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      In-memory DBMS

      Analysis of memory-centric OLTP DBMS. Related subjects include:

      October 21, 2016

      Rapid analytics

      “Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

      1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

      2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

      September 6, 2016

      “Real-time” is getting real

      I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:

      A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:

      Straightforward applications of this principle include: Read more

      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.

      Read more

      December 10, 2015

      Readings in Database Systems

      Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:

      I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.

      But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**

      *When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.

      **Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.

      Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as:? Read more

      February 22, 2015

      Data models

      7-10 years ago, I repeatedly argued the viewpoints:

      Since then, however:

      So it’s probably best to revisit all that in a somewhat organized way.

      Read more

      February 12, 2015

      MongoDB 3.0

      Old joke:

      A lot has happened in MongoDB technology over the past year. For starters:

      *Newly-released MongoDB 3.0 is what was previously going to be MongoDB 2.8. My clients at MongoDB finally decided to give a “bigger” release a new first-digit version number.

      To forestall confusion, let me quickly add: Read more

      January 19, 2015

      Where the innovation is

      I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. ?? But if we abandon any hope that this post could be comprehensive, I can at least say:

      1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.

      2. Even so, there’s much room for innovation around data movement and management. I’d start with:

      3. As I suggested last year, data transformation is an important area for innovation.? Read more

      July 14, 2014

      21st Century DBMS success and failure

      As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.

      DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.

      In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.

      Buyer inertia is a greater concern.

      A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.

      Otherwise I’d say:? Read more

      June 8, 2014

      Optimism, pessimism and fatalism — fault-tolerance, Part 1

      Writing data management or analysis software is hard. This post and its sequel are about some of the reasons why.

      When systems work as intended, writing and reading data is easy. Much of what’s hard about data management is dealing with the possibility — really the inevitability — of failure. So it might be interesting to survey some of the many ways that considerations of failure come into play. Some have been major parts of IT for decades; others, if not new, are at least newly popular in this cluster-oriented, RAM-crazy era. In this post I’ll focus on topics that apply to single-node systems; in the sequel I’ll emphasize topics that are clustering-specific.

      Major areas of failure-aware design — and these overlap greatly — include:

      Long-standing basics

      In a single-server, disk-based configuration, techniques for database fault-tolerance start: Read more

      May 6, 2014

      Notes and comments, May 6, 2014

      After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

      Here is a catch-all post to complete the set.? Read more

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