Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
There’s much confusion about Cloudera’s SQL plans and beliefs, and the company has mainly itself to blame. That said, here’s what I think is going on.
- Hive is good at some tasks and terrible at others.
- Hive is good at batch data transformation.
- Hive is bad at ad-hoc query, unless you really, really need Hive’s scale and low license cost. One example, per Eli Collins: Facebook has a 500 petabyte Hive warehouse, but jokes that on a good day an analyst can run 6 queries against it.
- Impala is meant to be good at what Hive is bad at – i.e., fast-response query. (Cloudera mentioned reliable 100 millisecond response times for at least one user.)
- Impala is also meant to be good at what Hive is good at, and will someday from Cloudera’s standpoint completely supersede Hive, but Cloudera is in no hurry for that day to arrive. Hive is more mature. Hive still has more SQL coverage than Impala. There’s a lot of legacy investment in Hive. Cloudera gets little business advantage if a customer sunsets Hive.
- Impala is already decent at some tasks analytic RDBMS are commonly used for. Cloudera insists that some queries run very quickly on Impala. I believe them.
- Impala is terrible at others, including some of the ones most closely associated with the concept of “data warehousing”. Data modeling is a big zero right now. Impala’s workload management, concurrency and all that are very immature.
- There are some use cases for which SQL-on-Hadoop blows away analytic RDBMS, for example ones involving data transformations – perhaps on multi-structured data – that are impractical in RDBMS.
And of course, as vendors so often do, Cloudera generally overrates both the relative maturity of Impala and the relative importance of the use cases in which its offerings – Impala or otherwise – shine.
- A survey of SQL/Hadoop integration (February, 2014)
- The cardinal rules of DBMS development (March, 2013)
|Categories: Cloudera, Data warehousing, Facebook, Hadoop, SQL/Hadoop integration, Workload management||4 Comments|
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.
For quite some time, one of the most frequent marketing pitches I’ve heard is “Analytics made easy for everybody!”, where by “quite some time” I mean “over 30 years”. “Uniquely easy analytics” is a claim that I meet with the greatest of skepticism.* Further confusing matters, these claims are usually about what amounts to business intelligence tools, but vendors increasingly say “Our stuff is better than the BI that came before, so we don’t want you to call it ‘BI’ as well.”
*That’s even if your slide deck doesn’t contain a picture of a pyramid of user kinds; if there actually is such a drawing, then the chance that I believe you is effectively nil.
All those caveats notwithstanding, there are indeed at least three forms of widespread analytics:
- Fairly standalone, eas(ier) to use business intelligence tools, sometimes marketed as focusing on “data exploration” or “data discovery”.
- Charts and graphs integrated or at least well-embedded into production applications. This technology is on a long-term rise. But in some sense, integrated reporting has been around since the invention of accounting.
- Predictive analytics built into automated systems, for example ad selection. This is not what is usually meant by the “easy analytics” claim, and I’ll say no more about it in this post.
It would be nice to say that the first two bullet points represent a fairly clean operational/investigative BI split, but that would be wrong; human real-time dashboards can at once be standalone and operational.
From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:
- Hadoop (always, and please see below).
- Analytic RDBMS (ditto).
- NoSQL and NewSQL.
- Specifically, SQL-on-Hadoop
- Spark and other memory-centric technology, including streaming.
- Public policy, mainly but not only in the area of surveillance/privacy.
- General strategic advice for all sizes of tech company.
Other stuff on my mind includes but is not limited to:
1. Certain categories of buying organizations are inherently leading-edge.
- Internet companies have adopted Hadoop, NoSQL, NewSQL and all that en masse. Often, they won’t even look at things that are conventional or expensive.
- US telecom companies have been buying 1 each of every DBMS on the market since pre-relational days.
- Financial services firms — specifically algorithmic traders and broker-dealers — have been in their own technical world for decades …
- … as have national-security agencies …
- … as have pharmaceutical research departments.
Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.
It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:
- Flex Zone, a schema-on-need technology very much like Hadapt’s (but of course with access to Vertica performance).
- What sounds like an alternate query execution capability for short-request queries, the big point of which is that it saves them from being broadcast across the whole cluster, hence improving scalability. (Adding nodes of course doesn’t buy you much for the portion of a workload that’s broadcast.)
Other Vertica 7 enhancements include:
- A lot of Bottleneck Whack-A-Mole.
- “Significant” improvements to the Vertica management console.
- Security enhancements (Kerberos), Hadoop integration enhancements (HCatalog), and enhanced integration with Hadoop security (Kerberos again).
- Some availability hardening. (“Fault groups”, which for example let you ensure that data is replicated not just to 2+ nodes, but also that the nodes aren’t all on the same rack.)
- Java as an option to do in-database analytics. (Who knew that feature was still missing?)
- Some analytic functionality. (Approximate COUNT DISTINCT, but not yet Approximate MEDIAN.)
Overall, two recurring themes in our discussion were:
- Load and ETL (Extract/Transform/Load) performance, and/or obviating ETL.
- Short-request performance, in the form of more scalable short-request concurrency.
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||5 Comments|
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
I’m a little shaky on embargo details — but I do know what was in my own quote in a Splunk press release that went out yesterday.
Splunk has been rolling out a lot of news. In particular:
- Hunk follows through on the Hadoop/Splunk (get it?) co-opetition I foreshadowed last year, including access to Hadoop via the same tools that run over the Splunk data store, plus …
- … some Datameer-like capabilities to view partial Hadoop-job results as they flow in.
- Splunk 6 has lots of new features, including a bunch of better please-don’t-call-it-BI capabilities, and …
- … a high(er)-performance data store into which you can selectively copy columns of data.
I imagine there are some operationally-oriented use cases for which Splunk instantly offers the best Hadoop business intelligence choice available. But what I really think is cool is Splunk’s schema-on-need story, wherein:
- Data comes in wholly schema-less, in a time series of text snippets.
- Some of the fields in the text snippets are indexed for faster analysis, automagically or upon user decree.
- All this can now happen over the Splunk data store or (new option) over Hadoop.
- Fields can (in another new option) also be copied to a separate data store, claimed to be of much higher performance.
That highlights a pretty serious and flexible vertical analytic stack. I like it.
|Categories: Business intelligence, Data models and architecture, Data warehousing, Hadoop, Schema on need, Splunk||2 Comments|
I coined the term schema-on-need last month. More precisely, I coined it while being briefed on JSON-in-Teradata, which was announced earlier this week, and is slated for availability in the first half of 2014.
The basic JSON-in-Teradata story is as you expect:
- A JSON document is stuck into a relational field.
(Oddly, Teradata wasn’t yet sure whether the field would be a BLOB or VARCHAR or something else.)Edit: See Dan Graham’s comment below.
- Fields within the JSON document can be indexed on.
- Those fields can be referenced in SQL statements much as regular Teradata columns can.
You have to retrieve the whole document.Edit: See Dan Graham’s comment below.
- To avert the performance pain of retrieving the whole document, you can of course copy any particular field into a column of its own. (That’s the schema-on-need part of the story.)
JSON virtual columns are referenced a little differently than ordinary physical columns are. Thus, if you materialize a virtual column, you have to change your SQL. If you’re doing business intelligence through a semantic layer, or otherwise have some kind of declarative translation, that’s probably not a big drawback. If you’re coding analytic procedures directly, it still may not be a big drawback — hopefully you won’t reference the virtual column too many times in code before you decide to materialize it instead.
My Bobby McFerrin* imitation notwithstanding, Hadapt illustrates a schema-on-need approach that is slicker than Teradata’s in two ways. First, Hadapt has full SQL transparency between virtual and physical columns. Second, Hadapt handles not just JSON, but anything represented by key-value pairs. Still, like XML before it but more concisely, JSON is a pretty versatile data interchange format. So JSON-in-Teradata would seem to be useful as it stands.
*The singer in the classic 1988 music video Don’t Worry Be Happy. The other two performers, of course, were Elton John and Robin Williams.
|Categories: Data models and architecture, Data warehousing, Hadapt, Schema on need, Structured documents, Teradata||3 Comments|