Data types
Analysis of data management technology optimized for specific datatypes, such as text, geospatial, object, RDF, or XML. Related subjects include:
- Any subcategory
- Database diversity
Text data management, Part 1: Confusion
This is Part 1 of a three post series. The posts cover:
- Confusion about text data management.
- Choices for text data management (general and short-request).
- Choices for text data management (analytic).
There’s much confusion about the management of text data, among technology users, vendors, and investors alike. Reasons seems to include:
- The terminology around text data is inaccurate.
- Data volume estimates for text are misleading.
- Multiple different technologies are in the mix, including:
- Enterprise text search.
- Text analytics — text mining, sentiment analysis, etc.
- Document stores — e.g. document-oriented NoSQL, or MarkLogic.
- Log management and parsing — e.g. Splunk.
- Text archiving — e.g., various specialty email archiving products I couldn’t even name.
- Public web search — Google et al.
- Text search vendors have disappointed, especially technically.
- Text analytics vendors have disappointed, especially financially.
- Other analytic technology vendors ignore what the text analytic vendors actually have accomplished, and reinvent inferior wheels rather than OEM the state of the art.
Above all: The use cases for text data vary greatly, just as the use cases for simply-structured databases do.
There are probably fewer people now than there were six years ago who need to be told that text and relational database management are very different things. Other misconceptions, however, appear to be on the rise. Specific points that are commonly overlooked include: Read more
| Categories: Analytic technologies, Archiving and information preservation, Google, Log analysis, MarkLogic, NoSQL, Oracle, Splunk, Text | 2 Comments |
Defining NoSQL
A reporter tweeted: “Is there a simple plain English definition for NoSQL?” After reminding him of my cynical yet accurate Third Law of Commercial Semantics, I gave it a serious try, and came up with the following. More precisely, I tweeted the bolded parts of what’s below; the rest is commentary added for this post.
NoSQL is most easily defined by what it excludes: SQL, joins, strong analytic alternatives to those, and some forms of database integrity. If you leave all four out, and you have a strong scale-out story, you’re in the NoSQL mainstream. Read more
| Categories: Cassandra, dbShards and CodeFutures, MarkLogic, MySQL, Object, Open source, Petabyte-scale data management, Schooner Information Technology | 7 Comments |
Oracle NoSQL is unlikely to be a big deal
Alex Williams noticed that there will be a NoSQL session at Oracle OpenWorld next week, and is wondering whether this will be a big deal. I think it won’t be.
There really are three major points to NoSQL.
- Dynamic schemas. This is the only one of the three that truly depends on NoSQL.
- Scale-out short-request processing. If you want to scale out efficiently at high request volumes, you’re best off not using all the flexibility SQL/relational DBMS offer. (In particular, you don’t want to do cross-node joins). Not coincidentally, a number of the best scale-out offerings were built to be NoSQL.
- Open source. Doing a relational DBMS is a big project. It seems easier to build NoSQL ones.
Oracle can address the latter two points as aggressively as it wishes via MySQL. It so happens I would generally recommend MySQL enhanced by dbShards, Schooner, and/or dbShards/Schooner, rather than Oracle-only MySQL … but that’s a detail. In some form or other, Oracle’s MySQL is a huge player in the scale-out, open source, short-request database management market.
So that leaves us with dynamic schemas. Oracle has at least four different sets of technology in that area:
- As Workday noticed years ago, MySQL can be used as a functional, basic key-value store.
- Oracle also has XML-based Berkeley DB/SleepyCat kicking around.*
- The XML extensions to Oracle’s core DBMS could be alleged to have a dynamic schema/NoSQL flavor. (Blech.)
- A dynamic schema argument could also be made for object-oriented DBMS technology. While Oracle doesn’t to my knowledge exactly sell that, it does have the Tangosol Coherence line of technology, with a potentially similar programming model.
If Oracle is now refreshing and rebranding one or more of these as “NoSQL”, there’s no reason to view that as a big deal at all.
*That’s Mike Olson’s former company, if you’re keeping score at home.
| Categories: MySQL, NoSQL, Object, OLTP, Open source, Oracle, Parallelization, Schooner Information Technology, Structured documents | 13 Comments |
The database architecture of salesforce.com, force.com, and database.com
salesforce.com, force.com, and database.com use exactly the same database infrastructure and architecture. That’s the good news. The bad news is that salesforce.com is somewhat obscure about technical details, for reasons such as:
- A long-ago marketing decision to not give infrastructure details, so as to convey a “Don’t worry; we’ll take care of everything” message.
- Even so, a long-ago and perhaps now-regretted marketing decision to disclose and even exaggerate salesforce.com’s reliance on Oracle, as part of an early-days attempt to prove salesforce was using enterprise-class technology.
- A desire to hide the recipe for salesforce.com’s secret sauce.
- Force of habit — I’m not sure salesforce even knows how to tell its technical story with any clarity.
Actually, salesforce.com has moved some kinds of data out of Oracle that previously used to be stored there. Besides Oracle, salesforce uses at least a file system and a RAM-based data store about which I have no details. Even so, much of salesforce.com’s data is stored in Oracle — a single instance of Oracle, which it believes may be the largest instance of Oracle in the world.
| Categories: Data models and architecture, Market share and customer counts, Memory-centric data management, Object, OLTP, Oracle, salesforce.com, Software as a Service (SaaS) | 19 Comments |
Aster Data business trends
Last month, I reviewed with the Aster Data folks which markets they were targeting and selling into, subsequent to acquisition by their new orange overlords. The answers aren’t what they used to be. Aster no longer focuses much on what it used to call frontline (i.e., low-latency, operational) applications; those are of course a key strength for Teradata. Rather, Aster focuses on investigative analytics — they’ve long endorsed my use of the term — and on the batch run/scoring kinds of applications that inform operational systems.
| Categories: Analytic technologies, Application areas, Aster Data, Data warehousing, DataStax, RDF and graphs, Surveillance and privacy, Teradata, Web analytics | 1 Comment |
Derived data, progressive enhancement, and schema evolution
The emphasis I’m putting on derived data is leading to a variety of questions, especially about how to tease apart several related concepts:
- Derived data.
- Many-step processes to produce derived data.
- Schema evolution.
- Temporary data constructs.
So let’s dive in. Read more
| Categories: Data models and architecture, Data warehousing, Derived data, MarkLogic, Text | Leave a Comment |
HP/Autonomy sound bites
HP has announced that:
- HP is buying Autonomy.
- HP is pulling back from WebOS.
- HP may spin off its PC business altogether.
On a high level, this means:
- HP is doubling down on enterprise IT.
- HP is taking a more software-centric approach to the enterprise IT business.
- HP is backing away from the consumer electronics business.
- HP in particular is backing away from the generic desktop/laptop PC business, which may with only moderate exaggeration be regarded as:
- The intersection of the enterprise IT and consumer electronics businesses.
- The least attractive sector of each.
My coverage of Autonomy isn’t exactly current, but I don’t know of anything that contradicts long-time competitor* Dave Kellogg’s skeptical view of Autonomy. Autonomy is a collection of businesses involved in the management, search, and retrieval of poly-structured data, in some cases with strong market share, but even so not necessarily with the strongest of reputations for technology or technology momentum. Autonomy started from a text search engine and a Bayesian search algorithm on top of that, which did a decent job for many customers. But if there’s been much in the way of impressive enhancement over the past 8-10 years, I’ve missed the news.
*Dave, of course, was CEO of MarkLogic.
Questions obviously arise about how the Autonomy acquisition relates to other HP businesses. My early thoughts include: Read more
| Categories: HP and Neoview, Market share and customer counts, Structured documents, Text, Vertica Systems | 10 Comments |
Couchbase business update
I decided I needed some Couchbase drilldown, on business and technology alike, so I had solid chats with both CEO Bob Wiederhold and Chief Architect Dustin Sallings. Pretty much everything I wrote at the time Membase and CouchOne merged to form Couchbase (the company) still holds up. But I have more detail now. 😉
Context for any comments on customer traction includes:
- Membase went into limited production release in October, and full release in January. Similar things are true of CouchDB.
- Hence, most sales of Couchbase’s products have been made over the past 6 months.
- Couchbase (the merged product) is at this point only in a pre-production developer’s release.
- Couchbase has both a direct sales force and a classic open-source “funnel”-based online selling model. Naturally, Couchbase’s understanding of what its customers are doing is more solid with respect to the direct sales base.
- Most of Couchbase’s revenue to date seems to have come from a limited number of big-ticket “lighthouse” accounts (as opposed to, say, the larger number of smaller deals that come in through the online funnel).
That said,
- Most Membase purchases are for new applications, as opposed to memcached migrations. However, customers are the kinds of companies that probably also are using memcached elsewhere.
- Most other Membase purchases are replacements for the Membase/MySQL combination. Bob says those are easy sales with short sales cycles.
- Pure memcached support is a small but non-zero business for Couchbase, and a fine source of upsell opportunities.
- In the pipeline but not so much yet in the customer base are SaaS vendors and the like who use and may want to replace traditional DBMS such as Oracle. Other than among those, Couchbase doesn’t compete much yet with Oracle et al.
- Pure CouchDB isn’t all that much of a business, at least relative to community size, as CouchDB is a single-server product commonly used by people who are content not to pay for support.
Membase sales are concentrated in five kinds of internet-centric companies, which in declining order are: Read more
Terminology: Dynamic- vs. fixed-schema databases
E. F. “Ted” Codd taught the computing world that databases should have fixed logical schemas (which protect the user from having to know about physical database organization). But he may not have been as universally correct as he thought. Cases I’ve noted in which fixed schemas may be problematic include:
- “A bunch of apps in one, similar but not the same” (in my recent post on MongoDB).
- Out-of-control product catalogs (ditto).
- Analytic use cases in which one keeps enhancing the database with derived data.
And if marketing profile analysis is ever done correctly, that will be a huge example for the list.
So what do we call those DBMS — for example NoSQL, object-oriented, or XML-based systems — that bake the schema into the applications or the records themselves? In the MongoDB post I went with “schemaless,” but I wasn’t really comfortable with that, so I took the discussion to Twitter. Comments from Vlad Didenko (in particular), Ryan Prociuk, Merv Adrian, and Roland Bouman favored the idea that schemas in such systems are changeable or late-bound, rather than entirely absent. I quickly agreed.
| Categories: Data models and architecture, NoSQL, Object, Structured documents | 48 Comments |
McObject and eXtremeDB
I talked with McObject yesterday. McObject has two product lines, both of which are something like in-memory DBMS — eXtremeDB, which is the main one, and Perst. McObject has been around since at least 2003, probably has no venture capital, and probably has a very low double-digit number of employees.*
*I could be wrong in those guesses; as small companies go, McObject is unusually prone to secrecy games.
As best I understand:
- eXtremeDB is something like an in-memory object-oriented DBMS, designed to be embeddable.
- However, much as with Objectivity and other old-school OODBMS, eXtremeDB winds up being more of a toolkit with which to build DBMS than a full DBMS.
- eXtremeDB has a few indexing schemes. The main one is good old B-trees. One customer wanted Patricia tries, so they’re in there. (Perhaps not coincidentally, solidDB relies on Patricia tries.) At least one wanted R-trees, so they’re in there too.
- eXtremeDB has long had the option of persistent logs.
- eXtremeDB newly has a hybrid memory-centric option, in which you can have more data in the database than fits into RAM.
- eXtremeDB newly has multi-master two-phase-commit clustering.
My guess three years ago that eXtremeDB might emerge as an alternative to solidDB seems to have been borne out. McObject CEO Steve Graves says that the core of McObject’s business is OEMs, in sectors such as telecom equipment and defense/aerospace. That’s exactly solidDB’s traditional market, except that solidDB got acquired by IBM and deemphasized it.
I’ve said before that if I were starting a SaaS effort — and it wasn’t just focused on analytics — I’d look at using a memory-centric OODBMS. Perhaps eXtremeDB is worth looking at in such scenarios.