Discussion of Endeca and its products, chiefly the Latitude business intelligence product line and underlying technologies.
When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.
- The four biggies are:
- MapReduce. Duh.
- SQL, specifically Impala. This is as opposed to the uneasy Hive/MapReduce layering.
- “Math” , which seems to mainly be through partnerships with SAS and Revolution Analytics. I don’t know a lot about how these work, but I presume they bypass MapReduce, in which case I could imagine them greatly outperforming Mahout.
- Stream processing (Storm) is next in line.
- Graph — e.g. Giraph — rises to at least the proof-of-concept level. Again, the hope would be that this well outperforms graph-on-MapReduce.
- Charles is also seeing at least POC interest in Spark.
- But MPI (Message Passing Interface) on Hadoop isn’t going anywhere fast, except to the extent it’s baked into SAS or other “math” frameworks. Generic MPI use cases evidently turn out to be a bad fit for Hadoop, due to factors such as:
- Low data volumes.
- Latencies in various parts of the system
HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.
Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more
|Categories: Cloudera, Complex event processing (CEP), Databricks, Spark and BDAS, Endeca, Hadoop, HP and Neoview, MapReduce, Predictive modeling and advanced analytics, RDF and graphs, Revolution Analytics, SAS Institute, Teradata||22 Comments|
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|
If I had my way, the business intelligence part of investigative analytics — i.e. , the class of business intelligence tools exemplified by QlikView and Tableau — would continue to be called “data exploration”. Exploration what’s actually going on, and it also carries connotations of the “fun” that users report having with the products. By way of contrast, I don’t know what “data discovery” means; the problem these tools solve is that the data has been insufficiently explored, not that it hasn’t been discovered at all. Still “data discovery” seems to be the term that’s winning.
Confusingly, the Teradata Aster library of functions is now called “Discovery” as well, although thankfully without the “data” modifier. Further marketing uses of the term “discovery” will surely follow.
Enough terminology. What sets exploration/discovery business intelligence tools apart? I think these products have two essential kinds of feature:
- Query modification.
- Query result revisualization.*
|Categories: Business intelligence, Endeca, Memory-centric data management, QlikTech and QlikView, Tableau Software||8 Comments|
In that post, I wrote:
… the Endeca paradigm is really to help you make your way through a structured database, where different portions of the database have different structures. Thus, at various points in your journey, it automagically provides you a list of choices as to where you could go next.
That kind of thing could help Oracle with apps like the wireless telco product catalog deal MongoDB got.
Going back to the Endeca-post quote well, Endeca itself said:
Inside the MDEX Engine there is no overarching schema; each data record carries its own metadata. This enables the rapid combination of a wide range of structured and unstructured content into Latitude’s unified data model. Once inside, the MDEX Engine derives common dimensions and metrics from the available metadata, instantly exposing each for high-performance refinement and analysis in the Discovery Framework. Have a new data source? Simply add it and the MDEX Engine will create new relationships where possible. Changes in source data schema? No problem, adjustments on the fly are easy.
And I pointed out that the MDEX engine was a columnar DBMS.
Meanwhile, Oracle’s own columnar DBMS efforts have been disappointing. Endeca could be an intended answer to that. However, while Oracle’s track record with standalone DBMS acquisitions is admirable (DEC RDB, MySQL, etc.), Oracle’s track record of integrating DBMS acquisitions into the Oracle product itself is not so good. (Express? Essbase? The text product line? None of that has gone particularly well.)
So while I would expect Endeca’s flagship e-commerce shopping engine products to flourish under Oracle’s ownership, I would be cautious about the integration of Endeca’s core technology into the Oracle product line.
I visited my then-clients at Endeca in January. We focused on underpinnings (and strategic counsel) more than on coolness in what the product actually does. But going over my notes I think there’s enough to write up now.
Before saying much else about Endeca, there’s one confusion to dispose of: What’s the relationship between Endeca’s efforts in e-commerce (helping shoppers navigate websites) and business intelligence (helping people navigate their own data)? As Endeca tells it:
- Endeca’s e-commerce and business intelligence efforts are reflections of the same technical approach. Indeed, I’m pretty sure Endeca’s product lines still share much/most of the same technology.
- Endeca went after e-commerce first because that’s where the provable ROI was. As I pointed out a couple of times in 2007, Endeca became a market leader in that area.
- Endeca increased its BI efforts later.
- Circa 2009-10, Endeca differentiated its e-commerce and BI product lines from each other.
- An e-commerce line extension called Page Builder is what really got Endeca through the recent recession.
- The BI product line Latitude was launched in the fall of 2010.