Greenplum
Analysis of data warehouse DBMS vendor Greenplum and its successor, EMC’s Data Computing division. Related subjects include:
- EMC, which bought Greenplum in 2010
- Data warehousing
- Data warehouse appliances
- PostgreSQL
Juggling analytic databases
I’d like to survey a few related ideas:
- Enterprises should each have a variety of different analytic data stores.
- Vendors — especially but not only IBM and Teradata — are acknowledging and marketing around the point that enterprises should each have a number of different analytic data stores.
- In addition to having multiple analytic data management technology stacks, it is also desirable to have an agile way to spin out multiple virtual or physical relational data marts using a single RDBMS. Vendors are addressing that need.
- Some observers think that the real essence of analytic data management will be in data integration, not the actual data management.
Here goes. Read more
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Comments on the 2012 Forrester Wave: Enterprise Hadoop Solutions
Forrester has released its Q1 2012 Forrester Wave: Enterprise Hadoop Solutions. (Googling turns up a direct link, but in case that doesn’t prove stable, here also is a registration-required link from IBM’s Conor O’Mahony.) My comments include:
- The Forrester Wave’s relative vendor rankings are meaningless, in that the document compares apples, peaches, almonds, and peanuts. Apparently, it covers any vendor that includes a distribution of Apache Hadoop MapReduce into something it offers, and that offered at least two (not necessarily full production) references for same.
- The Forrester Wave for “enterprise Hadoop” contradicts itself on the subject of Hortonworks.
- The Forrester Wave for “enterprise Hadoop” is correct when it says “Hortonworks … has Hadoop training and professional services offerings that are still embryonic.”
- Peculiarly, the Forrester Wave for “enterprise Hadoop” also says “Hortonworks offers an impressive Hadoop professional services portfolio”. Hortonworks will likely win one or more nice partnership deals with vendors in adjacent fields, but even so its professional services capabilities are … well, a good word might be “embryonic”.
- Forrester Waves always seem to have weird implicit definitions of “data warehousing”. This one is no exception.
- Forrester gave top marks in “Functionality” to 11 of 13 “enterprise Hadoop” vendors. This seems odd.
- I don’t know why MapR, which doesn’t like HDFS (Hadoop Distributed File System), got top marks in “Subproject integration”.
- Forrester gave top marks in “Storage” to Datameer. It also gave higher marks to MapR than to EMC Greenplum, even though EMC Greenplum’s technology is a superset of MapR’s. Very strange. (Edit: Actually, as per a comment below, there is some uncertainty about the EMC/MapR relationship.)
- Forrester gave higher marks in “Acceleration and optimization” to Hortonworks than to Cloudera and IBM, and higher marks yet to Pentaho. Very odd.
- I’m not sure what Forrester is calling a “Distributed EDW file store connector”, but it sounds like something that Cloudera has provided via partnership to a number of analytic DBMS vendors.
- Forrester’s “Strategy” rankings seem to correlate to a metric of “We’re a large enough vendor to go in N directions at once”, for various values of N.
- Forrester is correct to rank Cloudera’s “Adoption” as being stronger than EMC/Greenplum’s or MapR’s. But Hortonworks’ strong mark for “Adoption” baffles me.
| Categories: Cloudera, Data warehousing, EMC, Greenplum, Hadoop, Hortonworks, MapR, MapReduce, Pentaho | 11 Comments |
Hope for a new PostgreSQL era?
In a comedy of briefing errors, I’m not too clear on the details of my client salesforce.com’s new PostgreSQL-as-a-service offering, nor exactly on what my clients at VMware are bringing to the PostgreSQL virtualization/cloud party. That said:
- PostgreSQL is good technology.
- MySQL is narrowing the gap, but PostgreSQL is still ahead of MySQL in some ways. (Database extensibility if nothing else.)
- PostgreSQL has a lot of users. (Many of them in academia and/or Russia.)
- Neither EnterpriseDB (which now calls itself “The enterprise PostgreSQL company”) nor the PostgreSQL community leadership have covered themselves with stewardship glory.
- A significant number of interesting DBMS products can be regarded as PostgreSQL forks (e.g. Greenplum, Aster Data nCluster, Netezza if you squint, and Vertica if you stand on your head*).
- PostgreSQL advancement is not dead. For example, Hadapt beta users are running actual PostgreSQL on many nodes each.
- There’s no assurance that Oracle will be a benevolent MySQL steward forever. (Specifically, Oracle’s “Play nicely with others” antitrust commitments expire in 2014.)
So I think it would be cool if one or the other big company put significant wood behind the PostgreSQL arrow.
*While Vertica was originally released using little or no PostgreSQL code — reports varied — it featured high degrees of PostgreSQL compatibility.
| Categories: Aster Data, EnterpriseDB and Postgres Plus, Greenplum, MySQL, Netezza, Open source, salesforce.com, Vertica Systems | 8 Comments |
Analytic trends in 2012: Q&A
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
Highlights of a busy news week
I put up 14 posts over the past week, so perhaps you haven’t had a chance yet to read them all. 🙂 Highlights included:
- My most important post of the week was a general guide to IT vendor strategy. That one has already spawned discussion at many companies, from the tiny to the multi-billion-dollar.
- The best comment thread of the week was probably on my post about scale-out relational OLTP choices, in which people discussed the merits of various particular alternatives.
- I recommended that people strongly consider attending XLDB 5 in Menlo Park on October 18-19.
Most of the posts, however, were reactions to news events. In particular:
- Teradata announced that Teradata 14 will be hybrid-columnar, more in Vertica’s way than in Greenplum’s or Aster Data’s. (Pay no attention to the Wall Street Journal’s apparent belief that no other analytic DBMS is hybrid-columnar at all.)
- Aster announced the unsurprising news that there will be a Teradata Aster appliance. Also, Aster talked about greater analytic flexibility in the forthcoming Aster 5.0.
- With Oracle OpenWorld coming up, Oracle decided to get some of its announcing out of the way early. In particular, it announced the Oracle Database Appliance, which is small-business-friendly hardware for running the Oracle DBMS. However, the Oracle Database Appliance doesn’t seem to do much about the complexity of running the Oracle DBMS software.
- In a catch-all Hadoop post, I noted that:
- Oracle has now clearly said it has a Hadoop appliance coming, no doubt next week at OpenWorld.
- I still can’t see why Hadoop appliances would succeed, but a lot of smart folks seem to disagree with me.
- Greenplum announced what looks like a nice but unimportant little product upgrade.
- It’s a really good thing that previously reported plans to revamp Hadoop are underway.
- DataStax announced that it really is a Cassandra company after all. Pay no attention to previous marketing that seemed to put DataStax in the same Hadoop-alternative category as, say, MapR.
- Ingres has changed its name to Actian. The announcement seems like a confession that Ingres and VectorWise are going nowhere.
| Categories: Actian and Ingres, Aster Data, Data warehousing, DataStax, Greenplum, Hadoop, Teradata, VectorWise | Leave a Comment |
Some notes on Hadoop (mainly) and appliances
1. EMC Greenplum has evolved its appliance product line. As I read that, the latest announcement boils down to saying that you can neatly network together various Greenplum appliances in quarter-rack increments. If you take a quarter rack each of four different things, then Greenplum says “Hooray! Our appliance is all-in-one!” Big whoop.
2. That said, the Hadoop part of EMC ‘s story is based on MapR, which so far as I can tell is actually a pretty good Hadoop implementation. More precisely, MapR makes strong claims about performance and so on, and Apache Hadoop folks don’t reply “MapR is full of &#$!” Rather, they say “We’re going to close the gap with MapR a lot faster than the MapR folks like to think — and by the way, guys, thanks for the butt-kick.” A lot more precision about MapR may be found in this M. C. Srivas SlideShare.
3. On its latest earnings call, Oracle clearly said it would introduce a Hadoop appliance, versus just hinting at a Hadoop appliance the prior quarter. The money quote was: Read more
| Categories: Data warehouse appliances, eBay, EMC, Greenplum, Hadoop, MapR, MapReduce, Open source, Oracle | 2 Comments |
Hybrid-columnar soundbites
Busy couple of days talking with reporters. A few notes on hybrid-columnar analytic DBMS, all backed up by yesterday’s post on Teradata columnar:
- Oracle does not actually offer columnar I/O; the other three systems do. But see the “I won’t be surprised” part in yesterday’s Teradata post.
- Aster does not offer columnar compression; the other three do.
- EMC Greenplum and Teradata offer different kinds of ways to mix column and row storage in the same table; each has its advantages.
- Teradata generally has a more mature and capable offering than EMC Greenplum, for most purposes, whichever way you choose to organize your tables.
Edit: The Wall Street Journal got this wrong, writing that Teradata was the first-ever hybrid columnar system. Specifically, they wrote
While columnar technology has been around for years, Teradata says its product is unique because it allows users to include both columns and rows in the same database.
Googling on “Teradata To Unveil New Analytics Product To Speed Business Adoption” might get you around the paywall to see the offending piece.
| Categories: Aster Data, Columnar database management, Data warehousing, Database compression, Greenplum, Teradata | 2 Comments |
Data management at Zynga and LinkedIn
Mike Driscoll and his Metamarkets colleagues organized a bit of a bash Thursday night. Among the many folks I chatted with were Ken Rudin of Zynga, Sam Shah of LinkedIn, and D. J. Patil, late of LinkedIn. I now know more about analytic data management at Zynga and LinkedIn, plus some bonus stuff on LinkedIn’s People You May Know application. 🙂
It’s blindingly obvious that Zynga is one of Vertica’s petabyte-scale customers, given that Zynga sends 5 TB/day of data into Vertica, and keeps that data for about a year. (Zynga may retain even more data going forward; in particular, Zynga regrets ever having thrown out the first month of data for any game it’s tried to launch.) This is game actions, for the most part, rather than log files; true logs generally go into Splunk.
I don’t know whether the missing data is completely thrown away, or just stashed on inaccessible tapes somewhere.
I found two aspects of the Zynga story particularly interesting. First, those 5 TB/day are going straight into Vertica (from, I presume, memcached/Membase/Couchbase), as Zynga decided that sending the data to some kind of log first was more trouble than it’s worth. Second, there’s Zynga’s approach to analytic database design. Highlights of that include: Read more
| Categories: Aster Data, Couchbase, Data models and architecture, Games and virtual worlds, Greenplum, Hadoop, Petabyte-scale data management, Specific users, Vertica Systems, Zynga | 27 Comments |
Hadoop futures and enhancements
Hadoop is immature technology. As such, it naturally offers much room for improvement in both industrial-strengthness and performance. And since Hadoop is booming, multiple efforts are underway to fill those gaps. For example:
- Cloudera’s proprietary code is focused on management, set-up, etc.
- The “Phase 1” plans Hortonworks shared with me for Apache Hadoop are focused on industrial-strengthness, as are significant parts of “Phase 2”.*
- MapR tells a performance story versus generic Apache Hadoop HDFS and MapReduce. (One aspect of same is just C++ vs. Java.)
- So does Hadapt, but mainly vs. Hive.
- Cloudera also tells me there’s a potential 4-5X performance improvement in Hive coming down the pike from what amounts to an optimizer rewrite.
(Zettaset belongs in the discussion too, but made an unfortunate choice of embargo date.)
| Categories: Cloudera, Greenplum, Hadapt, Hadoop, HBase, MapR, MapReduce, Parallelization, Zettaset | 20 Comments |