Where Datameer is positioned
I’ve chatted with Datameer a couple of times recently, mainly with CEO Stefan Groschupf, most recently after XLDB last Tuesday. Nothing I learned greatly contradicts what I wrote about Datameer 1 1/2 years ago. In a nutshell, Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).
Stefan reports that these capabilities are appealing to a significant fraction of enterprise or other commercial Hadoop users, especially the EtL and the BI. I don’t doubt him.
| Categories: Business intelligence, Datameer, EAI, EII, ETL, ELT, ETLT, Hadoop | 4 Comments |
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
What to think about BEFORE you make a technology decision
When you are considering technology selection or strategy, there are a lot of factors that can each have bearing on the final decision — a whole lot. Below is a very partial list.
In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.
Multitenancy is particularly interesting, because it has numerous implications. Read more
Investigative analytics and derived data: Enzee Universe 2011 talk
I’ll be speaking Monday, June 20 at IBM Netezza’s Enzee Universe conference. Thus, as is my custom:
- I’m posting draft slides.
- I’m encouraging comment (especially in the short time window before I have to actually give the talk).
- I’m offering links below to more detail on various subjects covered in the talk.
The talk concept started out as “advanced analytics” (as opposed to fast query, a subject amply covered in the rest of any Netezza event), as a lunch break in what is otherwise a detailed “best practices” session. So I suggested we constrain the subject by focusing on a specific application area — customer acquisition and retention, something of importance to almost any enterprise, and which exploits most areas of analytic technology. Then I actually prepared the slides — and guess what? The mix of subjects will be skewed somewhat more toward generalities than I first intended, specifically in the areas of investigative analytics and derived data. And, as always when I speak, I’ll try to raise consciousness about the issues of liberty and privacy, our options as a society for addressing them, and the crucial role we play as an industry in helping policymakers deal with these technologically-intense subjects.
Slide 3 refers back to a post I made last December, saying there are six useful things you can do with analytic technology:
- Operational BI/Analytically-infused operational apps: You can make an immediate decision.
- Planning and budgeting: You can plan in support of future decisions.
- Investigative analytics (multiple disciplines): You can research, investigate, and analyze in support of future decisions.
- Business intelligence: You can monitor what’s going on, to see when it necessary to decide, plan, or investigate.
- More BI: You can communicate, to help other people and organizations do these same things.
- DBMS, ETL, and other “platform” technologies: You can provide support, in technology or data gathering, for one of the other functions.
Slide 4 observes that investigative analytics:
- Is the most rapidly advancing of the six areas …
- … because it most directly exploits performance & scalability.
Slide 5 gives my simplest overview of investigative analytics technology to date: Read more
Metaphors amok
It all started when I disputed James Kobielus’ blogged claim that Hadoop is the nucleus of the next-generation cloud EDW. Jim posted again to reiterate the claim, only this time he wrote that all EDW vendors [will soon] bring Hadoop into their heart of their architectures. (All emphasis mine.)
That did it. I tweeted, in succession:
- Actually, I vote for Hadoop as the lungs of the EDW — first place of entry for essential nutrients.
- Data integration can be the heart of the EDW, pumping stuff around. RDBMS/analytic platform can be the brain.
- iPad-based dashboards that may engender envy, but which actually are only used occasionally and briefly … well, you get the picture.*
*Woody Allen said in Sleeper that the brain was his second-favorite organ.
Of course, that body of work was quickly challenged. Responses included: Read more
| Categories: Analytic technologies, Business intelligence, Data warehousing, EAI, EII, ETL, ELT, ETLT, Fun stuff, Hadoop, Humor, MapReduce | Leave a Comment |
Hadoop confusion from Forrester Research
Jim Kobielus started a recent post
Most Hadoop-related inquiries from Forrester customers come to me. These have moved well beyond the “what exactly is Hadoop?” phase to the stage where the dominant query is “which vendors offer robust Hadoop solutions?”
What I tell Forrester customers is that, yes, Hadoop is real, but that it’s still quite immature.
So far, so good. But I disagree with almost everything Jim wrote after that.
Jim’s thesis seems to be that Hadoop will only be mature when a significant fraction of analytic DBMS vendors have own-branded versions of Hadoop alongside their DBMS, possibly via acquisition. Based on this, he calls for a formal, presumably vendor-driven Hadoop standardization effort, evidently for the whole Hadoop stack. He also says that
Hadoop is the nucleus of the next-generation cloud EDW, but that promise is still 3-5 years from fruition
where by “cloud” I presume Jim means first and foremost “private cloud.”
I don’t think any of that matches Hadoop’s actual strengths and weaknesses, whether now or in the 3-7 year future. My reasoning starts:
- Hadoop is well on its way to being a surviving data-storage-plus-processing system — like an analytic DBMS or DBMS-imitating data integration tool …
- … but Hadoop is best-suited for somewhat different use cases than those technologies are, and the gap won’t close as long as the others remain a moving target.
- I don’t think MapReduce is going to fail altogether; it’s too well-suited for too many use cases.
- Hadoop (as opposed to general MapReduce) has too much momentum to fizzle, perhaps unless it is supplanted by one or more embrace-and-extend MapReduce-plus systems that do a lot more than it does.
- The way for Hadoop to avoid being a MapReduce afterthought is to evolve sufficiently quickly itself; ponderous standardization efforts are quite beside the point.
As for the rest of Jim’s claim — I see three main candidates for the “nucleus of the next-generation enterprise data warehouse,” each with better claims than Hadoop:
- Relational DBMS, much like today. (E.g., Teradata, DB2, Exadata or their successors.) This is the case in which robustness of the central data store matters most.
- Grand cosmic data integration tools. (The descendants of Informatica PowerCenter, et al.) This is the case in which the logic of data relationships can safely be separated from physical storage.
- Nothing. (The architecture could have several strong members, none of which is truly the “nucleus.”) This is the case in which new ways keep being invented to extract high value from data, outrunning what grandly centralized solutions can adapt to. I think this is the most likely case of all.
| Categories: Data integration and middleware, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, Theory and architecture | 9 Comments |
Introduction to SnapLogic
I talked with the SnapLogic team last week, in connection with their SnapReduce Hadoop-oriented offering. This gave me an opportunity to catch up on what SnapLogic is up to overall. SnapLogic is a data integration/ETL (Extract/Transform/Load) company with a good pedigree: Informatica founder Gaurav Dillon invested in and now runs SnapLogic, and VC Ben Horowitz is involved. SnapLogic company basics include:
- SnapLogic has raised about $18 million from Gaurav Dillon and Andreessen Horowitz.
- SnapLogic has almost 60 people.
- SnapLogic has around 150 customers.
- Based in San Mateo, SnapLogic has an office in the UK and is growing its European business.
- SnapLogic has both SaaS (Software as a Service) and on-premise availability, but either way you pay on a subscription basis.
- Typical SnapLogic deal size is under $20K/year. Accordingly, SnapLogic sells over the telephone.
- SnapReduce is in beta with about a dozen customers, and slated for release by year-end.
SnapLogic’s core/hub product is called SnapCenter. In addition, for any particular kind of data one might want to connect, there are “snaps” which connect to — i.e. snap into — SnapCenter.
SnapLogic’s market position(ing) sounds like Cast Iron’s, by which I mean: Read more
| Categories: Cloud computing, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, SnapLogic, Software as a Service (SaaS) | 1 Comment |
Data integration vendors and Hadoop
There have been many recent announcements about how data integration/ETL (Extract/Transform/Load) vendors are going to work with MapReduce. Most of what they say boils down to one or more of a few things:
- Hadoop generally stores data in HDFS (Hadoop Distributed File System). ETL vendors want to be able to extract data from or load it into HDFS.
- ETL vendors have development environments that let you specify/script/whatever ETL jobs. ETL vendors want their development tools to develop ETL processes executed via MapReduce/Hadoop.
- In particular, this allows ETL vendors to exploit the parallel-processing capabilities of MapReduce.
Some additional twists include:
- Pentaho announced business intelligence and ETL for Hadoop last year.
- Syncsort thinks different sort algorithms should be usable with Hadoop. Consequently, it plans to contribute technology to the community to make sort pluggable into Hadoop. (However, Syncsort is keeping its own sort technology proprietary.)
- Syncsort is considering replicating some Hive functionality, starting with joins, hopefully running much faster. (However, Syncsort’s basic Hadoop support is a quarter or three away, so any more advanced functionality would probably come out in 2012 or beyond.)
- SnapLogic fondly thinks that its generation of MapReduce jobs is particularly intelligent.
Finally, my former clients at Pervasive, who haven’t briefed me for a while, seem to have told Doug Henschen that they have pointed DataRush at MapReduce.* However, I couldn’t find evidence of same on the Pervasive DataRush website beyond some help in using all the cores on any one Hadoop node.
*Also see that article because it names a bunch of ETL vendors doing Hadoop-related things.
| Categories: Data integration and middleware, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, Parallelization, Pentaho, Pervasive Software, SnapLogic, Syncsort | 1 Comment |
Introduction to Syncsort and DMExpress
Let’s start with some Syncsort basics.
- Syncsort was founded in 1968.
- As you might guess from its name and age, Syncsort started out selling software for IBM mainframes, used for sorting data. However, for the past 30 or so years, Syncsort’s products have gone beyond sort to also do join, aggregation, and merge. This was the basis for Syncsort’s expansion into the more general ETL (Extract/Transform/Load) business.
- As you might further guess, along the way there was a port to UNIX, development of a GUI (Graphical User Interface), and a change of ownership as Syncsort’s founder more or less cashed out.
- At this point, Syncsort sees itself primarily as a data integration/ETL company, whose main claim to fame is performance, with further claims of linear scaling and no manual tuning.*
One of Syncsort’s favorite value propositions is to contrast the cost of doing ETL in Syncsort, on commodity hardware, to the cost of doing ELT (Extract/Load/Transform) on high-end Teradata gear.
| Categories: Data integration and middleware, Database compression, EAI, EII, ETL, ELT, ETLT, Specific users, Syncsort | 9 Comments |
Comments on EMC Greenplum
I am annoyed with my former friends at Greenplum, who took umbrage at a brief sentence I wrote in October, namely “eBay has thrown out Greenplum“. Their reaction included:
- EMC Greenplum no longer uses my services.
- EMC Greenplum no longer briefs me.
- EMC Greenplum reneged on a commitment to fund an effort in the area of privacy.
The last one really hurt, because in trusting them, I put in quite a bit of effort, and discussed their promise with quite a few other people.