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  • Theory and architecture

    Analysis of design choices in databases and database management systems. Related subjects include:

    June 20, 2018

    Brittleness and incremental improvement

    Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “robust”, “mature(d), “strengthened”, or simply “improved”.* Otherwise, it can reasonably be called “brittle”.

    *It’s also common to use the word “harden(ed)”. But I think that’s a poor choice, as brittle things are often also hard.

    0. As a general rule in IT:

    There are many categories of IT strengthening. Two of the broadest are:

    1. One of my more popular posts stated:

    Developing a good DBMS requires 5-7 years and tens of millions of dollars.

    The reasons I gave all spoke to brittleness/strengthening, most obviously in:

    Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.

    Similar things are true for other kinds of “platform software” or distributed systems.

    2. The UI brittleness/improvement story starts similarly:? Read more

    May 20, 2018

    Technology implications of political trends

    The tech industry has a broad range of political concerns. While I may complain that things have been a bit predictable in other respects, politics is having real and new(ish) technical consequences. In some cases, existing technology is clearly adequate to meet regulators’ and customers’ demands. Other needs look more like open research challenges.

    1. Privacy regulations will be very different in different countries or regions. For starters:

    All of these rules are subject to change based on:

    And so I believe: For any multinational organization that handles customer data, privacy/security requirements are likely to change constantly. Technology decisions need to reflect that reality.

    2. Data sovereignty/geo-compliance is a big deal. In fact, this is one area where the EU and authoritarian countries such as Russia formally agree. Each wants its citizens’ data to be stored locally, so as to ensure adherence to local privacy rules.

    For raw, granular data, that’s a straightforward — even if annoying — requirement to meet. But things get murkier for data that is aggregated or otherwise derived. Read more

    May 20, 2018

    Some stuff that’s always on my mind

    I have a LOT of partially-written blog posts, but am struggling to get any of them finished (obviously). Much of the problem is that they have so many dependencies on each other. Clearly, then, I should consider refactoring my writing plans. ??

    So let’s start with this. Here, in no particular order, is a list of some things that I’ve said in the past, and which I still think are or should be of interest today. It’s meant to be background for numerous posts I write in the near future, and indeed a few hooks for such posts are included below.

    1.? Data(base) management technology is progressing pretty much as I expected.

    2. Rightly or wrongly, enterprises are often quite sloppy about analytic accuracy.

    Read more

    August 22, 2017

    Imanis Data

    I talked recently with the folks at Imanis Data. For starters:

    Read more

    June 30, 2017

    Analytics on the edge?

    There’s a theory going around to the effect that:

    There’s enough truth to all that to make it worth discussing. But the strong forms of the claims seem overblown.

    1. This story doesn’t even make sense except for certain new classes of application. Traditional business applications run all over the world, in dedicated or SaaSy modes as the case may be. E-commerce is huge. So is content delivery. Architectures for all those things will continue to evolve, but what we have now basically works.

    2. When it comes to real-world appliances, this story is partially accurate. An automobile is a rolling network of custom Linux systems, each running hand-crafted real-time apps, a few of which also have minor requirements for remote connectivity. That’s OK as far as it goes, but there could be better support for real-time operational analytics. If something as flexible as Spark were capable of unattended operation, I think many engineers of real-world appliances would find great ways to use it.

    3. There’s a case to be made for something better yet. I think the argument is premature, but it’s worth at least a little consideration.? Read more

    June 16, 2017

    Generally available Kudu

    I talked with Cloudera about Kudu in early May. Besides giving me a lot of information about Kudu, Cloudera also helped confirm some trends I’m seeing elsewhere, including:

    Now let’s talk about Kudu itself. As I discussed at length in September 2015, Kudu is:

    Kudu’s adoption and roll-out story starts: Read more

    April 17, 2017

    Interana

    Interana has an interesting story, in technology and business model alike. For starters:

    And to be clear — if we leave aside any questions of marketing-name sizzle, this really is business intelligence. The closest Interana comes to helping with predictive modeling is giving its ad-hoc users inspiration as to where they should focus their modeling attention.

    Interana also has an interesting twist in its business model, which I hope can be used successfully by other enterprise software startups as well. Read more

    March 12, 2017

    Introduction to SequoiaDB and SequoiaCM

    For starters, let me say:

    Also:

    Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

    Read more

    December 18, 2016

    Introduction to Crate.io and CrateDB

    Crate.io and CrateDB basics include:

    In essence, CrateDB is an open source and less mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it off.

    CrateDB’s not-just-relational story starts:

    Read more

    November 23, 2016

    DBAs of the future

    After a July visit to DataStax, I wrote

    The idea that NoSQL does away with DBAs (DataBase Administrators) is common. It also turns out to be wrong. DBAs basically do two things.

    • Handle the database design part of application development. In NoSQL environments, this part of the job is indeed largely refactored away. More precisely, it is integrated into the general app developer/architect role.
    • Manage production databases. This part of the DBA job is, if anything, a bigger deal in the NoSQL world than in more mature and automated relational environments. It’s likely to be called part of “devops” rather than “DBA”, but by whatever name it’s very much a thing.

    That turns out to understate the core point, which is that DBAs still matter in non-RDBMS environments. Specifically, it’s too narrow in two ways.

    My wake-up call for that latter bit was a recent MongoDB 3.4 briefing. MongoDB certainly has various efforts in administrative tools, which I won’t recapitulate here. But to my surprise, MongoDB also found a role for something resembling relational database design. The idea is simple: A database administrator defines a view against a MongoDB database, where views: Read more

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