Analysis of cloud computing, especially as applied to database management and analytics. Related subjects include:
It seems reasonable to wonder whether analytic data management is headed for the cloud. In no particular order:
- Amazon Redshift appears to be prospering.
- So are some SaaS (Software as a Service) business intelligence vendors.
- Amazon Elastic MapReduce is still around.
- Snowflake Computing launched with a cloud strategy.
- Cazena, with vague intentions for cloud data warehousing, destealthed.*
- Cloudera made various cloud-related announcements.
- Data is increasingly machine-generated, and machine-generated data commonly originates off-premises.
- The general argument for cloud-or-at-least-colocation has compelling aspects.
- Analytic workloads can be “bursty”, and so could benefit from true cloud elasticity.
I talked with the Snowflake Computing guys Friday. For starters:
- Snowflake is offering an analytic DBMS on a SaaS (Software as a Service) basis.
- The Snowflake DBMS is built from scratch (as opposed, to for example, being based on PostgreSQL or Hadoop).
- The Snowflake DBMS is columnar and append-only, as has become common for analytic RDBMS.
- Snowflake claims excellent SQL coverage for a 1.0 product.
- Snowflake, the company, has:
- 50 people.
- A similar number of current or past users.
- 5 referenceable customers.
- 2 techie founders out of Oracle, plus Marcin Zukowski.
- Bob Muglia as CEO.
Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*
*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.
In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:
- Ingest formats are JSON, XML or AVRO for now.
- I gather that the system automagically decides which fields/attributes are sufficiently repeated to be broken out as separate columns; also, there’s a column for the documents themselves.
I don’t know enough details to judge whether I’d call that an example of schema-on-need.
A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more
|Categories: Amazon and its cloud, Cloud computing, Data mart outsourcing, Data models and architecture, Data warehousing, Market share and customer counts, Parallelization, Pricing, Software as a Service (SaaS), Structured documents||1 Comment|
- 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.
1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?
2. A few thoughts on cloud, colocation, etc.:
- The economies of scale of colocation-or-cloud over operating your own data center are compelling. Most of the reasons you outsource hardware manufacture to Asia also apply to outsourcing data center operation within the United States. (The one exception I can think of is supply chain.)
- The arguments for cloud specifically over colocation are less persuasive. Colo providers can even match cloud deployments in rapid provisioning and elastic pricing, if they so choose.
- Surely not coincidentally, I am told that Rackspace is deemphasizing cloud, reemphasizing colocation, and making a big deal out of Open Compute. In connection with that, Rackspace has pulled back from its leadership role in OpenStack.
- I’m hearing much more mention of Amazon Redshift than I used to. It seems to have a lot of traction as a simple and low-cost option.
- I’m hearing less about Elastic MapReduce than I used to, although I imagine usage is still large and growing.
- In general, I get the impression that progress is being made in overcoming the inherent difficulties in cloud (and even colo) parallel analytic processing. But it all still seems pretty vague, except for the specific claims being made for traction of Redshift, EMR, and so on.
- Teradata recently told me that in colocation pricing, it is common for floor space to be everything, with power not separately metered. But I don’t think that trend is a big deal, as it is not necessarily permanent.
- Cloud hype is of course still with us.
- Other than the above, I stand by my previous thoughts on appliances, clusters and clouds.
3. As for the analytic DBMS industry: Read more
As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.
DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.
In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.
Buyer inertia is a greater concern.
- A significant minority of enterprises are highly committed to their enterprise DBMS standards.
- Another significant minority aren’t quite as committed, but set pretty high bars for new DBMS products to cross nonetheless.
- FUD (Fear, Uncertainty and Doubt) about new DBMS is often justifiable, about stability and consistent performance alike.
A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.
- First the newer products (from Netezza onwards) were sold to organizations who knew they wanted great performance or price/performance.
- Then it became more about selling “business value” to organizations who needed more convincing about the benefits of great price/performance.
- Then the behemoth vendors became more competitive, as Teradata introduced lower-price models, Oracle introduced Exadata, Sybase got more aggressive with Sybase IQ, IBM bought Netezza, EMC bought Greenplum, HP bought Vertica and so on. It is now hard for a non-behemoth analytic RDBMS vendor to make headway at large enterprise accounts.
- Meanwhile, Hadoop has emerged as serious competitor for at least some analytic data management, especially but not only at internet companies.
Otherwise I’d say: Read more
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
I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:
- An actual automation and management user interface, as opposed to the current management style, which is almost entirely via scripts (except for the monitoring UI).
- That’s scheduled for public beta in May, and general availability later this year.
- It will include some kind of integrated provisioning with VMware, OpenStack, et al.
- One goal is to let you apply database changes, software upgrades, etc. without taking the cluster down.
- A reasonable backup strategy.
- A snapshot copy is made of the database.
- A copy of the log is streamed somewhere.
- Periodically — the default seems to be 6 hours — the log is applied to create a new current snapshot.
- For point-in-time recovery, you take the last snapshot prior to the point, and roll forward to the desired point.
- A reasonable locking strategy!
- Document-level locking is all-but-promised for MongoDB 2.8.
- That means what it sounds like. (I mention this because sometimes an XML database winds up being one big document, which leads to confusing conversations about what’s going on.)
- Security. My eyes glaze over at the details, but several major buzzwords have been checked off.
- A general code rewrite to allow for (more) rapid addition of future features.
In 1981, Gerry Chichester and Vaughan Merlyn did a user-survey-based report about transaction-oriented fourth-generation languages, the leading application development technology of their day. The report included top-ten lists of important features during the buying cycle and after implementation. The items on each list were very similar — but the order of the items was completely different. And so the report highlighted what I regard as an eternal truth of the enterprise software industry:
What users value in the product-buying process is quite different from what they value once a product is (being) put into use.
Here are some thoughts about how that comes into play today.
Wants outrunning needs
1. For decades, BI tools have been sold in large part via demos of snazzy features the CEO would like to have on his desk. First it was pretty colors; then it was maps; now sometimes it’s “real-time” changing displays. Other BI features, however, are likely to be more important in practice.
2. In general, the need for “real-time” BI data freshness is often exaggerated. If you’re a human being doing a job that’s also often automated at high speed — for example network monitoring or stock trading — there’s a good chance you need fully human real-time BI. Otherwise, how much does a 5-15 minute delay hurt? Even if you’re monitoring website sell-through — are your business volumes really high enough that 5 minutes matters much? eBay answered “yes” to that question many years ago, but few of us work for businesses anywhere near eBay’s scale.
Even so, the want for speed keeps growing stronger.
3. Similarly, some desires for elastic scale-out are excessive. Your website selling koi pond accessories should always run well on a single server. If you diversify your business to the point that that’s not true, you’ll probably rewrite your app by then as well.
4. Some developers want to play with cool new tools. That doesn’t mean those tools are the best choice for the job. In particular, boring old SQL has merits — such as joins! — that shiny NoSQL hasn’t yet replicated.
5. Some developers, on the other hand, want to keep using their old tools, on which they are their employers’ greatest experts. That doesn’t mean those tools are the best choice for the job either.
6. More generally, some enterprises insist on brand labels that add little value but lots of expense. Yes, there are many benefits to vendor consolidation, and you may avoid many headaches if you stick with not-so-cutting-edge technology. But “enterprise-grade” hardware failure rates may not differ enough from “consumer-grade” ones to be worth paying for.
|Categories: Benchmarks and POCs, Business intelligence, Cloud computing, Clustering, Data models and architecture, Data warehousing, NoSQL, Software as a Service (SaaS), Vertica Systems||3 Comments|
The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.
Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing.
The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.
Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.
*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.
WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.
Disclosure: My fingerprints are all over that deal.
In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.
I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.
I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.
I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.
*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.
Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:
- SaaS has been around for over half a century, and at times has been the dominant mode of application delivery.
- The term multi-tenancy is being used in several different ways.
- Multi-tenancy, in the purest sense, is inessential to SaaS. It’s simply an implementation choice that has certain benefits for the SaaS provider. And by the way, …
- … salesforce.com, the chief proponent of the theory that true multi-tenancy is the hallmark of true SaaS, abandoned that position this week.
- Internet-based services are commonly, if you squint a little, SaaS. Examples include but are hardly limited to Google, Twitter, Dropbox, Intuit, Amazon Web Services, and the company that hosts this blog (KnownHost).
- Some of the core arguments for SaaS’ rise, namely the various efficiencies of data center outsourcing and scale, apply equally to the public cloud, to SaaS, and to AEaaS (Anything Else as a Service).
- These benefits are particularly strong for inherently networked use cases. For example, you really don’t want to be hosting your website yourself. And salesforce.com got its start supporting salespeople who worked out of remote offices.
- In theory and occasionally in practice, certain SaaS benefits, namely the outsourcing of software maintenance and updates, could be enjoyed on-premises as well. Whether I think that could be a bigger deal going forward will be explored in future posts.
For smaller enterprises, the core outsourcing argument is compelling. How small? Well:
- What’s the minimum level of IT operations headcount needed for mission-critical systems? Let’s just say “several”.
- What does that cost? Fully burdened, somewhere in the six figures.
- What fraction of the IT budget should such headcount be? As low a double digit percentage as possible.
- What fraction of revenues should be spent on IT? Some single-digit percentage.
So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*
|Categories: Amazon and its cloud, Buying processes, Cloud computing, Data mart outsourcing, Data warehouse appliances, Data warehousing, Infobright, Netezza, Pricing, salesforce.com, Software as a Service (SaaS), Workday||4 Comments|