One of the most important issues in privacy and surveillance is also one of the least-discussed — the use of new surveillance technologies in ordinary law enforcement. Reasons for this neglect surely include:
- Governments, including in the US, lie about this subject a lot. Indeed, most of the reporting we do have is exposure of the lies.
- There’s no obvious technology industry ox being gored. What I wrote in another post about Apple, Microsoft et al. upholding their customers’ rights doesn’t have a close analogue here.
One major thread in the United States is: Read more
Numerous tussles fit the template:
- A government wants access to data contained in one or more devices (mobile/personal or server as the case may be).
- The computer’s manufacturer or operator doesn’t want to provide it, for reasons including:
- That’s what customers prefer.
- That’s what other governments require.
- Being pro-liberty is the right and moral choice. (Yes, right and wrong do sometimes actually come into play. )
As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more
|Categories: Amazon and its cloud, Google, Microsoft and SQL*Server, Surveillance and privacy, Web analytics||2 Comments|
This year, privacy and surveillance issues have been all over the news. The most important, in my opinion, deal with the tension among:
- Personal privacy.
- General law enforcement.
More precisely, I’d say that those are the most important in Western democracies. The biggest deal worldwide may be China’s movement towards an ever-more-Orwellian surveillance state.
The main examples on my mind — each covered in a companion post — are:
- The Apple/FBI conflict(s) about locked iPhones.
- The NSA’s propensity to share data with civilian law enforcement.
Legislators’ thinking about these issues, at least in the US, seems to be confused but relatively nonpartisan. Support for these assertions includes:
- The recent unanimous passage in the US House of Representatives of a law restricting police access to email.
- An absurd anti-encryption bill proposed in the US Senate.
- The infrequent mention of privacy/surveillance issues in the current election campaign.
I do think we are in for a spate of law- and rule-making, especially in the US. Bounds on the possible outcomes likely include: Read more
My blogs are having a bad time with comment spam. While Akismet and other safeguards are intercepting almost all of the ~5000 attempted spam comments per day, the small fraction that get through are still a large absolute number to deal with.
There’s some danger I’ll need to restrict comments here to combat it. (At the moment they’ve been turned off almost entirely on Text Technologies, which may be awkward if I want to put a post up there rather than here.) If I do, I’ll say so in a separate post. I apologize in advance for any inconvenience.
Whenever somebody asks for my help on application technology strategy, I start by trying to ascertain three things. The absolute first is actually a prerequisite to almost any kind of useful conversation, which is to ascertain in general terms what the hell it is that we are talking about.
My second goal is to ascertain technology constraints. Three common types are:
- Compatible with legacy systems and/or enterprise standards.
- Cheap, free and/or open source.
- Proven, vetted by sufficiently many references, and/or generally having an “enterprise-y” reputation.
That’s often a short and straightforward discussion, except in those awkward situations when all three of my bullet points above are applicable at once.
The third item is usually more interesting. I try to figure out what is to be accomplished. That’s usually not a simple matter, because the initial list of goals and requirements is almost never accurate. It’s actually more common that I have to tell somebody to be more ambitious than that I need to rein them in.
Commonly overlooked needs include:
- If you want to sell something and have happy users, you need a good UI.
- You will also soon need tools and a UI for administration.
- Customers demand low-latency/fresh data. Your explanation of why they don’t really need it doesn’t contradict the fact that they want it.
- Providing data access and saying “You can hook up any BI tool you want and build charts” is not generally regarded as offering a good UI.
- When “adding analytics” to something previously focused on short-request processing, it is common to underestimate the variety of things users will soon want to do. (One common reason for this under-estimate is that after years of being told it can’t be done, they’ve learned not to ask.)
And if you take one thing away from this post, then take this:
- If you “know” exactly which features are or aren’t helpful to users, …
- .. and if you supply only what you “know” they should use, …
- … then you will discover that what you “knew” wasn’t really accurate.
I guarantee it.
|Categories: Business intelligence, Buying processes, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics||2 Comments|
In a companion introduction to Kafka post, I observed that Kafka at its core is remarkably simple. Confluent offers a marchitecture diagram that illustrates what else is on offer, about which I’ll note:
- The red boxes — “Ops Dashboard” and “Data Flow Audit” — are the initial closed-source part. No surprise that they sound like management tools; that’s the traditional place for closed source add-ons to start.
- “Schema Management”
- Is used to define fields and so on.
- Is not equivalent to what is ordinarily meant by schema validation, in that …
- … it allows schemas to change, but puts constraints on which changes are allowed.
- Is done in plug-ins that live with the producer or consumer of data.
- Is based on the Hadoop-oriented file format Avro.
Kafka offers little in the way of analytic data transformation and the like. Hence, it’s commonly used with companion products. Read more
|Categories: Data integration and middleware, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Kafka and Confluent, Market share and customer counts, Streaming and complex event processing (CEP)||3 Comments|
- Kafka has gotten considerable attention and adoption in streaming.
- Kafka is open source, out of LinkedIn.
- Folks who built it there, led by Jay Kreps, now have a company called Confluent.
- Confluent seems to be pursuing a fairly standard open source business model around Kafka.
- Confluent seems to be in the low to mid teens in paying customers.
- Confluent believes 1000s of Kafka clusters are in production.
- Confluent reports 40 employees and $31 million raised.
At its core Kafka is very simple:
- Kafka accepts streams of data in substantially any format, and then streams the data back out, potentially in a highly parallel way.
- Any producer or consumer of data can connect to Kafka, via what can reasonably be called a publish/subscribe model.
- Kafka handles various issues of scaling, load balancing, fault tolerance and so on.
So it seems fair to say:
- Kafka offers the benefits of hub vs. point-to-point connectivity.
- Kafka acts like a kind of switch, in the telecom sense. (However, this is probably not a very useful metaphor in practice.)
|Categories: Data integration and middleware, Humor, Kafka and Confluent, Market share and customer counts, Microsoft and SQL*Server, Open source, Specific users, Streaming and complex event processing (CEP)||10 Comments|
Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.
Making Cloudera run in the cloud has three major aspects:
- Cloudera’s usual software, ported to run on the cloud platform(s).
- Cloudera Director, which for example launches cloud instances.
- Points of integration, e.g. taking information about security-oriented roles from the platform and feeding then to the role-based security that is specific to Cloudera Enterprise.
Features new in this week’s release of Cloudera Director include:
- An API for job submission.
- Support for spot and preemptable instances.
- High availability.
- Some cluster repair.
- Some cluster cloning.
I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.
As for porting, let me start by noting: Read more
I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
|Categories: Business intelligence, Business Objects, Cognos, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Information Builders, MicroStrategy, Software as a Service (SaaS), Teradata||5 Comments|
When I find myself making the same observation fairly frequently, that’s a good impetus to write a post based on it. And so this post is based on the thought that there are many analogies between:
- Oracle and the Oracle DBMS.
- IBM and the IBM mainframe.
And when you look at things that way, Oracle seems to be swimming against the tide.
Drilling down, there are basically three things that can seriously threaten Oracle’s market position:
- Growth in apps of the sort for which Oracle’s RDBMS is not well-suited. Much of “Big Data” fits that description.
- Outright, widespread replacement of Oracle’s application suites. This is the least of Oracle’s concerns at the moment, but could of course be a disaster in the long term.
- Transition to “the cloud”. This trend amplifies the other two.
Oracle’s decline, if any, will be slow — but I think it has begun.
There’s a clear market lead in the core product category. IBM was dominant in mainframe computing. While not as dominant, Oracle is definitely a strong leader in high-end OTLP/mixed-use (OnLine Transaction Processing) RDBMS.
That market lead is even greater than it looks, because some of the strongest competitors deserve asterisks. Many of IBM’s mainframe competitors were “national champions” — Fujitsu and Hitachi in Japan, Bull in France and so on. Those were probably stronger competitors to IBM than the classic BUNCH companies (Burroughs, Univac, NCR, Control Data, Honeywell).
Similarly, Oracle’s strongest direct competitors are IBM DB2 and Microsoft SQL Server, each of which is sold primarily to customers loyal to the respective vendors’ full stacks. SAP is now trying to play a similar game.
The core product is stable, secure, richly featured, and generally very mature. Duh.
The core product is complicated to administer — which provides great job security for administrators. IBM had JCL (Job Control Language). Oracle has a whole lot of manual work overseeing indexes. In each case, there are many further examples of the point. Edit: A Twitter discussion suggests the specific issue with indexes has been long fixed.
Niche products can actually be more reliable than the big, super-complicated leader. Tandem Nonstop computers were super-reliable. Simple, “embeddable” RDBMS — e.g. Progress or SQL Anywhere — in many cases just work. Still, if you want one system to run most of your workload 24×7, it’s natural to choose the category leader. Read more
|Categories: Cloud computing, Database diversity, Exadata, IBM and DB2, Market share and customer counts, Microsoft and SQL*Server, NoSQL, Oracle, Software as a Service (SaaS)||27 Comments|