Occasionally I talk with an astute reporter — there are still a few left — and get led toward angles I hadn’t considered before, or at least hadn’t written up. A blog post may then ensue. This is one such post.
There is a group of questions going around that includes:
- Is Hadoop overhyped?
- Has Hadoop adoption stalled?
- Is Hadoop adoption being delayed by skills shortages?
- What is Hadoop really good for anyway?
- Which adoption curves for previous technologies are the best analogies for Hadoop?
To a first approximation, my responses are: Read more
|Categories: Application areas, Data warehousing, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Hortonworks, MapR, MapReduce, Market share and customer counts, Open source, Pricing||6 Comments|
I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:
- The closest thing to an HBase company, ala MongoDB/MongoDB or DataStax/Cassandra, is Cloudera.
- Cloudera still uses a figure of 20% of its customers being HBase-centric.
- HBaseCon and so on notwithstanding, that figure isn’t really reflected in Cloudera’s marketing efforts. Cloudera’s marketing commitment to HBase has never risen to nearly the level of MongoDB’s or DataStax’s push behind their respective core products.
- With Cloudera’s move to “zero/one/many” pricing, Cloudera salespeople have little incentive to push HBase hard to accounts other than HBase-first buyers.
- Cloudera no longer dominates HBase development, if it ever did.
- Cloudera is the single biggest contributor to HBase, by its count, but doesn’t make a majority of the contributions on its own.
- Cloudera sees Hortonworks as having become a strong HBase contributor.
- Intel is also a strong contributor, as are end user organizations such as Chinese telcos. Not coincidentally, Intel was a major Hadoop provider in China before the Intel/Cloudera deal.
- As far as Cloudera is concerned, HBase is just one data storage technology of several, focused on high-volume, high-concurrency, low-latency short-request processing. Cloudera thinks this is OK because of HBase’s strong integration with the rest of the Hadoop stack.
- Others who may be inclined to disagree are in several cases doing projects on top of HBase to extend its reach. (In particular, please see the discussion below about Apache Phoenix and Trafodion, both of which want to offer relational-like functionality.)
|Categories: Cloudera, Clustering, Data models and architecture, Database diversity, Hadoop, HBase, Hortonworks, HP and Neoview, Intel, Market share and customer counts, NoSQL, Open source||4 Comments|
Hortonworks, IBM, EMC Pivotal and others have announced a project called “Open Data Platform” to do … well, I’m not exactly sure what. Mainly, it sounds like:
- An attempt to minimize the importance of any technical advantages Cloudera or MapR might have.
- A face-saving way to admit that IBM’s and Pivotal’s insistence on having their own Hadoop distributions has been silly.
- An excuse for press releases.
- A source of an extra logo graphic to put on marketing slides.
Edit: Now there’s a press report saying explicitly that Hortonworks is taking over Pivotal’s Hadoop distro customers (which basically would mean taking over the support contracts and then working to migrate them to Hortonworks’ distro).
The claim is being made that this announcement solves some kind of problem about developing to multiple versions of the Hadoop platform, but to my knowledge that’s a problem rarely encountered in real life. When you already have a multi-enterprise open source community agreeing on APIs (Application Programming interfaces), what API inconsistency remains for a vendor consortium to painstakingly resolve?
Anyhow, it now seems clear that if you want to use a Hadoop distribution, there are three main choices:
- Cloudera’s flavor, whether as software (from Cloudera) or in an appliance (e.g. from Oracle).
- MapR’s flavor, as software from MapR.
- Hortonworks’ flavor, from a number of vendors, including Hortonworks, IBM, Pivotal, Teradata et al.
In saying that, I’m glossing over a few points, such as: Read more
|Categories: Amazon and its cloud, Cloudera, EMC, Emulation, transparency, portability, Greenplum, Hadoop, Hortonworks, IBM and DB2, MapR, Open source||10 Comments|
1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.
- The predictive experimentation I wrote about over Thanksgiving calls naturally for some BI/dashboarding to monitor how it’s going.
- If you think about Nutonian’s pitch, it can be approximated as “Root-cause analysis so easy a business analyst can do it.” That could be interesting to jump to after BI has turned up anomalies. And it should be pretty easy to whip up a UI for choosing a data set and objective function to model on, since those are both things that the BI tool would know how to get to anyway.
I’ve also heard a couple of ideas about how predictive modeling can support BI. One is via my client Omer Trajman, whose startup ScalingData is still semi-stealthy, but says they’re “working at the intersection of big data and IT operations”. The idea goes something like this:
- Suppose we have lots of logs about lots of things.* Machine learning can help:
- Notice what’s an anomaly.
- Group* together things that seem to be experiencing similar anomalies.
- That can inform a BI-plus interface for a human to figure out what is happening.
Makes sense to me.
* The word “cluster” could have been used here in a couple of different ways, so I decided to avoid it altogether.
Finally, I’m hearing a variety of “smart ETL/data preparation” and “we recommend what columns you should join” stories. I don’t know how much machine learning there’s been in those to date, but it’s usually at least on the roadmap to make the systems (yet) smarter in the future. The end benefit is usually to facilitate BI.
2. Discussion of graph DBMS can get confusing. For example: Read more
|Categories: Business intelligence, Greenplum, Hadoop, Hortonworks, Log analysis, Neo Technology and Neo4j, Nutonian, Predictive modeling and advanced analytics, RDF and graphs, WibiData||4 Comments|
- Hortonworks’ subscription revenues for the 9 months ended last September 30 appear to be:
- $11.7 million from everybody but Microsoft, …
- … plus $7.5 million from Microsoft, …
- … for a total of $19.2 million.
- Hortonworks states subscription customer counts (as per Page 55 this includes multiple “customers” within the same organization) of:
- 2 on April 30, 2012.
- 9 on December 31, 2012.
- 25 on April 30, 2013.
- 54 on September 30, 2013.
- 95 on December 31, 2013.
- 233 on September 30, 2014.
- Per Page 70, Hortonworks’ total September 30, 2014 customer count was 292, including professional services customers.
- Non-Microsoft subscription revenue in the quarter ended September 30, 2014 seems to have been $5.6 million, or $22.5 million annualized. This suggests Hortonworks’ average subscription revenue per non-Microsoft customer is a little over $100K/year.
- This IPO looks to be a sharply “down round” vs. Hortonworks’ Series D financing earlier this year.
- In March and June, 2014, Hortonworks sold stock that subsequently was converted into 1/2 a Hortonworks share each at $12.1871 per share.
- The tentative top of the offering’s price range is $14/share.
- That’s also slightly down from the Series C price in mid-2013.
And, perhaps of interest only to me — there are approximately 50 references to YARN in the Hortonworks S-1, but only 1 mention of Tez.
|Categories: Hadoop, Hortonworks, HP and Neoview, Market share and customer counts, Microsoft and SQL*Server, Pricing, Teradata, Yahoo||8 Comments|
- Cloudera continued to improve various aspects of its product line, especially Impala with a Version 2.0. Good for them. One should always be making one’s products better.
- Cloudera announced a variety of partnerships with companies one would think are opposed to it. Not all are Barney. I’m now hard-pressed to think of any sustainable-looking relationship advantage Hortonworks has left in the Unix/Linux world. (However, I haven’t heard a peep about any kind of Cloudera/Microsoft/Windows collaboration.)
- Cloudera is getting more cloud-friendly, via a new product — Cloudera Director. Probably there are or will be some cloud-services partnerships as well.
Notes on Cloudera Director start:
- It’s closed-source.
- Code and support are included in any version of Cloudera Enterprise.
- It’s a management tool. Indeed, Cloudera characterized it to me as a sort of manager of Cloudera Managers.
What I have not heard is any answer for the traditional performance challenge of Hadoop-in-the-cloud, which is:
- Hadoop, like most analytic RDBMS, tightly couples processing and storage in a shared-nothing way.
- Standard cloud architectures, however, decouple them, thus mooting a considerable fraction of Hadoop performance engineering.
Maybe that problem isn’t — or is no longer — as big a deal as I’ve been told.
Hadoop World/Strata is this week, so of course my clients at Cloudera will have a bunch of announcements. Without front-running those, I think it might be interesting to review the current state of the Cloudera product line. Details may be found on the Cloudera product comparison page. Examining those details helps, I think, with understanding where Cloudera does and doesn’t place sales and marketing focus, which given Cloudera’s Hadoop market stature is in my opinion an interesting thing to analyze.
So far as I can tell (and there may be some errors in this, as Cloudera is not always accurate in explaining the fine details):
- CDH (Cloudera Distribution … Hadoop) contains a lot of Apache open source code.
- Cloudera has a much longer list of Apache projects that it thinks comprise “Core Hadoop” than, say, Hortonworks does.
- Specifically, that list currently is: Hadoop, Flume, HCatalog, Hive, Hue, Mahout, Oozie, Pig, Sentry, Sqoop, Whirr, ZooKeeper.
- In addition to those projects, CDH also includes HBase, Impala, Spark and Cloudera Search.
- Cloudera Manager is closed-source code, much of which is free to use. (I.e., “free like beer” but not “free like speech”.)
- Cloudera Navigator is closed-source code that you have to pay for (free trials and the like excepted).
- Cloudera Express is Cloudera’s favorite free subscription offering. It combines CDH with the free part of Cloudera Manager. Note: Cloudera Express was previously called Cloudera Standard, and that terminology is still reflected in parts of Cloudera’s website.
- Cloudera Enterprise is the umbrella name for Cloudera’s three favorite paid offerings.
- Cloudera Enterprise Basic Edition contains:
- All the code in CDH and Cloudera Manager, and I guess Accumulo code as well.
- Commercial licenses for all that code.
- A license key to use the entirety of Cloudera Manager, not just the free part.
- Support for the “Core Hadoop” part of CDH.
- Support for Cloudera Manager. Note: Cloudera is lazy about saying this explicitly, but it seems obvious.
- The code for Cloudera Navigator, but that’s moot, as the corresponding license key for Cloudera Navigator is not part of the package.
- Cloudera Enterprise Data Hub Edition contains:
- Everything in Cloudera Basic Edition.
- A license key for Cloudera Navigator.
- Support for all of HBase, Accumulo, Impala, Spark, Cloudera Search and Cloudera Navigator.
- Cloudera Enterprise Flex Edition contains everything in Cloudera Basic Edition, plus support for one of the extras in Data Hub Edition.
In analyzing all this, I’m focused on two particular aspects:
- The “zero, one, many” system for defining the editions of Cloudera Enterprise.
- The use of “Data Hub” as a general marketing term.
|Categories: Cloudera, Data warehousing, Databricks, Spark and BDAS, Hadoop, HBase, Hortonworks, Open source, Pricing||2 Comments|
I’m on record as noting and agreeing with an industry near-consensus that Spark, rather than Tez, will be the replacement for Hadoop MapReduce. I presumed that Hortonworks, which is pushing Tez, disagreed. But Shaun Connolly of Hortonworks suggested a more nuanced view. Specifically, Shaun tweeted thoughts including:
Tez vs Spark = Apples vs Oranges.
Spark is general-purpose engine with elegant APIs for app devs creating modern data-driven apps, analytics, and ML algos.
Tez is a framework for expressing purpose-built YARN-based DAGs; its APIs are for ISVs & engine/tool builders who embed it
[For example], Hive embeds Tez to convert its SQL needs into purpose-built DAGs expressed optimally and leveraging YARN
That said, I haven’t yet had a chance to understand what advantages Tez might have over Spark in the use cases that Shaun relegates it to.
- The Twitter discussion with Shaun was a spin-out from my research around streaming for Hadoop.
|Categories: Data warehousing, Databricks, Spark and BDAS, Hadoop, Hortonworks, Predictive modeling and advanced analytics||6 Comments|
The genesis of this post is that:
- Hortonworks is trying to revitalize the Apache Storm project, after Storm lost momentum; indeed, Hortonworks is referring to Storm as a component of Hadoop.
- Cloudera is talking up what I would call its human real-time strategy, which includes but is not limited to Flume, Kafka, and Spark Streaming. Cloudera also sees a few use cases for Storm.
- This all fits with my view that the Current Hot Subject is human real-time data freshness — for analytics, of course, since we’ve always had low latencies in short-request processing.
- This also all fits with the importance I place on log analysis.
- Cloudera reached out to talk to me about all this.
Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.
In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more
|Categories: Cloudera, Complex event processing (CEP), Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Health care, Hortonworks, Log analysis, Specific users, Splunk, Web analytics||6 Comments|
I’ve talked with many companies recently that believe they are:
- Focused on building a great data management and analytic stack for log management …
- … unlike all the other companies that might be saying the same thing …
- … and certainly unlike expensive, poorly-scalable Splunk …
- … and also unlike less-focused vendors of analytic RDBMS (which are also expensive) and/or Hadoop distributions.
At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.
Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.
A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with: Read more