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:
Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.
Discussions of DBMS performance are always odd, for starters because:
- Workloads and use cases vary greatly.
- In particular, benchmarks such as the YCSB or TPC-H aren’t very helpful.
- It’s common for databases or at least working sets to be entirely in RAM — but it’s not always required.
- Consistency and durability models vary. What’s more, in some systems — e.g. MongoDB — there’s considerable flexibility as to which model you use.
- In particular, there’s an increasingly common choice in which data is written synchronously to RAM on 2 or more servers, then asynchronously to disk on each of them. Performance in these cases can be quite different from when all writes need to be committed to disk. Of course, you need sufficient disk I/O to keep up, so SSDs (Solid-State Drives) can come in handy.
- Many workloads are inherently single node (replication aside). Others are not.
MongoDB and 10gen
I caught up with Ron Avnur at 10gen. Technical highlights included: Read more
Well-resourced Silicon Valley start-ups typically announce their existence multiple times. Company formation, angel funding, Series A funding, Series B funding, company launch, product beta, and product general availability may not be 7 different “news events”, but they’re apt to be at least 3-4. Platfora, no exception to this rule, is hitting general availability today, and in connection with that I learned a bit more about what they are up to.
In simplest terms, Platfora offers exploratory business intelligence against Hadoop-based data. As per last weekend’s post about exploratory BI, a key requirement is speed; and so far as I can tell, any technological innovation Platfora offers relates to the need for speed. Specifically, I drilled into Platfora’s performance architecture on the query processing side (and associated data movement); Platfora also brags of rendering 100s of 1000s of “marks” quickly in HTML5 visualizations, but I haven’t a clue as to whether that’s much of an accomplishment in itself.
Platfora’s marketing suggests it obviates the need for a data warehouse at all; for most enterprises, of course, that is a great exaggeration. But another dubious aspect of Platfora marketing actually serves to understate the product’s merits — Platfora claims to have an “in-memory” product, when what’s really the case is that Platfora’s memory-centric technology uses both RAM and disk to manage larger data marts than could reasonably be fit into RAM alone. Expanding on what I wrote about Platfora when it de-stealthed: Read more
|Categories: Business intelligence, Columnar database management, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Market share and customer counts, Memory-centric data management, Platfora, Workload management||11 Comments|
Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times.
Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:
- Every level of storage (disk, RAM, etc.).
- Indexes, aggregates and raw data alike.
To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more
It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.
Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:
- “We get data into a form in which it can be analyzed.” This is the story behind, among others:
- Most of the data integration and ETL (Extract/Transform/Load) industries, software vendors and consulting firms alike.
- Many things that purport to be “analytic applications” or data warehouse “quick starts”.
- “Data reduction” use cases in event processing.*
- Text analytics tools.
- “Forget all that transformation foofarah — just load (or write) data into our thing and start analyzing it immediately.” This at various times has been much of the story behind:
- Relational DBMS, according to their inventor E. F. Codd.
- MOLAP (Multidimensional OnLine Analytic Processing), also according to RDBMS inventor E. F. Codd.
- Any kind of analytic DBMS, or general purpose DBMS used for data warehousing.
- Newer kinds of analytic DBMS that are faster than older kinds.
- The “data mart spin-out” feature of certain analytic DBMS.
- In-memory analytic data stores.
- NoSQL DBMS that have a few analytic features.
- TokuDB, similarly.
- Electronic spreadsheets, from VisiCalc to Datameer.
- “Our tools help you with specific kinds of analyses or analytic displays.” This is the story underlying, among others:
- The business intelligence industry.
- The predictive analytics industry.
- Algorithmic trading use cases in complex event processing.*
- Some analytic applications.
*Complex event/stream processing terminology is always problematic.
My thoughts on all this start: Read more
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.
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
The 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems is out. I’ll split my comments into two posts — this one on concepts, and a companion on specific vendor evaluations.
- Maintaining working links to Gartner Magic Quadrants is an adventure. But as of early February, 2013, this link seems live.
- I also commented on the 2011, 2010, 2009, 2008, 2007, and 2006 Gartner Magic Quadrants for Data Warehouse DBMS.
Let’s start by again noting that I regard Gartner Magic Quadrants as a bad use of good research. On the facts:
- Gartner collects a lot of input from traditional enterprises. I envy that resource.
- Gartner also does a good job of rounding up vendor claims about user base sizes and the like. If nothing else, you should skim the MQ report for that reason.
- Gartner observations about product feature sets are usually correct, although not so consistently that they should be relied on.
When it comes to evaluations, however, the Gartner Data Warehouse DBMS Magic Quadrant doesn’t do as well. My concerns (which overlap) start:
- The Gartner MQ conflates many different use cases into one ranking (inevitable in this kind of work, but still regrettable).
- A number of the MQ vendor evaluations seem hard to defend. So do some of Gartner’s specific comments.
- Some of Gartner’s criteria seemingly amount to “parrots back our opinions to us”.
- As do I, Gartner thinks a vendor’s business and financial strength are important. But Gartner overdoes the matter, drilling down into picky issues it can’t hope to judge, such as assessing a vendor’s “ability to generate and develop leads.” *
- The 2012 Gartner Data Warehouse DBMS Magic Quadrant is closer to being a 1-dimensional ranking than 2-dimensional, in that entries are clustered along the line x=y. This suggests strong correlation among the results on various specific evaluation criteria.
|Categories: Data integration and middleware, Data warehousing, Database compression, Emulation, transparency, portability, Hadoop, Market share and customer counts, Oracle, Text||5 Comments|
I’ve hacked both the PHP and CSS that drive this website. But if I had to write PHP or CSS from scratch, I literally wouldn’t know how to begin.
Something similar, I suspect, is broadly true of “business analysts.” I don’t know how somebody can be a competent business analyst without being able to generate, read, and edit SQL. (Or some comparable language; e.g., there surely are business analysts who only know MDX.) I would hope they could write basic SELECT statements as well.
But does that mean business analysts are comfortable with the fancy-schmantzy extended SQL that the analytic platform vendors offer them? I would assume that many are but many others are not. And thus I advised such a vendor recently to offer sample code, and lots of it — dozens or hundreds of isolated SQL statements, each of which does a specific task.* A business analyst could reasonably be expected to edit any of those to point them his own actual databases, even though he can’t necessarily be expected to easily write such statements from scratch. Read more
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||4 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|