Data warehouse appliances
Analysis of data warehouse appliances – i.e., of hardware/software bundles optimized for fast query and analysis of large volumes of (usually) relational data. Related subjects include:
- Data warehousing
- Parallelization
- Netezza
- DATAllegro
- Teradata
- Kickfire
- (in The Monash Report) Computing appliances in multiple domains
RDBMS and their bundle-mates
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.
Things I keep needing to say
Some subjects just keep coming up. And so I keep saying things like:
Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.
Most generalizations about Hadoop are false. Reasons include:
- Hadoop is a collection of disparate things, most particularly data storage and application execution systems.
- The transition from Hadoop 1 to Hadoop 2 will be drastic.
- For key aspects of Hadoop — especially file format and execution engine — there are or will be widely varied options.
Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.
Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.
Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)
Hortonworks, Hadoop, Stinger and Hive
I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger — but at my request we cycled through a bunch of other topics as well. Company-specific notes include:
- Hortonworks founder J. Eric “Eric14” Baldeschwieler is no longer at Hortonworks, although I imagine he stays closely in touch. What he’s doing next is unspecified, except by the general phrase “his own thing”. (Derrick Harris has more on Eric’s departure.)
- John Kreisa still is at Hortonworks, just not as marketing VP. Think instead of partnerships and projects.
- ~250 employees.
- ~70-75 subscription customers.
Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:
- 10ish nodes for a typical starting cluster.
- 100ish nodes for a typical “data lake” committed adoption.
- Teradata UDA (Unified Data Architecture)* customers sometimes (typically?) jumping straight to a data lake scenario.
- A few users in the 10s of 1000s of nodes. (Obviously Yahoo is one.)
- HBase used in >50% of installations.
- Hive probably even more than that.
- Hortonworks is seeing a fair amount of interest in Windows Hadoop deployments.
*By the way — Teradata seems serious about pushing the UDA as a core message.
Ecosystem notes, in Hortonworks’ perception, included:
- Cloudera is obviously Hortonworks’ biggest distro competitor. Next is IBM, presumably in its blue-forever installed base. MapR is barely on the radar screen; Pivotal’s likely rise hasn’t yet hit sales reports.
- Hortonworks evidently sees a lot of MicroStrategy and Tableau, and some Platfora and Datameer, the latter two at around the same level of interest.
- Accumulo is a big deal in the Federal government, and has gotten a few health care wins as well. Its success is all about security. (Note: That’s all consistent with what I hear elsewhere.)
I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.
Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:
- It’s been in preview/release candidate/commercial beta mode for weeks.
- Q3 is the goal; H2 is the emphatic goal.
- Yahoo’s been in production with YARN >8 months, and has no MapReduce 1 clusters left. (Yahoo has >35,000 Hadoop nodes.)
- The last months of delays have been mainly about sprucing up various APIs and protocols, which may need to serve for a similar multi-year period as Hadoop 1’s have. But there also was some YARN stabilization into May.
Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.
Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include: Read more
Notes on Teradata systems
Teradata is announcing its new high-end systems, the Teradata 6700 series. Notes on that include:
- Teradata tends to get 35-55% (roughly speaking) annual performance improvements, as measured by its internal blended measure Tperf. A big part of this is exploiting new-generation Intel processors.
- This year the figure is around 40%.
- The 6700 is based on Intel’s Sandy Bridge.
- Teradata previously told me that Ivy Bridge — the next one after Sandy Bridge — could offer a performance “discontinuity”. So, while this is just a guess, I expect that next year’s Teradata performance improvement will beat this year’s.
- Teradata has now largely switched over to InfiniBand.
Teradata is also talking about data integration and best-of-breed systems, with buzzwords such as:
- Teradata Unified Data Architecture.
- Fabric-based computing, even though this isn’t really about storage.
- Teradata SQL-H.
| Categories: Data integration and middleware, Data warehouse appliances, Data warehousing, Pricing, SAS Institute, Teradata | 3 Comments |
Appliances, clusters and clouds
I believe:
- The trend to clustered computing is sustainable.
- The trend to appliances is also sustainable.
- The “single” enterprise cluster is almost as much of a pipe dream as the single enterprise database.
I shall explain.
Arguments for hosting applications on some kind of cluster include:
- If the workload requires more than one server — well, you’re in cluster territory!
- If the workload requires less than one server — throw it into the virtualization pool.
- If the workload is uneven — throw it into the virtualization pool.
Arguments specific to the public cloud include:
- A large fraction of new third-party applications are SaaS (Software as a Service). Those naturally live in the cloud.
- Cloud providers have efficiencies that you don’t.
That’s all pretty compelling. However, these are not persuasive reasons to put everything on a SINGLE cluster or cloud. They could as easily lead you to have your VMware cluster and your Exadata rack and your Hadoop cluster and your NoSQL cluster and your object storage OpenStack cluster — among others — all while participating in several different public clouds as well.
Why would you not move work into a cluster at all? First, if ain’t broken, you might not want to fix it. Some of the cluster options make it easy for you to consolidate existing workloads — that’s a central goal of VMware and Exadata — but others only make sense to adopt in connection with new application projects. Second, you might just want device locality. I have a gaming-class PC next to my desk; it drives a couple of monitors; I like that arrangement. Away from home I carry a laptop computer instead. Arguments can be made for small remote-office servers as well.
| Categories: Cloud computing, Clustering, Data warehouse appliances, Exadata, NoSQL, Software as a Service (SaaS) | 8 Comments |
Hadoop distributions
Elephants! Elephants!
One elephant went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.
Elephants! Elephants!
Two elephants went out to play
Sat on a spider’s web one day.
They had such enormous fun
Called for another elephant to come.
Elephants! Elephants!
Three elephants went out to play
Etc.
— Popular children’s song
It’s Strata week, with much Hadoop news, some of which I’ve been briefed on and some of which I haven’t. Rather than delve into fine competitive details, let’s step back and consider some generalities. First, about Hadoop distributions and distro providers:
- Conceptually, the starting point for a “Hadoop distribution” is some version of Apache Hadoop.
- Hortonworks is still focused on Hadoop 1 (without YARN and so on), because that’s what’s regarded as production-ready. But Hortonworks does like HCatalog.
- Cloudera straddles Hadoop 1 and Hadoop 2, shipping aspects of Hadoop 2 but not recommending them for production use.
- Some of the newer distros seem to be based on Hadoop 2, if the markitecture slides are to be believed.
- Optionally, the version numbers of different parts of Hadoop in a distribution could be a little mismatched, if the distro provider takes responsibility for testing them together.
- Cloudera seems more willing to do that than Hortonworks.
- Different distro providers may choose different sets of Apache Hadoop subprojects to include.
- Cloudera seems particularly expansive in what it is apt to include. Perhaps not coincidentally, Cloudera folks started various Hadoop subprojects.
- Optionally, distro providers’ additional proprietary code can be included, to be used either in addition to or instead of Apache Hadoop code. (In the latter case, marketing can then ensue about whether this is REALLY a Hadoop distribution.)
- Hortonworks markets from a “more open source than thou” stance, even though:
- It is not a purist in that regard.
- That marketing message is often communicated by Hortonworks’ very closed-source partners.
- Several distro providers, notably Cloudera, offer management suites as a big part of their proprietary value-add. Hortonworks, however, is focused on making open-source Ambari into a competitive management tool.
- Performance is another big area for proprietary code, especially from vendors who look at HDFS (Hadoop Distributed File System) and believe they can improve on it.
- I conjecture packaging/installation code is often proprietary, but that’s a minor issue that doesn’t get mentioned much.
- Hortonworks markets from a “more open source than thou” stance, even though:
- Optionally, third parties’ code can be provided, open or closed source as the case may be.
Most of the same observations could apply to Hadoop appliance vendors.
| Categories: Cloudera, Data warehouse appliances, EMC, Greenplum, Hadoop, Hortonworks, IBM and DB2, Intel, MapR, Market share and customer counts | 5 Comments |
Key questions when selecting an analytic RDBMS
I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:
- How big is your database? How big is your budget?
- How do you feel about appliances?
- How do you feel about the cloud?
- What are the size and shape of your workload?
- How fresh does the data need to be?
Let’s drill down. Read more
Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations
To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.
Gartner seems confused about Kognitio’s products and history alike.
- Gartner calls Kognitio an “in-memory” DBMS, which is not accurate.
- Gartner doesn’t remark on Kognitio’s worst-in-class* compression.
- Gartner gives Kognitio oddly high marks for a late, me-too Hadoop integration strategy.
- Gartner writes as if Kognitio’s next attempt at the US market will be the first one, which is not the case.
- Gartner says that Kognitio pioneered data warehouse SaaS (Software as a Service), which actually has existed since the pre-relational 1970s.
Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.
* non-existent
In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more
Notes on Microsoft SQL Server
I’ve been known to gripe that covering big companies such as Microsoft is hard. Still, Doug Leland of Microsoft’s SQL Server team checked in for phone calls in August and again today, and I think I got enough to be worth writing about, albeit at a survey level only,
Subjects I’ll mention include:
- Hadoop
- Parallel Data Warehouse
- PolyBase
- Columnar data management
- In-memory data management (Hekaton)
One topic I can’t yet comment about is MOLAP/ROLAP, which is a pity; if anybody can refute my claim that ROLAP trumps MOLAP, it’s either Microsoft or Oracle.
Microsoft’s slides mentioned Yahoo refining a 6 petabyte Hadoop cluster into a 24 terabyte SQL Server “cube”, which was surprising in light of Yahoo’s history as an Oracle reference.
Notes on Hadoop hardware
I talked with Cloudera yesterday about an unannounced technology, and took the opportunity to ask some non-embargoed questions as well. In particular, I requested an update to what I wrote last year about typical Hadoop hardware.
Cloudera thinks the picture now is:
- 2-socket servers, with 4- or 6-core chips.
- Increasing number of spindles, with 12 2-TB spindles being common.
- 48 gigs of RAM is most common, with 64-96 fairly frequent.
- A couple of 1GigE networking ports.
Discussion around that included:
- Enterprises had been running out of storage space; hence the increased amount of storage. 🙂
- Even more storage can be stuffed on a node, and at times is. But at a certain point there’s so much data on a node that recovery from node failure is too forbidding.
- There are some experiments with 10 GigE.
| Categories: Cloudera, Data warehouse appliances, Hadoop, MapR, Solid-state memory, Storage | 7 Comments |