August 28, 2016

Are analytic RDBMS and data warehouse appliances obsolete?

I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:

Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.

My answer, in a nutshell, is:

Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud — are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.

To see why, let’s start by asking: “With what do you want to integrate your analytic SQL processing?”

Beyond those cases, a big issue is integration with … well, with data integration. Analytic RDBMS got a lot of their workloads from ELT or ETLT, which stand for Extract/(Transform)/Load/Transform. I.e., you’d load data into an efficient analytic RDBMS and then do your transformations, vs. the “traditional” (for about 10-15 years of tradition) approach of doing your transformations in your ETL (Extract/Transform/Load) engine. But in bigger installations, Hadoop often snatches away that part of the workload, even if the rest of the processing remains on a dedicated analytic RDBMS platform such as Teradata’s.

And suppose you want to integrate with more advanced analytics — e.g. statistics, other predictive modeling/machine learning, or graph analytics? Well — and this both surprised and disappointed me — analytic platforms in the RDBMS sense didn’t work out very well. Early Hadoop had its own problems too. But Spark is doing just fine, and seems poised to win.

My technical observations around these trends include:

And finally, if a task is “partly relational”, then Hadoop or Spark often fit both parts.

But suppose you just want to do business intelligence, which is still almost always done over relational data structures? Analytic RDBMS offer the trade-offs:

Suppose all that is a good match for your situation. Then you should surely continue using an analytic RDBMS, if you already have one, and perhaps even acquire one if you don’t. But for many other use cases, analytic RDBMS are no longer the best way to go.

Finally, how does the cloud affect all this? Mainly, it brings one more analytic RDBMS competitor into the mix, namely Amazon Redshift. Redshift is a simple system for doing analytic SQL over data that was in or headed to the Amazon cloud anyway. It seems to be quite successful.

Bottom line: Analytic RDBMS are no longer in their youthful prime, but they are healthy contributors in middle age. Mainly, they’re still best-of-breed for supporting demanding BI.


29 Responses to “Are analytic RDBMS and data warehouse appliances obsolete?”

  1. David Rydzewski on August 28th, 2016 11:31 pm

    We use Vertica on AWS and we are pretty happy with it. We load raw and use parallel SQL to transform. We prefer it over Redshift for several reasons – partitioning, multiple projections per table, extensibility (UDF framework), and transactions actually work as expected.

    “I hear about Vertica more as a technology to be replaced” – I’m curious what it is usually being replaced by?

  2. Joel Wittenmyer on August 29th, 2016 9:02 am

    @2009 is when I began reading your posts. I was re-architecting a data warehouse. You left one out of your list that is my favorite, and which came to my attention via your posts of that time: Exasol. With GoldenGate Change Data Capture to deliver our database update in near-real-time, and a Data Vault Integrattion Hub on Exasol, we will virtualize the delivery layer for BI, and do analytics on the Data Vault. How does that strike you?

  3. Ron Dunn on August 29th, 2016 9:14 am

    Curt, others have mentioned Exasol and Vertica in the cloud, and there’s Snowflake, Azure SQL Data Warehouse, Teradata and Google BigQuery also making big strides in this space.

    I agree that the on-premise DW appliance is on life support, but cloud-based solutions offer a great alternative.

  4. Dan Osipov on August 29th, 2016 6:25 pm

    I’d also add Google Big Query as an interesting technology, as well as Presto – open source DB from FB, that is similar to Impala. I’ve seen Presto start to eat away workloads previously handled by Vertica.

  5. David Gruzman on August 30th, 2016 9:02 am

    I think situation is quite different between cloud and in-premises. I believe, that in cloud elastic allocation of resources is a right way to do analytics over big data…
    In-premises, where compute is actually scarce and not-elastic resource – efficiency per pound of hardware of MPP databases is important factor.

  6. cerberus on August 30th, 2016 10:34 am

    Actian is axing their analytical databases, they already fired their dev teams.

  7. Curt Monash on August 30th, 2016 3:27 pm

    If Vertica is replaced, it will be because either:

    — The user thinks a Hadoop- and/or Spark-oriented stack is cheaper, more functional in analytics, and/or better at dealing with multi-structured data.

    — The user wants to do analytics on data that’s being updated at OLTP speeds.

  8. Curt Monash on August 30th, 2016 3:30 pm

    Actian’s website seems to be in a bit of disarray. I was tipped off last night that it’s hard to find material about ParAccel or VectorWise there. However, what used to be Pervasive DataRush and related technologies still seem to be being pushed.

    But then, I can’t find an Ingres references either, and Actian is surely still in the Ingres business, so we’ll have to wait and see.

    I’d be interested in hearing from Actian as to what’s up. I’ve blacklisted them for years due to some lies they told publicly about previous conversations with me, and I still wouldn’t want a product-feature briefing for that reason. But if they want to tell me what businesses they basically are or aren’t still in, I’m curious.

  9. Curt Monash on August 30th, 2016 3:31 pm

    If you look back, I covered Exasol years ago. It seemed like a sensibly-architected technology, but without the maturity to be a top-tier competitor, and without the momentum (Germany excepted) to change that.

    The comment above is the first time I’ve heard about them in ages.

  10. Curt Monash on August 30th, 2016 3:34 pm

    Ron et al.,

    I agree that cloud/SaaS is a viable deployment option for ever more analytic RDBMS. Vertica in particular got to the cloud early for test/dev.

    But to what extent are new cloud adopters adopting “traditional” systems over the less functional but usually cheaper Redshift?

  11. Curt Monash on August 31st, 2016 5:09 pm

    An informant in whom I have great confidence tells me that Actian was shopping Vector/Vectorwise and Matrix/ParAccel for quite a while, unsuccessfully. The informant also supplied revenue numbers that were amazingly low.

  12. Curt Monash on August 31st, 2016 5:12 pm

    Meanwhile, Infobright suddenly canned its community edition, but is moving forward otherwise.

    And Exasol is putting out minor press releases like a company that is still chugging along. Ditto Kognitio. While I may be wrong about this, I get the impression that each of Infobright, Kognitio and Exasol is telling a “query accelerator” story as much as it is telling an analytic RDBMS one.

  13. An analytical database adapts to world that includes Hadoop, Spark - Artificial Intelligence Online on September 1st, 2016 2:13 am

    […] a recent blog post that ponders the future of the analytical RDBMS, Monash said the systems still excelled at key business intelligent jobs such as complex ad hoc […]

  14. Harri Kallio on September 1st, 2016 10:56 am

    Actually Ingres is mentioned at Actian site under Data Management section:

  15. James on September 5th, 2016 12:33 pm

    Apparently HP are offloading Vertica as well

    What will that mean for the Product and these products moving forward? Are the big players all going to follow and abandon them, consolidating the DBMS space again to what it looked like in the nineties (Oracle, Teradata…) and some niche players?

    Interesting times.

  16. Curt Monash on September 6th, 2016 2:22 am

    As long as the Hadoop and NoSQL vendors aren’t acquired, we’re not consolidated the way we were before. Amazon also seems here to stay.

  17. “Real-time” is getting real | DBMS 2 : DataBase Management System Services on September 6th, 2016 2:43 am

    […] Business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic RDBMS. […]

  18. Tim Tyrrell on September 6th, 2016 12:25 pm

    We liked your article and certainly noted the absence of Infobright. Here is what we have been up to.
    All the best,

  19. “Real-time” is getting real | DBMS 2 : DataBase Management System Services – Cloud Data Architect on September 7th, 2016 3:59 am

    […] Business intelligence should occur at interactive speeds, which is a major reason that there’s a market for high-performance analytic RDBMS. […]

  20. Curt Monash on September 7th, 2016 8:04 pm


    That’s a perfect way to use the comments on this blog. Too marketing-spun for me to ever endorse myself, but with enough meat that I’m happy to see my readers’ attention drawn to it by you. 🙂

  21. Madhu on September 23rd, 2016 11:20 pm

    Trafodion seems to be getting stable and may not be a bad choice for an analytics rdbms. Well almost an rdbms ! Doing some benchmarks on it in our data sciences lab, so can comment better once this exercise is over.C curious as to how their mvcc based scheme scales.

  22. Thomas Henson on September 29th, 2016 9:22 am

    I believe we will have RDBMS for awhile but their footprint will definitely shrink. In the Hadoop eco system we have yet to provide that fast connection that exist in RDBMS for supporting the dashboard and customer facing insights.

    What I see working right now is to ETL offload data from the data warehouse experiment and find new insights, then push that completed data model back out the data warehouse.

  23. Len Fischer on November 3rd, 2016 3:44 pm

    Hi Curt,

    I started as the Actian, SVP of Marketing, on Monday Oct 24th. I am sorry that you had a bad experience with Actian previously. I would love to talk one on one so please reach out to me and suggest some days/times that work for you.

    Did you see our Press Release this week?


  24. Chris on November 5th, 2016 5:21 pm

    Redshift is nothing but ParAccel!!

  25. Curt Monash on November 11th, 2016 11:45 pm


    Actually, Redshift started as a stripped-down subset of ParAccel.

  26. Chris on November 17th, 2016 1:44 pm

    Have used both and there isn’t any difference.
    Only diff is udf support , even error messages are same :))

  27. Curt Monash on November 17th, 2016 1:53 pm

    Besides UDFs, I’m pretty sure Amazon didn’t use all of ParAccel’s scale-out technology. But if you’re happy with Redshift performance and scaling, you don’t need to care about that part.

    Other than performance and functionality, I agree they’re the same thing! 🙂

  28. Introduction to and CrateDB | DBMS 2 : DataBase Management System Services on December 18th, 2016 12:27 am

    […] mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it […]

  29. moved here on July 20th, 2022 4:20 pm

    moved here

    In-memory DBMS | DBMS 2 : DataBase Management System Services

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