Analysis of data management technology optimized for text data. Related subjects include:
This is part of a four post series spanning two blogs.
- One post gives a general historical overview of the artificial intelligence business.
- One post specifically covers the history of expert systems.
- One post (this one) gives a general present-day overview of the artificial intelligence business.
- One post explores the close connection between machine learning and (the rest of) AI.
1. “Artificial intelligence” is a term that usually means one or more of:
- “Smart things that computers can’t do yet.”
- “Smart things that computers couldn’t do until recently.”
- “Technology that has emerged from the work of computer scientists who said they were doing AI.”
- “Underpinnings for other things that might be called AI.”
But that covers a lot of ground, especially since reasonable people might disagree as to what constitutes “smart”.
2. Examples of what has been called “AI” include:
- Rule-based processing, especially if it is referred to as “expert systems”.
- Machine learning.
- Many aspects of “natural language processing” — a term almost as overloaded as “artificial intelligence” — including but not limited to:
- Text search.
- Speech recognition, especially but not only if it seems somewhat lifelike.
- Automated language translation.
- Natural language database query.
- Machine vision.
- Autonomous vehicles.
- Robots, especially but not only ones that seem somewhat lifelike.
- Automated theorem proving.
- Playing chess at an ELO rating of 1600 or better.
- Beating the world champion at chess.
- Beating the world champion at Jeopardy.
- Anything that IBM brands or rebrands as “Watson”.
Obviously, a large fraction of what I write about involves technical differentiation. So let’s try for a framework where differentiation claims can be placed in context. This post will get through the generalities. The sequels will apply them to specific cases.
Many buying and design considerations for IT fall into six interrelated areas: Read more
MongoDB isn’t the only company I reached out to recently for an update. Another is DataStax. I chatted mainly with Patrick McFadin, somebody with whom I’ve had strong consulting relationships at a user and vendor both. But Rachel Pedreschi contributed the marvelous phrase “twinkling dashboard”.
It seems fair to say that in most cases:
- Cassandra is adopted for operational applications, specifically ones with requirements for extreme uptime and/or extreme write speed. (Of course, it should also be the case that NoSQL data structures are a good fit.)
- Spark, including SparkSQL, and Solr are seen primarily as ways to navigate or analyze the resulting data.
Those generalities, in my opinion, make good technical sense. Even so, there are some edge cases or counterexamples, such as:
- DataStax trumpets British Gas‘ plans collecting a lot of sensor data and immediately offering it up for analysis.*
- Safeway uses Cassandra for a mobile part of its loyalty program, scoring customers and pushing coupons at them.
- A large title insurance company uses Cassandra-plus-Solr to manage a whole lot of documents.
*And so a gas company is doing lightweight analysis on boiler temperatures, which it regards as hot data.
While most of the specifics are different, I’d say similar things about MongoDB, Cassandra, or any other NoSQL DBMS that comes to mind: Read more
|Categories: Business intelligence, Cassandra, Databricks, Spark and BDAS, DataStax, NoSQL, Open source, Petabyte-scale data management, Predictive modeling and advanced analytics, Specific users, Text||6 Comments|
One pleasure in talking with my clients at MongoDB is that few things are NDA. So let’s start with some numbers:
- >2,000 named customers, the vast majority of which are unique organizations who do business with MongoDB directly.
- ~75,000 users of MongoDB Cloud Manager.
- Estimated ~1/4 million production users of MongoDB total.
Also >530 staff, and I think that number is a little out of date.
MongoDB lacks many capabilities RDBMS users take for granted. MongoDB 3.2, which I gather is slated for early November, narrows that gap, but only by a little. Features include:
- Some JOIN capabilities.
- Specifically, these are left outer joins, so they’re for lookup but not for filtering.
- JOINs are not restricted to specific shards of data …
- … but do benefit from data co-location when it occurs.
- A BI connector. Think of this as a MongoDB-to- SQL translator. Using this does require somebody to go in and map JSON schemas and relational tables to each other. Once that’s done, the flow is:
- Basic SQL comes in.
- Filters and GroupBys are pushed down to MongoDB. A result set … well, it results.
- The result set is formatted into a table and returned to the system — for example a business intelligence tool — that sent the SQL.
- Database-side document validation, in the form of field-specific rules that combine into a single expression against which to check a document.
- This is fairly simple stuff — no dependencies among fields in the same document, let alone foreign key relationships.
- MongoDB argues, persuasively, that this simplicity makes it unlikely to recreate the spaghetti code maintenance nightmare that was 1990s stored procedures.
- MongoDB concedes that, for performance, it will ordinarily be a good idea to still do your validation on the client side.
- MongoDB points out that enforcement can be either strict (throw errors) or relaxed (just note invalid documents to a log). The latter option is what makes it possible to install this feature without breaking your running system.
There’s also a closed-source database introspection tool coming, currently codenamed MongoDB Scout. Read more
|Categories: Business intelligence, EAI, EII, ETL, ELT, ETLT, Market share and customer counts, MongoDB, NoSQL, Open source, Structured documents, Text||6 Comments|
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||7 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.