: Archiving and information preservation, Columnar database management, Data warehousing, Database compression, Oracle, Rainstor, Teradata | 7 Comments |
Oracle Database Appliance soundbites
It turns out that Oracle’s new small appliance isn’t really an Exadata Mini-Me. Rather, the Oracle Database Appliance is — well, it seems to be a box with an Oracle DBMS in it. (Plus Oracle RAC and so on.) The whole thing is priced for and targeted at the SMB (Small & Medium Business) market, whatever that means to Oracle.
I’m not hugely optimistic about the Oracle Database Appliance. Rather, my thoughts — lightly edited from a chat with a reporter — include:
- This doesn’t solve Oracle’s SMB problems, which include:
- Oracle software is too difficult and costly to administer. The appliance will make a modest dent in that one, but it’s not any kind of game-changer, because the issues relate to the antique design of the Oracle DBMS. (I.e., I think ongoing database administration is a bigger deal than, say, one-time system set-up.)
- SMBs use third-party applications whenever they can, with an increasing preference for SaaS. Application and SaaS vendors prefer non-Oracle alternatives when they are feasible.
- Thus, Oracle is not well positioned to thrive in the SMB market … except maybe through its MySQL subsidiary, but that has a long way to go too.
- Clayton Christensen’s The Innovator’s Solution teaches us that Oracle should focus on selling a thick stack of technology to its highest-end customers, and that’s exactly what Oracle does focus on.
| Categories: MySQL, Oracle, Software as a Service (SaaS) | 13 Comments |
Exadata Mini-Me?
It is being suggested that Oracle is about to introduce small, (relatively) cheap Exadata boxes. Key quotes include:
We estimate a price point of $100K-$200K, well below Exadata prices of $500K-$2.5M.
and
- The Exadata could fit under a desk;
- Customers wouldn’t need a database admin to maintain the Exadata environment;
- The focus of the Exadata mini would be ease of management over running complex enterprise applications.
The whole thing sounds appealing, but I must confess that the idea of “zero-DBA” Oracle takes me aback. It might look OK at demo time, but I have trouble imagining it working in live production situations.
| Categories: Exadata, Oracle | 14 Comments |
Are there any remaining reasons to put new OLTP applications on disk?
Once again, I’m working with an OLTP SaaS vendor client on the architecture for their next-generation system. Parameters include:
- 100s of gigabytes of data at first, growing to >1 terabyte over time.
- High peak loads.
- Public cloud portability (but they have private data centers they can use today).
- Simple database design — not a lot of tables, not a lot of columns, not a lot of joins, and everything can be distributed on the same customer_ID key.
- Stream the data to a data warehouse, that will grow to a few terabytes. (Keeping only one year of OLTP data online actually makes sense in this application, but of course everything should go into the DW.)
So I’m leaning to saying: Read more
The database architecture of salesforce.com, force.com, and database.com
salesforce.com, force.com, and database.com use exactly the same database infrastructure and architecture. That’s the good news. The bad news is that salesforce.com is somewhat obscure about technical details, for reasons such as:
- A long-ago marketing decision to not give infrastructure details, so as to convey a “Don’t worry; we’ll take care of everything” message.
- Even so, a long-ago and perhaps now-regretted marketing decision to disclose and even exaggerate salesforce.com’s reliance on Oracle, as part of an early-days attempt to prove salesforce was using enterprise-class technology.
- A desire to hide the recipe for salesforce.com’s secret sauce.
- Force of habit — I’m not sure salesforce even knows how to tell its technical story with any clarity.
Actually, salesforce.com has moved some kinds of data out of Oracle that previously used to be stored there. Besides Oracle, salesforce uses at least a file system and a RAM-based data store about which I have no details. Even so, much of salesforce.com’s data is stored in Oracle — a single instance of Oracle, which it believes may be the largest instance of Oracle in the world.
| Categories: Data models and architecture, Market share and customer counts, Memory-centric data management, Object, OLTP, Oracle, salesforce.com, Software as a Service (SaaS) | 19 Comments |
salesforce.com, force.com, database.com, data.com, heroku.com — notes and context
As previously noted, I attended Dreamforce, the user conference for my clients at salesforce.com. When I work with them, I focus primarily on database.com and related businesses. I’ve had to struggle a bit, however, to sort out the various pieces, and specifically the differences among:
- salesforce.com. This is the parent company, and the runaway leader in the SaaS (Software as a Service) enterprise application market, especially in the area of CRM (Customer Relationship Management).
- force.com. This is salesforce.com’s application development stack split out for other SaaS vendors to use, both inside and outside the CRM segment. It can be referred to as a PaaS offering (Platform as a Service). force.com relies on a proprietary salesforce.com language called APEX, which has a strong stored procedure/ database trigger orientation.
- database.com. This is the database part of force.com, spun out separately in general availability as of Dreamforce two weeks ago.
- data.com. Also launched at Dreamforce (and based, if I understand correctly, on an acquisition), this is a provider of 3rd-party data you might use as inputs to your CRM systems.
- Heroku. Another salesforce.com acquisition, Heroku is in essence a PaaS competitor to force.com. Heroku is focused on Ruby and Java, and supports a number of DBMS, SQL and NoSQL alike.
- AppExchange. This is a marketplace for things designed to integrate with salesforce.com (and perhaps also apps built on force.com). The latest claim is that there are 1200+ AppExchange offerings.
- The complete set of SaaS apps built on force.com. A 2008 white paper refers to 47,000 organizations being “supported” by force.com. Recently I’ve heard a figure just under 100,000. I’m not clear as to what that metric measures — aggregate users of SaaS apps built via force.com? Clearly there are a lot of SaaS apps built on force.com, with actual customers, but I don’t know how big “a lot” is. (Perhaps a salesforce.com person could chime into the comment thread with some clarity.)
| Categories: Market share and customer counts, Pricing, salesforce.com, Software as a Service (SaaS) | 2 Comments |
Kaminario goes (mainly) flash
Kaminario, which used to be in the business of solid state storage via DRAM, now is emphasizing hybrid DRAM/flash storage appliances instead. The reason is evidently price. Per terabyte of primary storage (before mirroring onto disk and so on):
- A Kaminario K2 DRAM-only appliance costs $100K.
- A Kaminario K2 flash-only appliance costs $30K (but nobody buys that configuration).
- A typical Kaminario K2 hybrid DRAM/flash appliance might cost $35K (which tells us that there’s a lot more flash than DRAM).
Kaminario positions DRAM as where you focus your most write-intensive/ bottlenecking loads, such as logging or temp space, with the primary benefit being performance and a secondary benefit being slowing the wear on your flash.
| Categories: Kaminario, OLTP, Pricing, Solid-state memory | 6 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 |
Aster Data business trends
Last month, I reviewed with the Aster Data folks which markets they were targeting and selling into, subsequent to acquisition by their new orange overlords. The answers aren’t what they used to be. Aster no longer focuses much on what it used to call frontline (i.e., low-latency, operational) applications; those are of course a key strength for Teradata. Rather, Aster focuses on investigative analytics — they’ve long endorsed my use of the term — and on the batch run/scoring kinds of applications that inform operational systems.