Predictive modeling and advanced analytics
Discussion of technologies and vendors in the overlapping areas of predictive analytics, predictive modeling, data mining, machine learning, Monte Carlo analysis, and other “advanced” analytics.
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 gives a general present-day overview of the artificial intelligence business.
- One post (this one) explores the close connection between machine learning and (the rest of) AI.
1. I think the technical essence of AI is usually:
- Inputs come in.
- Decisions or actions come out.
- More precisely — inputs come in, something intermediate is calculated, and the intermediate result is mapped to a decision or action.
- The intermediate results are commonly either numerical (a scalar or perhaps a vector of scalars) or a classification/partition into finitely many possible intermediate outputs.
Of course, a lot of non-AI software can be described the same way.
To check my claim, please consider:
- It fits rules engines/expert systems so simply it’s barely worth saying.
- It fits any kind of natural language processing; the intermediate results might be words or phrases or concepts or whatever.
- It fits machine vision beautifully.
To see why it’s true from a bottom-up standpoint, please consider the next two points.
2. It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response. Examples of what I mean include: Read more
|Categories: Facebook, Google, IBM and DB2, Microsoft and SQL*Server, Predictive modeling and advanced analytics||6 Comments|
I talked with Cloudera shortly ahead of today’s announcement of Cloudera 5.5. Much of what we talked about had something or other to do with SQL data management. Highlights include:
- Impala and Kudu are being donated to Apache. This actually was already announced Tuesday. (Due to Apache’s rules, if I had any discussion with Cloudera speculating on the likelihood of Apache accepting the donations, I would not be free to relay it.)
- Cloudera is introducing SQL extensions so that Impala can query nested data structures. More on that below.
- The basic idea for the nested datatype support is that there are SQL extensions with a “dot” notation to let you get at the specific columns you need.
- From a feature standpoint, we’re definitely still in the early days.
- When I asked about indexes on these quasi-columns, I gathered that they’re not present in beta but are hoped for by the time of general availability.
- Basic data skipping, also absent in beta, seems to be more confidently expected in GA.
- This is for Parquet first, Avro next, and presumably eventually native JSON as well.
- This is said to be Dremel-like, at least in the case of Parquet. I must confess that I’m not familiar enough with Apache Drill to compare the two efforts.
- Cloudera is increasing its coverage of Spark in several ways.
- Cloudera is adding support for MLlib.
- Cloudera is adding support for SparkSQL. More on that below.
- Cloudera is adding support for Spark going against S3. The short answer to “How is this different from the Databricks service?” is:
- More “platform” stuff from the Hadoop stack (e.g. for data ingest).
- Less in the way of specific Spark usability stuff.
- Cloudera is putting into beta what it got in the Xplain.io acquisition, which it unfortunately is naming Cloudera Navigator Optimizer. More on that in a separate post.
- Impala and Hive are getting column-level security via Apache Sentry.
- There are other security enhancements.
- Some policy-based information lifecycle management is being added as well.
While I had Cloudera on the phone, I asked a few questions about Impala adoption, specifically focused on concurrency. There was mention of: Read more
|Categories: Benchmarks and POCs, Cloudera, Data warehousing, Databricks, Spark and BDAS, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, SQL/Hadoop integration||4 Comments|
In the previous post I broke product differentiation into 6-8 overlapping categories, which may be abbreviated as:
- (Other) trustworthiness
- User experience
and sometimes also issues in adoption and administration.
Now let’s use this framework to examine two market categories I cover — data management and, in separate post, business intelligence.
Applying this taxonomy to data management:
|Categories: Buying processes, Clustering, Data warehousing, Database diversity, Microsoft and SQL*Server, Predictive modeling and advanced analytics, Pricing||2 Comments|
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
This is part of a three-post series on Kudu, a new data storage system from Cloudera.
- Part 1 (this post) is an overview of Kudu technology.
- Part 2 is a lengthy dive into how Kudu writes and reads data.
- Part 3 is a brief speculation as to Kudu’s eventual market significance.
Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.
*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes.
- Kudu is an alternative to HDFS (Hadoop Distributed File System), or to HBase.
- Kudu is meant to be the underpinning for Impala, Spark and other analytic frameworks or engines.
- Kudu is not meant for OLTP (OnLine Transaction Processing), at least in any foreseeable release. For example:
- Kudu doesn’t support multi-row transactions.
- There are no active efforts to front-end Kudu with an engine that is fast at single-row queries.
- Kudu is rather columnar, except for transitory in-memory stores.
- Kudu’s core design points are that it should:
- Accept data very quickly.
- Immediately make that data available for analytics.
- More specifically, Kudu is meant to accept, along with slower forms of input:
- Lots of fast random writes, e.g. of web interactions.
- Streams, viewed as a succession of inserts.
- Updates and inserts alike.
- The core “real-time” use cases for which Kudu is designed are, unsurprisingly:
- Low-latency business intelligence.
- Predictive model scoring.
- Kudu is designed to work fine with spinning disk, and indeed has been tested to date mainly on disk-only nodes. Even so, Kudu’s architecture is optimized for the assumption that there will be at least some flash on the node.
- Kudu is designed primarily to support relational/SQL processing. However, Kudu also has a nested-data roadmap, which of course starts with supporting the analogous capabilities in Impala.
|Categories: Business intelligence, Cloudera, Columnar database management, Database compression, Databricks, Spark and BDAS, Hadoop, HBase, Predictive modeling and advanced analytics, Solid-state memory, SQL/Hadoop integration||7 Comments|
- My client Rocana is the renamed ScalingData, where Rocana is meant to signify ROot Cause ANAlysis.
- Rocana was founded by Omer Trajman, who I’ve referenced numerous times in the past, and who I gather is a former boss of …
- … cofounder Eric Sammer.
- Rocana recently told me it had 35 people.
- Rocana has a very small number of quite large customers.
Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:
- Actual operations — figuring out exactly what isn’t working, ASAP.
Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:
- The sort of network data you’d generally think of — “everything” except packet-inspection stuff.
- Firewall output.
- Database server logs.
- Point-of-sale data (at a retailer).
- “Application data”, whatever that means. (Edit: See Tom Yates’ clarifying comment below.)
|Categories: Business intelligence, Hadoop, Kafka and Confluent, Log analysis, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, Pricing, Rocana, Splunk, Web analytics||1 Comment|
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|
Let’s start with some terminology biases:
- I dislike the term “big data” but like the Vs that define it — Volume, Velocity, Variety and Variability.
- Though I think it’s silly, I understand why BI innovators flee from the term “business intelligence” (they’re afraid of not sounding new).
So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.
It turns out that:
- Zoomdata does its processing server-side, which allows for load-balancing and scale-out. Scale-out and claims of great query speed are relevant when data is of high volume.
- Zoomdata depends heavily on Spark.
- Zoomdata’s UI assumes data can be a mix of historical and streaming, and that if looking at streaming data you might want to also check history. This addresses velocity.
- Zoomdata assumes data can be in a variety of data stores, including:
- Relational (operational RDBMS, analytic RDBMS, or SQL-on-Hadoop).
- Files (generic HDFS — Hadoop Distributed File System or S3).*
- NoSQL (MongoDB and HBase were mentioned).
- Search (Elasticsearch was mentioned among others).
- Zoomdata also tries to detect data variability.
- Zoomdata is OEM/embedding-friendly.
*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.
Core aspects of Zoomdata’s technical strategy include: Read more
In which I observe that Tim Cook and the EFF, while thankfully on the right track, haven’t gone nearly far enough.
Traditionally, the term “chilling effect” referred specifically to inhibitions on what in the US are regarded as First Amendment rights — the freedoms of speech, the press, and in some cases public assembly. Similarly, when the term “chilling effect” is used in a surveillance/privacy context, it usually refers to the fear that what you write or post online can later be held against you. This concern has been expressed by, among others, Tim Cook of Apple, Laura Poitras, and the Electronic Frontier Foundation, and several research studies have supported the point.
But that’s only part of the story. As I wrote in July, 2013,
… with the new data collection and analytic technologies, pretty much ANY action could have legal or financial consequences. And so, unless something is done, “big data” privacy-invading technologies can have a chilling effect on almost anything you want to do in life.
The reason, in simplest terms, is that your interests could be held against you. For example, models can estimate your future health, your propensity for risky hobbies, or your likelihood of changing your residence, career, or spouse. Any of these insights could be useful to employers or financial services firms, and not in a way that redounds to your benefit. And if you think enterprises (or governments) would never go that far, please consider an argument from the sequel to my first “chilling effects” post: Read more
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.