- Continuuity toured in 2012 and touted its “app server for Hadoop” technology.
- Continuuity recently changed its name to Cask and went open source.
- Cask’s product is now called CDAP (Cask Data Application Platform). It’s still basically an app server for Hadoop and other “big data” — ouch do I hate that phrase — data stores.
- Cask and Cloudera partnered.
- I got a more technical Cask briefing this week.
- App servers are a notoriously amorphous technology. The focus of how they’re used can change greatly every couple of years.
- Partly for that reason, I was unimpressed by Continuuity’s original hype-filled positioning.
So far as I can tell:
- Cask’s current focus is to orchestrate job flows, with lots of data mappings.
- This is supposed to provide lots of developer benefits, for fairly obvious reasons. Those are pitched in terms of an integration story, more in a “free you from the mess of a many-part stack” sense than strictly in terms of data integration.
- CDAP already has a GUI to monitor what’s going on. A GUI to specify workflows is coming very soon.
- CDAP doesn’t consume a lot of cycles itself, and hence isn’t a real risk for unpleasant overhead, if “overhead” is narrowly defined. Rather, performance drags could come from …
- … sub-optimal choices in data mapping, database design or workflow composition.
I’m on record as believing that:
- Hadoop needs a memory-centric storage grid.
- Tachyon is a strong candidate to fill the role.
- It’s an open secret that there will be a Tachyon company. However, …
- … no details have been publicized. Indeed, the open secret itself is still officially secret.
- Tachyon technology, which just hit 0.6 a couple of days ago, still lacks many features I regard as essential.
- As a practical matter, most Tachyon interest to date has been associated with Spark. This makes perfect sense given Tachyon’s origin and initial technical focus.
- Tachyon was in 50 or more sites last year. Most of these sites were probably just experimenting with it. However …
- … there are production Tachyon clusters with >100 nodes.
As a reminder of Tachyon basics: Read more
|Categories: Clustering, Databricks, Spark and BDAS, Hadoop, Memory-centric data management||3 Comments|
I chatted last night with Ion Stoica, CEO of my client Databricks, for an update both on his company and Spark. Databricks’ actual business is Databricks Cloud, about which I can say:
- Databricks Cloud is:
- Currently running on Amazon only.
- Not dependent on Hadoop.
- Databricks Cloud, despite having a 1.0 version number, is not actually in general availability.
- Even so, there are a non-trivial number of paying customers for Databricks Cloud. (Ion gave me an approximate number, but is keeping it NDA until Spark Summit East.)
- Databricks Cloud gets at data from S3 (most commonly), Redshift, Elastic MapReduce, and perhaps other sources I’m forgetting.
- Databricks Cloud was initially focused on ad-hoc use. A few days ago the capability was added to schedule jobs and so on.
- Unsurprisingly, therefore, Databricks Cloud has been used to date mainly for data exploration/visualization and ETL (Extract/Transform/Load). Visualizations tend to be scripted/programmatic, but there’s also an ODBC driver used for Tableau access and so on.
- Databricks Cloud customers are concentrated (but not unanimously so) in the usual-suspect internet-centric business sectors.
- The low end of the amount of data Databricks Cloud customers are working with is 100s of gigabytes. This isn’t surprising.
- The high end of the amount of data Databricks Cloud customers are working with is petabytes. That did surprise me, and in retrospect I should have pressed for details.
I do not expect all of the above to remain true as Databricks Cloud matures.
Ion also said that Databricks is over 50 people, and has moved its office from Berkeley to San Francisco. He also offered some Spark numbers, such as: Read more
|Categories: Amazon and its cloud, Cloud computing, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Parallelization, Petabyte-scale data management, Predictive modeling and advanced analytics, Software as a Service (SaaS)||6 Comments|
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
While I don’t find the Open Data Platform thing very significant, an associated piece of news seems cooler — Pivotal is open sourcing a bunch of software, with Greenplum as the crown jewel. Notes on that start:
- Greenplum has been an on-again/off-again low-cost player since before its acquisition by EMC, but open source is basically a commitment to having low license cost be permanently on.
- In most regards, “free like beer” is what’s important here, not “free like speech”. I doubt non-Pivotal employees are going to do much hacking on the long-closed Greenplum code base.
- That said, Greenplum forked PostgreSQL a long time ago, and the general PostgreSQL community might gain ideas from some of the work Greenplum has done.
- The only other bit of newly open-sourced stuff I find interesting is HAWQ. Redis was already open source, and I’ve never been persuaded to care about GemFire.
Greenplum, let us recall, is a pretty decent MPP (Massively Parallel Processing) analytic RDBMS. Various aspects of it were oversold at various times, and I’ve never heard that they actually licked concurrency. But Greenplum has long had good SQL coverage and petabyte-scale deployments and a columnar option and some in-database analytics and so on; i.e., it’s legit. When somebody asks me about open source analytic RDBMS to consider, I expect Greenplum to consistently be on the short list.
Further, the low-cost alternatives for analytic RDBMS are adding up. Read more
|Categories: Amazon and its cloud, Citus Data, Data warehouse appliances, EAI, EII, ETL, ELT, ETLT, EMC, Greenplum, Hadoop, Infobright, MonetDB, Open source, Pricing||5 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|
- Question: Why do policemen work in pairs?
- Answer: One to read and one to write.
A lot has happened in MongoDB technology over the past year. For starters:
- The big news in MongoDB 3.0* is the WiredTiger storage engine. The top-level claims for that are that one should “typically” expect (individual cases can of course vary greatly):
- 7-10X improvement in write performance.
- No change in read performance (which however was boosted in MongoDB 2.6).
- ~70% reduction in data size due to compression (disk only).
- ~50% reduction in index size due to compression (disk and memory both).
- MongoDB has been adding administration modules.
- A remote/cloud version came out with, if I understand correctly, MongoDB 2.6.
- An on-premise version came out with 3.0.
- They have similar features, but are expected to grow apart from each other over time. They have different names.
*Newly-released MongoDB 3.0 is what was previously going to be MongoDB 2.8. My clients at MongoDB finally decided to give a “bigger” release a new first-digit version number.
To forestall confusion, let me quickly add: Read more
|Categories: Database compression, Hadoop, Humor, In-memory DBMS, MongoDB, NoSQL, Open source, Structured documents, Sybase||8 Comments|
There are numerous ways that technology, now or in the future, can significantly improve personal safety. Three of the biggest areas of application are or will be:
- Crime prevention.
- Vehicle accident prevention.
- Medical emergency prevention and response.
Implications will be dramatic for numerous industries and government activities, including but not limited to law enforcement, automotive manufacturing, infrastructure/construction, health care and insurance. Further, these technologies create a near-certainty that individuals’ movements and status will be electronically monitored in fine detail. Hence their development and eventual deployment constitutes a ticking clock toward a deadline for society deciding what to do about personal privacy.
Theoretically, humans aren’t the only potential kind of tyrants. Science fiction author Jack Williamson postulated a depressing nanny-technology in With Folded Hands, the idea for which was later borrowed by the humorous Star Trek episode I, Mudd.
Of these three areas, crime prevention is the furthest along; in particular, sidewalk cameras, license plate cameras and internet snooping are widely deployed around the world. So let’s consider the other two.
Vehicle accident prevention
|Categories: Health care, Predictive modeling and advanced analytics, Public policy, Surveillance and privacy||3 Comments|
In one of my favorite posts, namely When I am a VC Overlord, I wrote:
I will not fund any entrepreneur who mentions “market projections” in other than ironic terms. Nobody who talks of market projections with a straight face should be trusted.
Even so, I got talked today into putting on the record a prediction that machine-generated data will grow at more than 40% for a while.
My reasons for this opinion are little more than:
- Moore’s Law suggests that the same expenditure will buy 40% or so more machine-generated data each year.
- Budgets spent on producing machine-generated data seem to be going up.
I was referring to the creation of such data, but the growth rates of new creation and of persistent storage are likely, at least at this back-of-the-envelope level, to be similar.
Anecdotal evidence actually suggests 50-60%+ growth rates, so >40% seemed like a responsible claim.
- My recent survey of machine-generated data topics started with a list of many different kinds of the stuff.
- My 2009 post on data warehouse volume growth makes similar points, and notes that high growth rates mean we likely can never afford to keep all machine-generated data permanently.
- My 2011 claim that traditional databases will migrate into RAM is sort of this argument’s flipside.
What will soft, mobile robots be able to do that previous generations cannot? A lot. But I’m particularly intrigued by two large categories:
- Inspection, maintenance and repair.
- Health care/family care assistance.
There are still many things that are hard for humans to keep in good working order, including:
- Power lines.
- Anything that’s underwater (cables, drilling platforms, etc.)
- Pipelines, ducts, and water mains (especially from the inside).
- Any kind of geographically remote power station or other installation.
Sometimes the issue is (hopefully minor) repairs. Sometimes it’s cleaning or lubrication. In some cases one might want to upgrade a structure with fixed sensors, and the “repair” is mainly putting those sensors in place. In all these cases, it seems that soft robots could eventually offer a solution. Further examples, I’m sure, could be found in factories, mines, or farms.
Of course, if there’s a maintenance/repair need, inspection is at least part of the challenge; in some cases it’s almost the whole thing. And so this technology will help lead us toward the point that substantially all major objects will be associated with consistent flows of data. Opportunities for data analysis will abound.