Analysis of cloud computing, especially as applied to database management and analytics. Related subjects include:
I must start by apologizing for giving a quote in a press release whose contents I deplore. Unlike occasions on which I’ve posted about inaccurate quotes, in this case the fault is mine. The quote is quite accurate. And NuoDB didn’t mislead me about the release’s contents; I just neglected to ask.
NuoDB evidently subscribes to the marketing fallacy:
- Big DBMS companies hit people repeatedly with marketing cudgels.
- We want to be a big DBMS company.
- Therefore we will hit people repeatedly with marketing cudgels too.
But to my taste, NuoDB’s worst travesty is not the deafening drumroll before launch (I asked off their mailing list months before), nor the claim that NuoDB’s launch would be a “big day” for the database industry (annoying but ordinary hype), nor the emergent flock of birds foofarah, nor even NuoDB’s overwrought benchmark marketing (distressingly many vendors do that).
Rather, I think NuoDB’s greatest marketing offense to date is its Codd-imitating “12 rules” for cloud database management. Read more
NuoDB has an interesting NewSQL story. NuoDB’s core design goals seem to be:
- Very flexible topology, including:
- Local replicas.
- Remote replicas.
- Easy deployment and management.
GenieDB is one of the newer and smaller NewSQL companies. GenieDB’s story is focused on wide-area replication and uptime, coupled to claims about ease and the associated low TCO (Total Cost of Ownership).
GenieDB is in my same family of clients as Cirro.
The GenieDB product is more interesting if we conflate the existing GenieDB Version 1 and a soon-forthcoming (mid-year or so) Version 2. On that basis:
- GenieDB has three tiers.
- GenieDB’s top tier is the usual MySQL front-end.
- GenieDB’s bottom tier is either Berkeley DB or a conventional MySQL storage engine.
- GenieDB’s bottom tier stores your entire database at every node.
- If you replicate locally, GenieDB’s middle tier operates a distributed cache.
- If you replicate wide-area, GenieDB’s middle tier allows active-active/multi-master replication.
The heart of the GenieDB story is probably wide-area replication. Specifics there include: Read more
|Categories: Cache, Cloud computing, Clustering, GenieDB, Market share and customer counts, MySQL, NewSQL||4 Comments|
Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:
- Amazon Redshift is missing parts of ParAccel, notably the extensibility framework.
- ParAccel did some engineering to make its DBMS run better in the cloud.
- Amazon did some engineering in the areas it knows better than ParAccel — cloud provisioning, cloud billing, and so on.
“We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.
The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.
So who should and will use Amazon Redshift? For starters, I’d say: Read more
|Categories: Amazon and its cloud, Business intelligence, Cloud computing, Data mart outsourcing, Data warehousing, Infobright, ParAccel, Predictive modeling and advanced analytics, Pricing, Vertica Systems||5 Comments|
In connection with Amazon’s Redshift announcement, ParAccel reached out, and so I talked with them for the first time in a long while. At the highest level:
- ParAccel now has 60+ customers, up from 30+ two years ago and 40ish soon thereafter.
- ParAccel is now focusing its development and marketing on analytic platform capabilities more than raw database performance.
- ParAccel is focusing on working alongside other analytic data stores — relational or Hadoop — rather than supplanting them.
There wasn’t time for a lot of technical detail, but I gather that the bit about working alongside other data stores:
- Is relatively new.
- Works via SELECT statements that reach out to the other data stores.
- Is called “on-demand integration”.
- Is built in ParAccel’s extensibility/analytic platform framework.
- Uses HCatalog when reaching into Hadoop.
Also, it seems that ParAccel:
- Is in the early stages of writing its own analytic functions.
- Bundles Fuzzy Logix and actually has some users for that.
|Categories: Amazon and its cloud, Cloud computing, Data warehousing, Hadoop, Market share and customer counts, ParAccel, Predictive modeling and advanced analytics, Specific users||5 Comments|
I chatted with Todd Papaioannou about his new company Continuuity. Todd is as handy at combining buzzwords as he is at concatenating vowels, and so Continuuity — with two “U”s — is making a big data fabric platform as a service with REST APIs that runs over Hadoop and HBase in the private or public clouds. I found the whole thing confusing, in that:
- I recoil against buzzwords. In particular …
- … I pay as little attention to distinctions among PaaS/IaaS/WaaS — Platform/Infrastructure/Whatever as a Service — as I can.
- The Continuuity story sounds Heroku-like, but Todd doesn’t want Continuuity compared to Heroku.
- Todd does want Continuuity discussed in terms of the application server category, but:
- It is hard to discuss app servers without segueing quickly amongst development, deployment, and data connectivity, and Continuuity is no exception to that rule.
- There is doubt as to whether using app servers makes any sense.
But all confusion aside, there are some interesting aspects to Continuuity. Read more
|Categories: Application servers, Cloud computing, Hadoop, HBase, MapReduce, Parallelization, Predictive modeling and advanced analytics, Software as a Service (SaaS)||6 Comments|
I’m not at Oracle OpenWorld, but as usual that won’t keep me from commenting. My bottom line on the first night’s announcements is:
- At many large enterprises, Oracle has a lock on much of their IT efforts. (But not necessarily in the internet or investigative analytics areas.) Tonight’s announcements serve to strengthen that.
- Tonight’s announcements do little to help Oracle in other market segments.
1. At the highest level, my view of Oracle’s strategy is the same as it’s been for several years:
Clayton Christensen’s The Innovator’s Solution teaches us that Oracle should focus on selling a thick stack of technology to its highest-end customers, and that’s exactly what Oracle does focus on.
2. Tonight’s news is closely in line with what Oracle’s Juan Loaiza told me three years ago, especially:
- Oracle thinks flash memory is the most important hardware technology of the decade, one that could lead to Oracle being “bumped off” if they don’t get it right.
- Juan believes the “bulk” of Oracle’s business will move over to Exadata-like technology over the next 5-10 years. Numbers-wise, this seems to be based more on Exadata being a platform for consolidating an enterprise’s many Oracle databases than it is on Exadata running a few Especially Big Honking Database management tasks.
3. Oracle is confusing people with its comments on multi-tenancy. I suspect:
- What Oracle is talking about when it says “multi-tenancy” is more like consolidation than true multi-tenancy.
- Probably there are a couple of true multi-tenancy features as well.
4. SaaS (Software as a Service) vendors don’t want to use Oracle, because they don’t want to pay for it.* This limits the potential impact of Oracle’s true multi-tenancy features. Even so: Read more
|Categories: Business intelligence, Cloud computing, Columnar database management, Data warehouse appliances, Data warehousing, Exadata, Memory-centric data management, Oracle, Software as a Service (SaaS), Solid-state memory, Storage||9 Comments|
I successfully resisted telephone consulting while on vacation, but I did do some by email. One was on the oft-recurring subject of Hadoop adoption. I think it’s OK to adapt some of that into a post.
Notes on past and current Hadoop adoption include:
- Enterprise Hadoop adoption is for experimental uses or departmental production (as opposed to serious enterprise-level production). Indeed, it’s rather tough to disambiguate those two. If an enterprise uses Hadoop to search for new insights and gets a few, is that an experiment that went well, or is it production?
- One of the core internet-business use cases for Hadoop is a many-step ETL, ELT, and data refinement pipeline, with Hadoop executing some or many of the steps. But I don’t think that’s in production at many enterprises yet, except in the usual forward-leaning sectors of financial services and (we’re all guessing) national intelligence.
- In terms of industry adoption:
- Financial services on the investment/trading side are all over Hadoop, just as they’re all over any technology. Ditto national intelligence, one thinks.
- Consumer financial services, especially credit card, are giving Hadoop a try too, for marketing and/or anti-fraud.
- I’m sure there’s some telecom usage, but I’m hearing of less than I thought I would. Perhaps this is because telcos have spent so long optimizing their data into short, structured records.
- Whatever consumer financial services firms do, retailers do too, albeit with smaller budgets.
Thoughts on how Hadoop adoption will look going forward include: Read more
|Categories: Cloud computing, Data warehouse appliances, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Investment research and trading, Telecommunications||3 Comments|
It feels like time to write about Clustrix, which I last covered in detail in May, 2010, and which is releasing Clustrix 4.0 today. Clustrix and Clustrix 4.0 basics include:
- Clustrix makes a short-request processing appliance.
- As you might guess from the name, Clustrix is clustered — peer-to-peer, with no head node.
- The Clustrix appliance uses flash/solid-state storage.
- Traditionally, Clustrix has run a MySQL-compatible DBMS.
- Clustrix 4.0 introduces JSON support. More on that below.
- Clustrix 4.0 introduces a bunch of administrative features, and parallel backup.
- Also in today’s announcement is a Rackspace partnership to offer Clustrix remotely, at monthly pricing.
- Clustrix has been shipping product for about 4 years.
- Clustrix has 20 customers in production, running >125 Clustrix nodes total.
- Clustrix has 60 people.
- List price for a (smallest size) Clustrix system is $150K for 3 nodes. Highest-end maintenance costs 15%.
- There’s also a $100K version meant for high availability/disaster recovery. Over half of Clustrix’s customers use off-site disaster recovery.
- Clustrix is raising a C round. Part of it has already been raised from insiders, as a kind of bridge.
The biggest Clustrix installation seems to be 20 nodes or so. Others seem to have 10+. I presume those disaster recovery customers have 6 or more nodes each. I’m not quite sure how the arithmetic on that all works; perhaps the 125ish count of nodes is a bit low.
Clustrix technical notes include: Read more
|Categories: Cloud computing, Clustering, Clustrix, Database compression, Market share and customer counts, MySQL, OLTP, Pricing, Structured documents||4 Comments|
This is part of a three-post series:
The canonical Metamarkets batch ingest pipeline is a bit complicated.
- Data lands on Amazon S3 (uploaded or because it was there all along).
- Metamarkets processes it, primarily via Hadoop and Pig, to summarize and denormalize it, and then puts it back into S3.
- Metamarkets then pulls the data into Hadoop a second time, to get it ready to be put into Druid.
- Druid is notified, and pulls the data from Hadoop at its convenience.
By “get data read to be put into Druid” I mean:
- Build the data segments (recall that Druid manages data in rather large segments).
- Note metadata about the segments.
That metadata is what goes into the MySQL database, which also retains data about shards that have been invalidated. (That part is needed because of the MVCC.)
By “build the data segments” I mean:
- Make the sharding decisions.
- Arrange data columnarly within shard.
- Build a compressed bitmap for each shard.
When things are being done that way, Druid may be regarded as comprising three kinds of servers: Read more