News and commentary relating to ClearStory Data.
After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.
- My claim that Spark will replace Hadoop MapReduce got much Twitter attention — including some high-profile endorsements — and also some responses here.
- My MemSQL post led to a vigorous comparison of MemSQL vs. VoltDB.
- My post on hardware and storage spawned a lively discussion of Hadoop hardware pricing; even Cloudera wound up disagreeing with what I reported Cloudera as having said. Sadly, there was less response to the part about the partial (!) end of Moore’s Law.
- My Cloudera/SQL/Impala/Hive apparently was well-balanced, in that it got attacked from multiple sides via Twitter & email. Apparently, I was too hard on Impala, I was too hard on Hive, and I was too hard on boxes full of cardboard file cards as well.
- My post on the Intel/Cloudera deal garnered a comment reminding us Dell had pushed the Intel distro.
- My CitusDB post picked up a few clarifying comments.
Here is a catch-all post to complete the set. Read more
A remarkable number of vendors are involved in what might be called “specialized business intelligence”. Some don’t want to call it that, because they think that “BI” is old and passé’, and what they do is new and better. Still, if we define BI technology as, more or less:
- Querying data and doing simple calculations on it, and …
- … displaying it in a nice interface …
- … which also provides good capabilities for navigation,
then BI is indeed a big part of what they’re doing.
Why would vendors want to specialize their BI technology? The main reason would be to suit it for situations in which even the best general-purpose BI options aren’t good enough. The obvious scenarios are those in which the mismatch is one or both of:
- Kinds of data.
- Kinds of questions asked about the data.
For example, in no particular order: Read more
|Categories: Business intelligence, ClearStory Data, Metamarkets and Druid, PivotLink, Platfora, Splunk, StreamBase||6 Comments|
ClearStory Data is:
- One of the two start-ups I’m most closely engaged with.
- Run by a CEO for whom I have great regard, but who does get rather annoying about secrecy.
- On the verge, finally, of fully destealthing.
I think I can do an interesting post about ClearStory while tap-dancing around the still-secret stuff, so let’s dive in.
- Has developed a full-stack business intelligence technology — which will however be given a snazzier name than “BI” — that is focused on incorporating a broad variety of third-party information, usually along with some of the customer’s own data. Thus, ClearStory …
- … pushes Variety and Variability to extremes, more so than it stresses Volume and Velocity. But it does want to be used at interactive/memory-centric speeds.
- Has put a lot of effort into user interface, but in ways that fit my theory that UI is more about navigation than actual display.
- Has much of its technical differentiation in the area of data mustering …
- … and much of the rest in DBMS-like engineering.
- Is a flagship user of Spark.
- Also relies on Storm, HDFS (Hadoop Distributed File System) and various lesser open source projects (e.g. the ubiquitous Zookeeper).
- Is to a large extent written in Scala.
- Is at this time strictly a multi-tenant SaaS (Software as a Service) offering, except insofar as there’s an on-premises agent to help feed customers’ own data into the core ClearStory cloud service.
To a first approximation, ClearStory ingests data in a system built on Storm (code name: Stormy), dumps it into HDFS, and then operates on it in a system built on Spark (code name: Sparky). Along the way there’s a lot of interaction with another big part of the system, a metadata catalog with no code name I know of. Or as I keep it straight:
- ClearStory’s end-user UI talks mainly to Sparky, and also to the metadata store.
- ClearStory’s administrative UI talks mainly to Stormy, and also to the metadata store.
As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:
- The UI aspect is why BI tends to be sold to business departments; the platform aspect is why it also makes sense to sell BI to IT shops attempting to establish enterprise standards.
- The UI aspect is why it makes sense to sell and market BI much as one would applications; the platform aspect is why it makes sense to sell and market BI much as one would database technology.
- The UI aspect is why vendors want to integrate BI with transaction-processing applications; the platform aspect is, I suppose, why they have so much trouble making the integration work.
- The UI aspect is why BI is judged on … well, on snazzy UIs and demos. The platform aspect is a big reason why the snazziest UI doesn’t always win.
*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.
Key aspects of BI platform technology can include:
- Query and data management. That’s the area I most commonly write about, for example in the cases of Platfora, QlikView, or Metamarkets. It goes back to the 1990s — notably the Business Objects semantic layer and Cognos PowerPlay MOLAP (MultiDimensional OnLine Analytic Processing) engine — and indeed before that to the report writers and fourth-generation languages of the 1970s. This overlaps somewhat with …
- … data integration and metadata management. Business Objects, Qlik, and other BI vendors have bought data integration vendors. Arguably, there was a period when Information Builders’ main business was data connectivity and integration. And sometimes the main value proposition for a BI deal is “We need some way to get at all that data and bring it together.”
- Security and access control – authentication, authorization, and all the additional As.
- Scheduling and delivery. When 10s of 1000s of desktops are being served, these aren’t entirely trivial. Ditto when dealing with occasionally-connected mobile devices.
|Categories: Business intelligence, Business Objects, ClearStory Data, Cognos, Data warehousing, Endeca, Information Builders, Metamarkets and Druid, MOLAP, Platfora, Predictive modeling and advanced analytics, QlikTech and QlikView||11 Comments|
I made a remarkably rumpled video appearance yesterday with SiliconAngle honchos John Furrier and Dave Vellante. (Excuses include <3 hours sleep, and then a scrambling reaction to a schedule change.) Topics covered included, with approximate timechecks:
- 0:00 Introductory pabulum, and some technical difficulties
- 2:00 More introduction
- 3:00 Dynamic schemas and data model churn
- 6:00 Surveillance and privacy
- 13:00 Hadoop, especially the distro wars
- 22:00 BI innovation
- 23:30 More on dynamic schemas and data model churn
Edit: Some of my remarks were transcribed.
- I posted on dynamic schemas data model churn a few days ago.
- I capped off a series on privacy and surveillance a few days ago.
- I commented on various Hadoop distributions in June.
|Categories: Business intelligence, ClearStory Data, Data warehousing, Hadoop, MapR, MapReduce, Surveillance and privacy||Leave a Comment|
UC Berkeley’s AMPLab is working on a software stack that:
- Is meant (among other goals) to improve upon Hadoop …
- … but also to interoperate with it, and which in fact …
- … uses significant parts of Hadoop.
- Seems to have the overall name BDAS (Berkeley Data Analytics System).
The whole thing has $30 million in projected funding (half government, half industry) and a 6-year plan (which they’re 2 years into).
Specific projects of note in all that include:
- Mesos, a cluster manager. I don’t know much about Mesos, but it seems to be in production use, most notably at Twitter supporting Storm.
- Spark, a replacement for MapReduce and the associated execution stack.
- Shark, a replacement for Hive.
|Categories: ClearStory Data, Databricks, Spark and BDAS, Hadoop, MapReduce, Parallelization, Specific users, SQL/Hadoop integration||10 Comments|
I visited my clients at Cloudera and Hortonworks last week, along with scads of other companies. A few of the takeaways were:
- Cloudera now has 220 employees.
- Cloudera now has over 100 subscription customers.
- Over the past year, Cloudera has more than doubled in size by every reasonable metric.
- Over half of Cloudera’s customers use HBase, vs. a figure of 18+ last July.
- Omer Trajman — who by the way has made a long-overdue official move into technical marketing — can no longer keep count of how many petabyte-scale Hadoop clusters Cloudera supports.
- Cloudera gets the majority of its revenue from subscriptions. However, professional services and training continue to be big businesses too.
- Cloudera has trained over 12,000 people.
- Hortonworks is training people too.
- Hortonworks now has 70 employees, and plans to have 100 or so by the end of this quarter.
- A number of those Hortonworks employees are executives who come from seriously profit-oriented backgrounds. Hortonworks clearly has capitalist intentions.
- Hortonworks thinks a typical enterprise Hadoop cluster has 20-50 nodes, with 50-100 already being on the large side.
- There are huge amounts of Elastic MapReduce/Hadoop processing in the Amazon cloud. Some estimates say it’s the majority of all Amazon Web Services processing.
- I met with 4 young-company clients who I regard as building vertical analytic stacks (WibiData, MarketShare, MetaMarkets, and ClearStory). All 4 are heavily dependent on Hadoop. (The same isn’t as true of older companies who built out a lot of technology before Hadoop was invented.)
- There should be more HBase information at HBaseCon on May 22.
- If MapR still has momentum, nobody I talked with has noticed.
|Categories: Amazon and its cloud, ClearStory Data, Cloud computing, Cloudera, Hadoop, HBase, Hortonworks, MapR, MapReduce, Market share and customer counts, Petabyte-scale data management, WibiData||1 Comment|
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Excluded from this round of disclosure is one vendor I have never written about.
- Included in this round of disclosure is one client paying for services partly in stock. All our other clients are cash-only.
For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. Read more
- (Bad.) I was planning to cover the launch as well, in a split exclusive, but that plan was changed, costing me considerable wasted work.
- (Worse.) I wasn’t told of the change as soon as it was known. Indeed, I wasn’t told at all; I was left to infer it from the fact that I was now being asked to talk with other reporters.
- (Horrific.) I was quoted in the ClearStory launch press release, but while the sentiments were reasonably in line with my own, the quote was incorrect.*
I’m utterly disgusted with this whole mess, although after talking with her a lot I’m fine with CEO Sharmila Mulligan’s part in it, which is to say with ClearStory’s part in general.
*I avoid the term “platform” as much as possible; indeed, I still don’t really know what the “new platforms” part was supposed to refer to. The Frankenquote wound up with some odd grammar as well.
Actually, in principle I’m a pretty close adviser to ClearStory (for starters, they’re one of my stealth-mode clients). That hasn’t really ramped up yet; in particular, I haven’t had a technical deep dive. So for now I’ll just say: