Analysis of data management technology optimized for text data. Related subjects include:
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.
1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.
2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:
- Databricks does not directly license or support on-premises Spark users. Rather …
- … it helps partner companies to do so, where:
- Examples of partner companies include usual-suspect Hadoop distribution vendors, and DataStax.
- “Help” commonly includes higher-level support.
However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.
Databricks, by the way, has grown to >50 people.
3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.
|Categories: Data integration and middleware, Databricks, Spark and BDAS, DataStax, Hadoop, Health care, Investment research and trading, Text||Leave a Comment|
Indexes are central to database management.
- My first-ever stock analyst report, in 1982, correctly predicted that index-based DBMS would supplant linked-list ones …
- … and to this day, if one wants to retrieve a small fraction of a database, indexes are generally the most efficient way to go.
- Recently, I’ve had numerous conversations in which indexing strategies played a central role.
Perhaps it’s time for a round-up post on indexing.
1. First, let’s review some basics. Classically:
- An index is a DBMS data structure that you probe to discover where to find the data you really want.
- Indexes make data retrieval much more selective and hence faster.
- While indexes make queries cheaper, they make writes more expensive — because when you write data, you need to update your index as well.
- Indexes also induce costs in database size and administrative efforts. (Manual index management is often the biggest hurdle for “zero-DBA” RDBMS installations.)
2. Further: Read more
|Categories: Data warehousing, Database compression, GIS and geospatial, Google, MapReduce, McObject, MemSQL, MySQL, ScaleDB, solidDB, Sybase, Text, Tokutek and TokuDB||18 Comments|
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.
- Volume has been solved. There are Hadoop installations with 100s of petabytes of data, analytic RDBMS with 10s of petabytes, general-purpose Exadata sites with petabytes, and 10s/100s of petabytes of analytic Accumulo at the NSA. Further examples abound.
- Velocity is being solved. My recent post on Hadoop-based streaming suggests how. In other use cases, velocity is addressed via memory-centric RDBMS.
- Variety and Variability have been solved. MongoDB, Cassandra and perhaps others are strong NoSQL choices. Schema-on-need is in earlier days, but may help too.
2. Even so, there’s much room for innovation around data movement and management. I’d start with:
- Product maturity is a huge issue for all the above, and will remain one for years.
- Hadoop and Spark show that application execution engines:
- Have a lot of innovation ahead of them.
- Are tightly entwined with data management, and with data movement as well.
- Hadoop is due for another refactoring, focused on both in-memory and persistent storage.
- There are many issues in storage that can affect data technologies as well, including but not limited to:
- Solid-state (flash or post-flash) vs. spinning disk.
- Networked vs. direct-attached.
- Virtualized vs. identifiable-physical.
- Graph analytics and data management are still confused.
We all tend to assume that data is a great and glorious asset. How solid is this assumption?
- Yes, data is one of the most proprietary assets an enterprise can have. Any of the Goldman Sachs big three* — people, capital, and reputation — are easier to lose or imitate than data.
- In many cases, however, data’s value diminishes quickly.
- Determining the value derived from owning, analyzing and using data is often tricky — but not always. Examples where data’s value is pretty clear start with:
- Industries which long have had large data-gathering research budgets, in areas such as clinical trials or seismology.
- Industries that can calculate the return on mass marketing programs, such as internet advertising or its snail-mail predecessors.
*”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.
This all raises the idea – if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include: Read more
|Categories: Data mart outsourcing, eBay, Health care, Investment research and trading, Log analysis, Scientific research, Text, Web analytics||5 Comments|
I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:
- An actual automation and management user interface, as opposed to the current management style, which is almost entirely via scripts (except for the monitoring UI).
- That’s scheduled for public beta in May, and general availability later this year.
- It will include some kind of integrated provisioning with VMware, OpenStack, et al.
- One goal is to let you apply database changes, software upgrades, etc. without taking the cluster down.
- A reasonable backup strategy.
- A snapshot copy is made of the database.
- A copy of the log is streamed somewhere.
- Periodically — the default seems to be 6 hours — the log is applied to create a new current snapshot.
- For point-in-time recovery, you take the last snapshot prior to the point, and roll forward to the desired point.
- A reasonable locking strategy!
- Document-level locking is all-but-promised for MongoDB 2.8.
- That means what it sounds like. (I mention this because sometimes an XML database winds up being one big document, which leads to confusing conversations about what’s going on.)
- Security. My eyes glaze over at the details, but several major buzzwords have been checked off.
- A general code rewrite to allow for (more) rapid addition of future features.
Some technical background about Splunk
In an October, 2009 technical introduction to Splunk, I wrote (emphasis added):
Splunk software both reads logs and indexes them. The same code runs both on the nodes that do the indexing and on machines that simply emit logs.
It turns out that the bolded part was changed several years ago. However, I don’t have further details, so let’s move on to Splunk’s DBMS-like aspects.
I also wrote:
The fundamental thing that Splunk looks at is an increment to a log – i.e., whatever has been added to the log since Splunk last looked at it.
That remains true. Confusingly, Splunk refers to these log increments as “rows”, even though they’re really structured and queried more like documents.
I further wrote:
Splunk has a simple ILM (Information Lifecycle management) story based on time. I didn’t probe for details.
Splunk’s ILM story turns out to be simple indeed.
- As data streams in, Splunk adds it to the most recent — “hot” — bucket. Once a bucket is full, it becomes immutable — “warm” — and a new hot bucket is opened to receive data.
- Splunk executes queries against whichever of these time-slice buckets make sense, then unions results together as needed.
Finally, I wrote:
I get the impression that most Splunk entity extraction is done at search time, not at indexing time. Splunk says that, if a <name, value> pair is clearly marked, its software does a good job of recognizing same. Beyond that, fields seem to be specified by users when they define searches.
I have trouble understanding how Splunk could provide flexible and robust reporting unless it tokenized and indexed specific fields more aggressively than I think it now does.
The point of what I in October, 2013 called
a high(er)-performance data store into which you can selectively copy columns of data
and which Splunk enthusiastically calls its “High Performance Analytic Store” is to meet that latter need.
Inverted list technology is confusing for several reasons, which start: Read more
|Categories: Data models and architecture, NoSQL, SAP AG, Splunk, Structured documents, Text||1 Comment|
IBM excels at game technology, most famously in Deep Blue (chess) and Watson (Jeopardy!). But except at the chip level — PowerPC — IBM hasn’t accomplished much at game/real world crossover. And so I suspect the Watson hype is far overblown.
I believe that for two main reasons. First, whenever IBM talks about big initiatives like Watson, it winds up bundling a bunch of dissimilar things together and claiming they’re a seamless whole. Second, some core Watson claims are eerily similar to artificial intelligence (AI) over-hype three or more decades past. For example, the leukemia treatment advisor that is being hopefully built in Watson now sounds a lot like MYCIN from the early 1970s, and the idea of collecting a lot of tidbits of information sounds a lot like the Cyc project. And by the way:
- MYCIN led to E-MYCIN, which led to the company Teknowledge, which raised a lot of money* but now has almost faded from memory.
- Cyc is connected to the computer science community’s standard unit of bogosity.
I recently wrote (emphasis added):
My clients at Teradata Aster probably see things differently, but I don’t think their library of pre-built analytic packages has been a big success. The same goes for other analytic platform vendors who have done similar (generally lesser) things. I believe that this is because such limited libraries don’t do enough of what users want.
The bolded part has been, shall we say, confirmed. As Randy Lea tells it, Teradata Aster sales qualification includes the determination that at least one SQL-MR operator — be relevant to the use case. (“Operator” seems to be the word now, rather than “function”.) Randy agreed that some users prefer hand-coding, but believes a large majority would like to push work to data analysts/business analysts who might have strong SQL skills, but be less adept at general mathematical programming.
This phrasing will all be less accurate after the release of Aster 6, which extends Aster’s capabilities beyond the trinity of SQL, the SQL-MR library, and Aster-supported hand-coding.
Randy also said:
- A typical Teradata Aster production customer uses 8-12 of the prebuilt functions (but now they seem to be called operators).
- nPath is used in almost every Aster account. (And by now nPath has morphed into a family of about 5 different things.)
- The Aster collaborative filtering operator is used in almost every account.
- Ditto a/the text operator.
- Several business intelligence vendors are partnering for direct access to selected Teradata Aster operators — mentioned were Tableau, TIBCO Spotfire, and Alteryx.
- I don’t know whether this is on the strength of a specific operator or not, but Aster is used to help with predictive parts failure applications in multiple industries.
And Randy seemed to agree when I put words in his mouth to the effect that the prebuilt operators save users months of development time.
Meanwhile, Teradata Aster has started a whole new library for relationship analytics.
|Categories: Application areas, Aster Data, Data warehousing, Predictive modeling and advanced analytics, Teradata, Text||1 Comment|
Two subjects in one post, because they were too hard to separate from each other
Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.
Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:
- The object/relational, aka universal, extensibility features developed in the 1990s for Oracle, DB2, Informix, Illustra, and Postgres. The most successful extensions probably have been:
- Geospatial indexing via ESRI.
- Full-text indexing, notwithstanding questionable features and performance.
- MySQL storage engines.
- MPP (Massively Parallel Processing) analytic RDBMS relying on single-node PostgreSQL, Ingres, and/or Microsoft SQL Server — e.g. Greenplum (especially early on), Aster (ditto), DATAllegro, DATAllegro’s offspring Microsoft PDW (Parallel Data Warehouse), or Hadapt.
- Splits in which a DBMS has serious processing both in a “database” layer and in a predicate-pushdown “storage” layer — most famously Oracle Exadata, but also MarkLogic, InfiniDB, and others.
- SQL-on-HDFS — Hive, Impala, Stinger, Shark and so on (including Hadapt).
Other examples on my mind include:
- Data manipulation APIs being added to key-value stores such as Couchbase and Aerospike.
- TokuMX, the Tokutek/MongoDB hybrid I just blogged about.
- NuoDB’s willing reliance on third-party key-value stores (or HDFS in the role of one).
- FoundationDB’s strategy, and specifically its acquisition of Akiban.
And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.
In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include: Read more